J.W. Mayo—Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado—USGS/SIR 2008–5086
FRONT COVER

Left

Aerial photograph of Chipeta Pines Subdivision showing four study sites: two 

residential lawns, a pond, and a commons park. Photograph courtesy of Mesa 

County, Colorado GIS Department, 2001.

Right, Top to Bottom

Grand Valley Irrigation Study site bluegrass lawn being irrigated. Photograph by 

John Mayo, October 11, 2006.

Orchard grass field at Colorado State University Orchard Mesa Experiment Station, 

Grand Junction, Colorado. Photograph by John Mayo, May 23, 2006.

Paradise Hills Subdivision pond. Photograph by John Mayo, November 22, 2005.

BACK COVER

Top to Bottom

Colorado State University Extension Service investigator collecting runoff data at study 

site. Photograph by John Mayo, September 29, 2005.

U.S. Geological Survey investigators installing a broad-crested flume at Colorado State 

University Orchard Mesa Experiment Station field to measure runoff. Photograph by 

John Mayo, April 26, 2006.

U.S. Geological Survey investigator collecting sprinkler system flow data at a study 

site. Photograph by John Mayo, October 11, 2005.

U.S. Geological Survey investigators performing acoustic depth profile of Paradise Hills 

Subdivision pond. Photograph by Craig Muelot, October 18, 2005.
Estimating the Effects of Conversion 

of Agricultural Land to Urban Land on 

Deep Percolation of Irrigation Water 

in the Grand Valley, Western Colorado

By John W. Mayo

Prepared in cooperation with the 

Colorado River Salinity Control Forum and the Mesa Conservation District

Scientific Investigation Report 2008–5086

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2008

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Mayo, J.W., 2008, Estimating the effects of conversion of agricultural land to urban land on deep percolation of irrigation 

water in the Grand Valley, western Colorado: U.S. Geological Survey Scientific Investigations Report 2008–5086, 58 p.
iii

Contents

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

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

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

Description of Study Area ...................................................................................................................2

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

Study Methods ...............................................................................................................................................4

Site Selection.........................................................................................................................................4

Residential Lots and Estates......................................................................................................5

Urban Park ....................................................................................................................................5

Gated-Pipe Irrigated Fields ........................................................................................................6

Irrigation-Water Holding Ponds ................................................................................................6

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

Residential Lots and Estates......................................................................................................6

Site Visits..............................................................................................................................6

Irrigation Event Logs ..........................................................................................................6

Water Pressure Logs..........................................................................................................7

Flow Rate Per Zone ............................................................................................................7

Precipitation ........................................................................................................................8

Soil-Moisture Core Samples.............................................................................................8

Urban Park ....................................................................................................................................8

Site Visits..............................................................................................................................8

Irrigation Event Logs ..........................................................................................................8

Water Pressure Logs..........................................................................................................8

Flow Rate Per Zone ............................................................................................................8

Precipitation ........................................................................................................................8

Soil-Moisture Core Samples.............................................................................................9

Urban Gated-Pipe Fields.............................................................................................................9

Site Visits..............................................................................................................................9

Irrigation Event Logs ..........................................................................................................9

Precipitation ........................................................................................................................9

Soil-Moisture Core Samples.............................................................................................9

Irrigation-Water Holding Ponds ..............................................................................................10

2005 Irrigation Field Season.....................................................................................................10

2006 Irrigation Field Season.....................................................................................................10

Deep Percolation Calculations.........................................................................................................11

Units of Measure........................................................................................................................11

Soil Moisture...............................................................................................................................11

Evapotranspiration ....................................................................................................................11

Urban Study Evapotranspiration Calculation...............................................................12

Natural Resources Conservation Service Monitoring and Evaluation 

Evapotranspiration Calculation.........................................................................12
iv

Daily Soil-Moisture Balance....................................................................................................13

Daily Soil-Moisture Balance Graph...............................................................................14

Physics of the Daily Soil-Moisture Balance Calculation Technique........................15

Soil Type ....................................................................................................................15

Field Capacity Corrections .....................................................................................16

Permanent Wilting Point Corrections...................................................................16

Irrigation Efficiency .................................................................................................16

Evapotranspiration Stress Correction Parameters............................................17

Whole Subdivision Irrigation-Water Application and Deep Percolation .........................17

Holding-Pond Water Balance..................................................................................................17

Topographic Surveys........................................................................................................17

Water Balance Calculation.............................................................................................17

Salt-Loading Calculation ..........................................................................................................18

Sources of Measurement Calculation Errors ................................................................................18

Instrumentation Measurement Errors....................................................................................18

Irrigation Application Efficiency Errors..................................................................................18

Handwritten Event Log Errors..................................................................................................18

Natural Resources Conservation Service Monitoring and Evaluation Data ............................18

Comparison between Natural Resources Conservation Service Monitoring 

and Evaluation Data and Urban Study Data .....................................................................20

Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation 

of Irrigation Water..........................................................................................................................20

Residential Lots, Estates, and Parks Results .................................................................................20

Example Soil-Moisture Balance Charts.................................................................................20

2005 Soil-Moisture Charts ...............................................................................................20

2006 Soil-Moisture Charts ...............................................................................................20

Urban Gated-Pipe Field Results .......................................................................................................24

Analysis of Residential Sites, Parks, and Gated-Pipe Fields.......................................................26

Bluegrass-Only Sites.................................................................................................................26

Statistical Tests for Bluegrass-Only Sites .............................................................................27

All-Vegetation Sites...................................................................................................................29

Statistical Tests for All-Vegetation Sites ...............................................................................29

Whole Subdivision Deep Percolation and Irrigation-Water Application ..................................29

Irrigation-Water Holding-Pond Seepage Results .........................................................................31

Natural Resources Conservation Service Monitoring and Evaluation Deep 

Percolation and Irrigation-Water Application..................................................................32

Comparison of Results Between this Study and the Natural Resources 

Conservation Service Monitoring and Evaluation ...........................................................36

Deep Percolation and Irrigation-Water Application Rates.................................................36

Salt Loading ................................................................................................................................38

Summary........................................................................................................................................................39

References Cited..........................................................................................................................................41

Appendix 1. Season Water Balance Charts for All Sites...................................................................44

Appendix 2. Link to Supplemental Data Tables
v

Figures

1. Map showing urban study site locations in the Grand Valley, western Colorado.............3

2–4. Photographs showing:

 2. Sprinkler-timer data logger attached to sprinkler timer .................................................7

 3. Irrigation system pressure logger in the field...................................................................8

 4. Broad-crested flume used to measure gated-pipe field tail-water runoff ..................9

5. Graph showing runoff linear regression equations for gated-pipe fields

(A) site 15, (B) site 16, and (C) sites 7W and 7E, Grand Valley, western Colorado...........10

6. Schematic diagram of soil-moisture characteristics ...........................................................11

7–17. Graphs showing:

 7. (A) Crop stress factor Ks for transpiration portion of composite crop 

coefficient, and (B) soil evaporation factor Kr for evaporation portion

of composite crop coefficient ...........................................................................................13

 8. Soil-moisture balance for bluegrass on 1/4-acre residential lot site 1, 

Grand Valley, western Colorado........................................................................................14

 9. Comparison of handwritten subdivision irrigation event logs to automated

logger data for eight sites, 2006 season ..........................................................................19

 10. Soil-moisture balance for bluegrass on 1/4-acre residential lot site 2,

Grand Valley, western Colorado........................................................................................22

 11. Soil-moisture balance for bluegrass on 5-acre estate site 9, Grand Valley,

western Colorado ................................................................................................................22

 12. Soil-moisture balance for bluegrass on subdivision commons park site 6,

Grand Valley, western Colorado........................................................................................23

 13. Soil-moisture balance for bluegrass on 1/4-acre residential site 8, Grand

Valley, western Colorado....................................................................................................23

 14. Soil-moisture balance for bluegrass on 5-acre estate site 18, Grand Valley,

western Colorado ................................................................................................................24

 15. Soil-moisture balance for native plants on 5-acre estate site 10, Grand

Valley, western Colorado....................................................................................................25

 16. Soil-moisture balance for orchard grass on 5-acre estate site 17, Grand

Valley, western Colorado....................................................................................................25

 17. Soil-moisture balance for orchard grass on gated-pipe field site 15, Grand

Valley, western Colorado....................................................................................................26

18. Boxplot showing deep percolation by site type for bluegrass-only vegetation

sites, Grand Valley, western Colorado ....................................................................................28

19. Chart showing irrigation-water application by site type for bluegrass-only

vegetation sites, Grand Valley, western Colorado ................................................................28

20. Graph showing deep percolation relative to effective precipitation for

bluegrass-only vegetation sites, Grand Valley, western Colorado ....................................29

21–30. Charts showing:

 21. Deep percolation by site type for all-vegetation type sites, Grand Valley,

western Colorado ................................................................................................................31

 22. Irrigation-water application by site type for all-vegetation type sites,

Grand Valley, western Colorado........................................................................................31

 23. Frequency distribution of field sizes included in Natural Resources

Conservation Service Monitoring and Evaluation (NRCS M&E) alfalfa-crop

sites (1985–2002)..................................................................................................................35
vi

21–30. Charts showing:—Continued

 25. Frequency distribution of irrigation-water application for Natural

Resources Conservation Service Monitoring and Evaluation

(NRCS M&E) alfalfa-crop sites (1985–2002)...................................................................35

 24. Frequency distribution of irrigation methods for Natural Resources

Conservation Service Monitoring and Evaluation (NRCS M&E) alfalfacrop sites (1985–2002) .......................................................................................................35

 26. Frequency distribution of deep percolation for Natural Resources

Conservation Service Monitoring and Evaluation (NRCS M&E) alfalfacrop sites (1985–2002) .......................................................................................................36

27–30. Boxplots showing:

 27. Season-total irrigation-water application by irrigation method for

Natural Resources Conservation Service Monitoring and Evaluation

(NRCS M&E) alfalfa sites (1985–2002) ............................................................................37

 28. Distribution of season-total deep percolation by irrigation method for

Natural Resources Conservation Service Monitoring and Evaluation

(NRCS M&E) alfalfa-crop sites (1985–2002)...................................................................37

 29. Season-total irrigation-water application by site acreage for

Natural Resources Conservation Service Monitoring and Evaluation

(NRCS M&E) alfalfa sites (1985–2002) ............................................................................38

 30. Distribution of season-total deep percolation by site acreage for

Natural Resources Conservation Service Monitoring and Evaluation

(NRCS M&E) alfalfa-crop sites (1985–2002)...................................................................38

Appendix Figures

A1–A27. Charts showing:

A1. Soil-moisture balance for 2005 season, site 1, Grand Valley,

western Colorado ............................................................................................................44

A2. Soil-moisture balance for 2005 season, site 2, Grand Valley,

western Colorado ............................................................................................................44

A3. Soil-moisture balance for 2005 season, site 6, Grand Valley,

western Colorado ............................................................................................................45

A4. Soil-moisture balance for 2005 season, site 8, Grand Valley,

western Colorado ............................................................................................................45

A5. Soil-moisture balance for 2005 season, site 9, Grand Valley, 

western Colorado ............................................................................................................46

A6. Soil-moisture balance for 2005 season, site 10, Grand Valley, 

western Colorado ............................................................................................................46

A7. Soil-moisture balance for 2005 season, site 11, Grand Valley, 

western Colorado ............................................................................................................47

A8. Soil-moisture balance for 2005 season, site 12, Grand Valley, 

western Colorado ............................................................................................................47

A9. Soil-moisture balance for 2005 season, site 13, Grand Valley,

western Colorado ............................................................................................................48

A10. Soil-moisture balance for 2005 season, site 14, Grand Valley, 

western Colorado ............................................................................................................48
vii

A1–A27. Charts showing:–Continued

A11. Soil-moisture balance for 2005 season, site 17, Grand Valley,

western Colorado ............................................................................................................49

A12. Soil-moisture balance for 2005 season, site 18, Grand Valley,

western Colorado ............................................................................................................49

A13. Soil-moisture balance for 2005 season, site 22, Grand Valley,

western Colorado ............................................................................................................50

A14. Soil-moisture balance for 2006 season, site 1, Grand Valley,

western Colorado ............................................................................................................50

A15. Soil-moisture balance for 2006 season, site 6, Grand Valley,

western Colorado ............................................................................................................51

A16. Soil-moisture balance for 2006 season, site 7 west, Grand Valley,

western Colorado ............................................................................................................51

A17. Soil-moisture balance for 2006 season, site 7 east, Grand Valley,

western Colorado ............................................................................................................52

A18. Soil-moisture balance for 2006 season, site 8, Grand Valley, 

western Colorado ............................................................................................................52

A19. Soil-moisture balance for 2006 season, site 9, Grand Valley,

western Colorado ............................................................................................................53

A20. Soil-moisture balance for 2006 season, site 10, Grand Valley,

western Colorado ............................................................................................................53

A21. Soil-moisture balance for 2006 season, site 11, Grand Valley,

western Colorado ............................................................................................................54

A22. Soil-moisture balance for 2006 season, site 12, Grand Valley,

western Colorado ............................................................................................................54

A23. Soil-moisture balance for 2006 season, site 15, Grand Valley,

western Colorado ............................................................................................................55

A24. Soil-moisture balance for 2006 season, site 16, Grand Valley,

western Colorado ............................................................................................................55

A25. Soil-moisture balance for 2006 season, site 17, Grand Valley,

western Colorado ............................................................................................................56

A26. Soil-moisture balance for 2006 season, site 18, Grand Valley,

western Colorado ............................................................................................................56

A27. Soil-moisture balance for 2006 season, site 21, Grand Valley,

western Colorado ............................................................................................................57

Tables

1. Summary of urban study sites for 2005 and 2006 irrigation seasons, Grand

Valley, western Colorado.............................................................................................................4

2. Characteristics of urban study residential 1/4-acre lots and 5-acre estates,

Grand Valley, western Colorado.................................................................................................5

3. Characteristics of urban study irrigation-water holding ponds ...........................................6

4. Data-collection methods at urban study 1/4-acre residential lots and 5-acre

estates ............................................................................................................................................7

5. Soil-moisture balance model parameters for sites that use daily calculations ..............15

6. Principal sources of error in estimates of factors used to calculate deep

percolation in urban study data ...............................................................................................19
viii

7. Summary of Natural Resources Conservation Service Monitoring and

Evaluation (NRCS M&E) sites and seasons for all crop types and for

alfalfa crops, 1985–2002.............................................................................................................20

8. Summary of data and results of deep percolation calculations for 2005

irrigation season for residential lots, estates, and urban parks .........................................21

9. Summary of data and results of deep percolation calculations for 2006 irrigation

season for residential lots, estates, and urban parks...............................................................21

10. Summary of data and results of deep percolation calculations for 2006

irrigation season for gated-pipe fields....................................................................................26

11. Summary statistics for bluegrass-only vegetation on residential lots, estates,

and urban parks ..........................................................................................................................27

12. Summary statistics by site type for bluegrass-only vegetation on residential

lots, estates, and urban parks ..................................................................................................27

13. Kruskal-Wallis correlation test results for bluegrass-only vegetation on

residential lots, estates, and urban parks ..............................................................................29

14. Kendall’s Tau correlation test results for bluegrass-only vegetation on

residential lots, estates, and urban parks ..............................................................................29

16. Summary statistics by site type for all-vegetation types on residential lots,

estates, urban parks, and gated-pipe fields...........................................................................30

15. Summary statistics for all-vegetation types on residential lots, estates, urban

parks, and gated-pipe sites.......................................................................................................30

17. Kruskal-Wallis test results for all-vegetation types on residential lots, estates,

urban parks, and gated-pipe fields..........................................................................................32

18. Kendall’s Tau correlation test results for all-vegetation types on residential

lots, estates, urban parks, and gated-pipe fields ..................................................................32

19. Whole subdivision estimates of mean deep percolation for bluegrass-only

sites and all-vegetation type sites ...........................................................................................33

20. Whole subdivision estimates of mean irrigation water applied for bluegrassonly sites and all-vegetation type sites...................................................................................34

21. Seepage results for irrigation-water holding ponds, 2005–2006 ........................................34

22. Summary statistics for Natural Resources Conservation Service Monitoring

and Evaluation (NRCS M&E) alfalfa-crop sites (1985–2002)................................................36

23. Deep percolation comparison statistics for urban study whole subdivision

bluegrass-only sites, all-vegetation type sites, and Natural Resources Conservation

Service Monitoring and Evaluation (NRCS M&E) alfalfa-crop sites..................................39

24. Irrigation-water application comparison statistics for urban study whole

subdivision bluegrass-only sites, all-vegetation type sites, and Natural Resources

Conservation Service Monitoring and Evaluation (NRCS M&E) alfalfa-crop sites................39

25. Salt loading comparisons for urban study whole subdivision sites and Natural

Resources Conservation Service Monitoring and Evaluation (NRCS M&E) sites...........39
ix

Conversion Factors, Datums, and Abbreviations

Multiply By To obtain

Length

inch 2.54 centimeter (cm)

inch 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2

)

acre 0.004047 square kilometer (km2

)

Volume

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

)

Flow rate

acre-foot per year (acre-ft/yr) 1,233 cubic meter per year (m3

/yr)

cubic foot per second (ft3

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

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

Vertical coordinate information is referenced to the National Geodetic Vertical Datum of 1929 

(NGVD 29).

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

Additional Abbreviations Used in This Report

CSU Colorado State University

FC Field Capacity

NRCS National Resources Conservation Service

PWP Permanent Wilting Point

RAW Readily Available Water

REW Readily Evaporable Water

TAW Total Available Water

USDA U.S. Department of Agriculture

USGS U.S. Geological Survey

Abstract

 The conversion of agricultural land to urban residential 

land is associated with rapid population growth in the Grand 

Valley of western Colorado. Information regarding the effects 

of this land-use conversion on deep percolation, irrigationwater application, and associated salt loading to the Colorado 

River is needed to support water-resource planning and conservation efforts. The Natural Resources Conservation Service 

(NRCS) assessed deep percolation and estimated salt loading 

derived from irrigated agricultural lands in the Grand Valley 

in a 1985 to 2002 monitoring and evaluation study (NRCS 

M&E). The U.S. Geological Survey (USGS), in cooperation 

with the Colorado River Salinity Control Forum and the Mesa 

Conservation District, quantified the current (2005–2006) deep 

percolation and irrigation-water application characteristics of 

1/4-acre residential lots and 5-acre estates, urban parks, and 

urban orchard grass fields in the Grand Valley, and compared 

the results to NRCS M&E results from alfalfa-crop sites. In 

addition, pond seepage from three irrigation-water holding 

ponds was estimated. Salt loading was estimated for the urban 

study results and the NRCS M&E results by using standard 

salt-loading factors.

A daily soil-moisture balance calculation technique was 

used at all urban study irrigated sites. Deep percolation was 

defined as any water infiltrating below the top 12 inches of 

soil. Deep percolation occurred when the soil-moisture balance in the first 12 inches of soil exceeded the field capacity 

for the soil type at each site. Results were reported separately 

for urban study bluegrass-only sites and for all-vegetation type 

(bluegrass, native plants, and orchard grass) sites. Deep percolation and irrigation-water application also were estimated 

for a complete irrigation season at three subdivisions by using 

mean site data from each subdivision. It was estimated that 

for the three subdivisions, 37 percent of the developed acreage 

was irrigated (the balance being impermeable surfaces).

The mean season-total deep percolation for bluegrassonly sites at three residential subdivisions was 0.30 acre-foot 

per irrigated acre and 0.12 acre-foot per developed acre. The 

season-total deep percolation for all-vegetation type sites at 

the three subdivisions was 0.27 acre-foot per irrigated acre 

and 0.11 acre-foot per developed acre. Mean season-total 

deep percolation for alfalfa-crop sites in the NRCS M&E was 

1.27 acre-feet per acre. For equivalent land areas, the conversion of land from agricultural to urban residential uses results 

in a potential deep percolation reduction of 91 percent for 

bluegrass-only sites and a potential reduction of 91 percent 

for all-vegetation type sites.

The mean season-total irrigation-water application for 

bluegrass-only sites in three residential subdivisions was 

2.80 acre-feet per irrigated acre and 1.05 acre-feet per developed acre. The mean season-total irrigation-water application for all-vegetation type sites at the three subdivisions 

was 2.56 acre-feet per irrigated acre and 0.98 acre-foot per 

developed acre. Mean season-total irrigation-water application 

for alfalfa-crop sites in the NRCS M&E was 3.79 acre-feet 

per developed acre. For equivalent land areas, this represents 

a reduction of 72 percent in irrigation-water application for 

bluegrass-only sites compared with agricultural sites, and 

a reduction of 74 percent for all-vegetation type sites in 

subdivisions. On the basis of a limited data set, mean seasontotal irrigation-water holding pond seepage was estimated to 

be 11.94 acre-feet per surface acre.

Salt loading for urban bluegrass-only sites was estimated 

to be 0.25 ton per developed acre and for all-vegetation type 

sites was estimated to be 0.22 ton per developed acre. Mean 

salt loading for NRCS M&E alfalfa-crop sites was 2.60 tons 

per acre. As a ratio, compared with alfalfa-crop sites, this 

represents a salt-loading reduction factor of 10.4 for bluegrassonly sites and a reduction factor of 11.8 for all-vegetation 

type sites. Salt loading from ponds was estimated to be 

24.48 tons per surface acre, a ratio increase over agricultural 

alfalfa-crop sites of 9.4.

Thus, the conversion of land from agricultural to residential urban subdivisions results in substantially lower irrigationwater application, lower deep percolation, and less salt loading 

per developed acre, with the exception of urban unlined ponds. 

Control of deep percolation from unlined ponds that are 

created to support residential irrigation could be an increasingly important factor to consider for minimizing irrigationinduced salt loading to the Colorado River.

Estimating the Effects of Conversion of Agricultural Land 

to Urban Land on Deep Percolation of Irrigation Water in 

the Grand Valley, Western Colorado

By John W. Mayo
2 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Introduction

The Grand Valley, in Mesa County in western Colorado, 

contributes an average of 500,000 tons of salt annually to 

the Colorado River because of salt pickup from irrigation 

(Hedlund, 1994). Rapid population growth in the Grand Valley 

has led to the conversion of agricultural land to urban residential land. The Grand Valley is underlain primarily by the 

Mancos Shale of Cretaceous age (Butler and others, 1996). 

Deep percolation of irrigation water through soils derived from 

the Mancos Shale results in leaching of salt, which reaches the 

Colorado River. Some salt load is delivered to irrigated lands 

with the Colorado River-supplied irrigation water, but this 

initial salt load is accounted for in the salt-loading factor used 

in this study.

Changes in deep percolation characteristics have not previously been quantified for lands converted from agricultural 

to residential use. The Grand Valley and other salinity control 

units are experiencing substantial land-use changes due to the 

conversion of large agricultural areas to smaller land units. 

These smaller units represent both urban agriculture and residential landscaping. Salinity control programs need information about the long-term effects of previous irrigation system 

improvements on deep percolation characteristics and reduction of salt loading. Salinity control program managers need 

to understand how changing land use may affect expected 

salinity-reduction benefits that were estimated for changes in 

agricultural irrigation practices, but without consideration of 

subsequent conversion to urban land use.

The Natural Resources Conservation Service (NRCS) 

assessed deep percolation and estimated salt loading derived 

from irrigated agricultural lands in the Grand Valley in a 

1985 to 2002 monitoring and evaluation study, hereinafter 

referred to as “NRCS M&E” (U.S. Department of Agriculture, 

1986–2003). That assessment provided a baseline of deep percolation characteristics on agricultural land and has been used 

by NRCS to make management decisions related to salinity 

reduction projects.

The U.S. Geological Survey (USGS), in cooperation 

with the Colorado River Salinity Control Forum and the 

Mesa Conservation District, quantified the current (2005–2006) 

deep percolation and irrigation-water application characteristics of residential lots and estates, urban parks, and orchard 

grass fields in the Grand Valley, and compared the results to 

1985–2002 NRCS M&E results from alfalfa-crop sites. Salt 

loading was estimated for the urban study results and the NRCS 

M&E results using standard salt-loading factors.

Purpose and Scope

This report presents estimates of the effects of conversion 

of agricultural land to urban land on deep percolation of irrigation water in the Grand Valley. Effects on the amounts of applied 

irrigation water and salt loading in the Colorado River are also 

discussed. The report details the methods used to collect data 

and the calculations used to estimate the effects of the land-use 

change. The urban study results are compared to the results of 

the 1985–2002 NRCS M&E. The study was divided into three 

parts: (1) calculation of irrigation-water application and deep 

percolation from turf grass, orchard grass, and native plants used 

in lawns and small pastures; (2) calculation of seepage from 

selected unlined irrigation holding ponds; and (3) comparison of 

irrigation-water application, deep percolation characteristics, and 

resulting salt loading in the Colorado River from the sites in this 

study to the alfalfa-crop sites in the NRCS M&E.

A daily soil-moisture balance calculation technique was 

used to calculate deep percolation at irrigated sites from the 

difference between inflow of irrigation water and effective 

precipitation and outflow of runoff and evapotranspiration. For 

this study, deep percolation for lawns and small pastures was 

defined as any water infiltrating below the top 12 inches of 

soil, which was assumed to be deeper than the turf-grass root 

zone. Daily deep percolation was calculated to have occurred 

when the daily soil-moisture balance exceeded the field capacity for the soil type at each site.

Additionally, pond seepage from three irrigation-water 

holding ponds was estimated. Holding ponds temporarily 

store water drawn from irrigation canals for whole subdivision 

irrigation systems and parks and are being installed throughout 

the Grand Valley as new subdivisions are developed. Deep 

percolation is normally considered to occur below the root 

zone of plants, while seepage is defined as loss from a body 

of water. For the purposes of salt-loading calculations, pond 

seepage in this report is treated as being functionally equivalent to deep percolation.

Finally, and most important, the irrigation-water application and deep percolation characteristics derived for the urban 

sites were compared to the results of the NRCS M&E. This 

comparison illustrates the effect of conversion from agricultural fields to urban lawns and pastures and provides for an 

estimate of changes in salt loading in the Colorado River from 

deep percolation of irrigation water.

Description of Study Area

The Grand Valley is in Mesa County in western Colorado, 

at the confluence of the Gunnison and Colorado Rivers (fig. 1). 

The valley is approximately 30 miles long and 5 miles wide. 

The principal city is Grand Junction, with a population of 

45,000 in 2005, while the entire metropolitan area population in the Grand Valley was approximately 134,000 in 2006 

(U.S. Census Bureau, 2007).

Historically, the Grand Valley has been a major fruitgrowing region with a large number of orchards and small 

farms. Irrigation canals have provided ample and inexpensive 

water from the Colorado and Gunnison Rivers for Grand 

Valley agriculture since about 1900. The irrigation distribution 

infrastructure has been retained to provide irrigation water to 

urban developments as land-use conversion from agriculture 

to urban occurs. Irrigation water is typically available in the 

valley from mid-April through October each year.
Introduction 3

Geologically, the Grand Valley is underlain by the 

Mancos Shale, which is a nonpoint source for salt and trace 

elements such as selenium (Butler and others, 1996). Deep 

percolation of irrigation water in the Grand Valley leaches 

considerable salt and selenium from Mancos Shale-derived 

soils and Mancos Shale bedrock. Upstream from the irrigated 

area of the Grand Valley, selenium concentrations are generally less than 1 part per billion (ppb), while downstream from 

irrigated areas, selenium concentrations commonly exceed 

4.6 ppb, the State of Colorado chronic standard for aquatic life 

(Butler and others, 1996).

To estimate the effect of land-use conversion on deep percolation of water in this report, 21 urban sites in five areas of the 

Grand Valley were selected for study—three areas on the north 

side of the Colorado River and two areas on the south side. The 

21 urban sites were studied for two irrigation seasons, in 2005 

and 2006, resulting in a total of 30 irrigation seasons of data. 

All of the urban sites selected had been previously irrigated for 

agriculture (Dan Champion, Colorado State University, oral 

commun., 2005). An effort was made to associate these urban 

study sites spatially with those of the NRCS M&E, but because 

the ownership of the NRCS M&E sites was not recorded for 

Quail Run 

Subdivision

Paradise Hills 

Subdivision

Grandview 

Subdivision

CSU Orchard Mesa 

Chipeta Pines Experiment Station

Subdivision

Colorado

Gunnison River

River

8 28

6 1

OM 7W 7E

22

12

26 13

21 2

15 16

1714

18 9

11

27

10

GJ

Grand Junction

1/4-acre residential bluegrass

5-acre estate bluegrass

5-acre estate orchard grass

5-acre estate native plants

Subdivision commons park

 bluegrass

Gated-pipe field orchard grass

Urban sites, by type and vegetation, 

 and number

EXPLANATION

7W

17

11

27

10

6

OM

22

Irrigation holding pond

COAGMET weather station

Other sites and number

0

0

1

1 2 KILOMETERS

N

2 MILES

COLORADO

Study area

108°37'30"

39°07'30"

108°30'

Base from Mesa County Government GIS Department, 2005.

Figure 1. Urban study site locations in the Grand Valley, western Colorado.
4 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

privacy reasons, only generalized spatial correlations were 

possible. The NRCS M&E sites were within the same general 

areas of the Grand Valley that were selected for the urban study 

and had similar soil types and geology. The 1985–2002 NRCS 

M&E in the Grand Valley included 20 alfalfa-crop sites, resulting in a total of 67 irrigation seasons of data.

The urban site types included: (1) sprinkler-irrigated 

residential lawns planted with bluegrass on 1/4-acre residential 

lots (n = 7 sites); (2) sprinkler-irrigated 5-acre estates planted 

with bluegrass, orchard grass (a type of pasture grass), and 

native plant landscaping (n = 6 sites); (3) sprinkler-irrigated 

urban parks planted with bluegrass (n = 1 site); (4) small (1 

to 2.5 acres) gated-pipe flood-irrigated urban pastures planted 

with orchard grass (n = 4 sites); and (5) small (0.2 to 1.1 acre) 

unlined perched irrigation-water holding ponds (n = 3 sites). 

Twelve of the 18 residential study sites had bluegrass, 5 had 

orchard grass, and 1 had native western Colorado xeriscape 

plants. Results were reported separately for the bluegrass-only 

sites and for all-vegetation type urban study sites.

Acknowledgments

Thanks are extended to the Board of Directors of the 

Mesa Conservation District for their support and oversight 

of the project.

Special thanks are due to all of the homeowners and irrigation managers who generously allowed us to monitor their 

irrigation practices and who kept handwritten logs for this 

study. Without their patience and enthusiasm, the study would 

not have been possible.

Thanks are also extended to the following individuals: 

Carl Zimmerman (Grand Junction Conservation Specialist for 

the Colorado State Conservation Board) for administration 

coordination with the Mesa Conservation District; Dawna Salazar 

(Mesa Conservation District Office Manager) who purchased 

equipment for the project; Jim Currier (NRCS Grand Junction 

District Conservationist) for mentoring, technical consultation, 

and liaison with the NRCS staff; Frank Riggle (NRCS Colorado 

Assistant State Conservationist for Water Resources) for consultation on evapotranspiration calculation methods and NRCS 

M&E data analysis; Dr. Curtis Swift (Colorado State University 

(CSU) Mesa County Extension Horticulturist) for technical 

consultations and the loan of weather stations; Ardith Blessinger 

(CSU County Extension) for performing the irrigation audits; 

Dr. Harold Larsen (Fruit Pathology Specialist at CSU Orchard 

Mesa Research Center) for evapotranspiration consultation, for 

providing pan evaporation data, and for finding archived NRCS 

M&E evapotranspiration (ET) data; Dan Champion (NRCS and 

CSU Extension, retired) for soil theory mentoring, for performing 

soil-moisture surveys, and for consultations on NRCS M&E data; 

Ryan Altenberg (NRCS Agricultural Engineer), who was very 

generous in loaning the GE Panametrics flow meter and consulting on irrigation practices; Terry Franklin (City of Grand Junction 

Water Services Superintendent) for aerial-photograph analysis of 

irrigated areas in two subdivisions; Dr. Michael Dukes (University 

of Florida, Gainesville) for consultations on residential irrigation 

research techniques; Dr. Frank Vignola (University of Oregon, 

Eugene) for help with conversion of historical meteorology data; 

Dr. Harold Duke (U.S. Department of Agriculture–Agricultural 

Research Service (USDA–ARS), retired) for help with the CSU 

Colorado Agricultural Meteorological Network (COAGMET) 

Web site and for evapotranspiration support; Robert Wilson 

(USGS Hydrologist), for assistance with field construction; 

Paul Diaz (USGS Hydrologic Technician) for the pond acoustic 

depth survey; and Craig Muelot (USGS Hydrologic Technician) 

who was invaluable in accurately collecting and processing all of 

the field data for two seasons. Without Craig’s attention to many 

details, the project could not have been completed.

Study Methods

Site Selection

The urban study included 21 sites, consisting of 1/4-acre 

residential lots (n = 7 sites), 5-acre estates (n = 6 sites), urban 

parks (n = 1 site), orchard (pasture) grass fields (n = 4 sites), 

and irrigation-water holding ponds (n = 3 sites) in the Grand 

Valley. A summary of site types for each irrigation season 

of the study is listed in table 1; nine sites were sampled in 

both 2005 and 2006. The two seasons of study resulted in 

30 irrigation seasons of data. In addition, deep percolation 

and irrigation-water application for three whole subdivisions 

was estimated using mean site data from those subdivisions.

Table 1. Summary of urban study sites for 2005 and 2006 irrigation seasons, Grand Valley, western Colorado.

[n/a, not applicable]

Type of site Vegetation Total number

of sites

Number of

2005 sites

Number of

2006 sites

Count of sites

used for both seasons

1⁄4-acre residential lot Bluegrass 7 6 4 3

5-acre estate Bluegrass 4 4 3 3

5-acre estate Native plants 1 1 1 1

5-acre estate Orchard grass 1 0 1 0

Commons park Bluegrass 1 1 1 1

Gated-pipe field Orchard grass 4 0 4 0

Irrigation holding pond n/a 3 1 3 1

Totals 21 13 17 9
Study Methods 5

Residential Lots and Estates

Two categories of residential sites were selected for the 

study—residential lots of approximately 1/4 acre each and 

estates of approximately 5 acres each. Site characteristics for 

the residential lots and estates are summarized in table 2.

The residential lots were located in three subdivisions 

(Chipeta Pines, Paradise Hills, and Grandview), two on the 

north side of the Colorado River and one on the south side. 

The estates were all in one subdivision (Quail Run) on the 

north side of the river.

The residential lots and estates had only Kentucky bluegrass vegetation, with two exceptions. The first exception was 

one of the 5-acre estates, which had both a small bluegrass lawn 

and a large, irrigated, native plant landscaped area (rabbit brush, 

four-wing saltbush, sage, and various bunch grasses). Because 

the native-plant landscaping was sprinkler irrigated on a regular 

schedule, covered a major part of the site (0.56 acre), and 

because there is interest in the use of native plantings (xeriscaping) to conserve water, it was desirable to collect data on 

the native landscaped portion of the site. This estate was therefore divided into two separate study sites of bluegrass and native 

plantings. The second exception to bluegrass was at one of the 

5-acre estates, which used orchard grass for the entire lawn area.

The area (in square feet) of all lawns and native-plant 

landscaped areas was determined using geographic information system (GIS) analysis of 2001–2005 aerial photographs 

provided by Mesa County. Irrigated areas containing shrubs, 

gardens, and trees were excluded, in addition to all nonirrigated vegetation at a site. Because of the variety of vegetation at the various residential sites, and because bluegrass is 

the predominant turf grass in the Grand Valley, two sets of 

water application and deep percolation results are reported: 

(1) bluegrass-only sites and (2) all-vegetation type sites. 

All residential sites used commonly available underground pop-up sprinkler systems. Sprinkler heads include 

both impulse and spray types. All sites had automatic 

sprinkler-timers and used irrigation water rather than treated 

water. None of the residential sprinkler-timers included a rain 

sensor to turn off watering during rain events. Because the cost 

of irrigation water is low in the Grand Valley, cost was not 

considered a limiting factor for water application. For example, the irrigation water bill for one 5-acre estate was reported 

by the homeowner to be $170 for the 2005 season. The soil 

type for each site was determined from the NRCS Web Soil 

Survey Web site and was predominantly loam and clay loam 

(U.S. Department of Agriculture, 2007).

Urban Park

A 0.45-acre commons park at one subdivision (Chipeta 

Pines) was included in the study for two seasons (table 1). The 

commons park is on the south side of the Colorado River and 

had Kentucky bluegrass vegetation. The site used an underground sprinkler system controlled by an automatic irrigation 

timer without a rain sensor.

Table 2. Characteristics of urban study residential 1/4-acre lots and 5-acre estates, Grand Valley, western Colorado.

[CSU, Colorado State University; n/a, not applicable] 

Site

number 

Year

studied Location Sitetype

Irrigated

acreage Vegetation

Number

of irrigation

zones

Average flow

for all

sprinkler zones

(gallons per minute)

Soil

type

1 2005, 2006 Chipeta Pines 1/4-acre residential lot 0.12 Bluegrass 10 12 Loam

2 2005 Paradise Hills 1/4-acre residential lot 0.12 Bluegrass 7 22 Clay loam

6 2005, 2006 Chipeta Pines Commons park 0.45 Bluegrass 3 74 Loam

7W 2006 CSU Orchard Mesa Gated-pipe field 0.99 Orchard grass n/a n/a Clay loam

7E 2006 CSU Orchard Mesa Gated-pipe field 0.99 Orchard grass n/a n/a Clay loam

8 2005, 2006 Chipeta Pines 1/4-acre residential lot 0.06 Bluegrass 1 30 Loam

9 2005, 2006 Quail Run 5-acre estate 1.12 Bluegrass 12 30 Clay loam

10 2005, 2006 Quail Run 5-acre estate 0.56 Native 2 30 Clay loam

11 2005, 2006 Quail Run 5-acre estate 0.14 Bluegrass 3 27 Clay loam

12 2005, 2006 Grandview 1/4-acre residential lot 0.05 Bluegrass 2 16 Loam

13 2005 Paradise Hills 1/4-acre residential lot 0.14 Bluegrass 5 14 Clay loam

14 2005 Quail Run 5-acre estate 1.00 Bluegrass 13 52 Clay loam

15 2006 Near Quail Run Gated-pipe field 0.95 Orchard grass n/a n/a Loam

16 2006 Near Quail Run Gated-pipe field 2.13 Orchard grass n/a n/a Clay loam

17 2006 Quail Run 5-acre estate 1.70 Orchard grass 16 43 Clay loam

18 2005, 2006 Quail Run 5-acre estate 0.82 Bluegrass 7 44 Clay loam

21 2006 Paradise Hills 1/4-acre residential lot 0.10 Bluegrass 6 15 Clay loam

22 2005 Grandview 1/4-acre residential lot 0.05 Bluegrass 2 23 Loam

23 2005, 2006 Chipeta Pines Whole subdivision 6.03 Bluegrass n/a n/a Loam

24 2005, 2006 Paradise Hills Whole subdivision 34.9 Bluegrass n/a n/a Clay loam

25 2005, 2006 Quail Run Whole subdivision 41.6 Bluegrass/native n/a n/a Clay loam
6 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Gated-Pipe Irrigated Fields

One of the purposes of the urban study was to measure 

deep percolation from small, urban fields that grow orchard 

grass and use furrow irrigation supplied by a gated-pipe 

system. The gated-pipe irrigation infrastructure is usually a 

holdover from agricultural use of the fields. These pastures 

are typically adjacent to homes and are commonly 1 to 2 acres 

in size. No suitable sites were found for the 2005 season, but 

for 2006, four sites (sites 7W, 7E, 15, and 16 in table 2) were 

located. Sites 15 and 16 are a pair of privately owned fields 

(0.95 and 2.13 acres) used to grow orchard grass for horses. 

Site 7 is a test field (1.98 acres) growing orchard grass at the 

Colorado State University (CSU) Western Colorado Research 

Center in Orchard Mesa. Because site 7 was always watered 

in halves, it was treated as two distinct sites, 7 West (7W) and 

7 East (7E). All four fields were planted in orchard grass, a 

bunch grass widely used for forage in the Grand Valley. The 

privately owned fields were grazed by horses periodically during the season, while the CSU field was used to produce hay.

Irrigation-Water Holding Ponds

Another purpose of this study was to calculate seepage 

from selected unlined irrigation holding ponds. These ponds 

are used by subdivisions to store irrigation water for onsite 

sprinkler irrigation pumping systems. Three subdivision ponds 

(Paradise Hills, Quail Run, and Chipeta Pines) were observed 

(table 3). The ponds receive gravity-fed water by underground 

pipe from the irrigation canals. The ponds contain water during the irrigation season and are allowed to empty naturally 

when the irrigation season ends about November 1. One pond 

was observed for the 2005 season, and three were observed for 

the 2006 season.

It was assumed that these holding ponds were perched 

above the water table; thus, ground-water inflow could be 

ignored. The observation that all three ponds are virtually 

dry during the winter supports this assumption, but this may 

not hold true during the irrigation season. One of the ponds 

(Paradise Hills) is approximately 150 yards from the irrigation 

canal and is lower than the canal. Anecdotal information from 

the Paradise Hills irrigation manager suggests that properties adjacent to and lower than the canal receive seepage, 

which affects their lawns during the summer (Tom Mahoney, 

homeowner, oral commun., 2005). It thus cannot be ruled out 

that seepage from the canal raises the level of ground water 

surrounding the pond. This could have the effect of reducing 

the predicted pond seepage during the irrigation season. The 

ponds at Quail Run and Chipeta Pines are farther from their 

respective supply canals (1 mile and 0.7 mile, respectively) 

and are probably not affected by any canal seepage. Research 

conducted by the Bureau of Reclamation on canal seepage 

in the Grand Valley indicated that canal seepage rates varied 

greatly, depending on testing procedures and location (Bureau 

of Reclamation, 1986).

Data Collection

Residential Lots and Estates

Data collection at the home sites included handwritten 

irrigation logs and digital data loggers that recorded sprinklertimer irrigation events and system water pressure. Table 4 

summarizes the data-collection methods used for residential 

lots and estates. Homeowners were encouraged to maintain 

normal irrigation practices during the study. In all cases, the 

homeowners were cooperative and friendly during the visits.

Site Visits

Each site was visited at least once a month from April 

through November. Data loggers were checked and downloaded, homeowner’s handwritten logs were digitally scanned, 

photographs of the lawn were taken, and visual observations of 

the lawn condition were recorded. Soil-moisture core samples 

were collected for gravimetric analysis, and a subjective visual 

and hand-feel estimate of soil moisture was made using traditional agricultural field techniques (Leopold, 2003).

Irrigation Event Logs

For the 2005 season, the primary record of irrigation 

events was a handwritten log kept by the homeowner. This 

irrigation event log recorded the sprinkler-timer irrigation 

schedules, including dates, time, and duration of irrigation 

events by zone. The homeowners mostly were cooperative 

and timely in maintaining these logs. Field personnel independently observed and wrote down the sprinkler-timer programs 

at each monthly visit when the timer was accessible.

For the 2006 season, 22-channel digital data loggers 

were designed and built to record directly the actual irrigation 

events for each sprinkler zone (fig. 2). Each 24-volt alternating 

Table 3. Characteristics of urban study irrigation-water holding ponds.

[Surface area, surface area of water when pond is at capacity]

Subdivision/

site number

(fig. 1)

Surface area

(acres)

Capacity

(acre-feet)

Maximum

depth

(feet)

Years

studied

Pond lining

material Soil type

Paradise Hills/26 1.09 2.90 4.49 2005, 2006 None Clay loam

Quail Run/27 0.34 1.37 6.76 2006 None Clay loam

Chipeta Pines/28 0.17 0.55 5.25 2006 None Loam
Study Methods 7

current (AC) signal from the timer that activates a zone valve 

was connected to an input channel. Every 60 seconds the data 

logger scanned all the channels but recorded only to the digital 

log when a valve changed state. This resulted in a greatly 

simplified and compressed digital log. In addition, the homeowners were asked to continue to maintain a handwritten 

log of their irrigation events as a backup in case of data 

logger failure.

Water Pressure Logs

Water pressure was recorded every 60 seconds using 

a solar-powered digital data logger at the irrigation watersupply pipe to each irrigation system (fig. 3). This provided different information, depending on the type of site. 

Because the 1/4-acre residential sites received irrigation 

water from a centrally pressurized system, water pressure 

at those sites provided an indication of whether the central 

supply was available and served as a cross-check against the 

sprinkler-timer events to ensure that the logged event actually resulted in water application to the lawn. For example, 

if the sprinkler-timer triggered an irrigation event but water 

pressure was zero, no water was applied. All of the 5-acre 

estate sites received gravity-fed irrigation water by underground pipe, which was then pressurized by a local water 

pump at each home. A master valve at the system input was 

opened by the timer during the irrigation event. For the 5-acre 

sites, water pressure was an indication that the system was 

delivering water and again served as a cross-check of the 

sprinkler-timer events.

Flow Rate Per Zone

By knowing the average water-flow rate per zone, a 

calculation of the total quantity of water delivered during an 

irrigation event can be made by multiplying the zone flow rate 

by the zone event duration. To determine average flow rate per 

zone in cubic feet per second (ft3

/s), a GE Panametrics PT878 

noninvasive acoustic flow meter was attached to the main irrigation water input pipe for a system. A 5-minute cycle was run 

for each zone, with the flow meter logging the flow rate every 

2 seconds. In 2005, water inflow rates for each zone at every site 

were measured in April and again in September. No significant 

differences in average flow rates were found in the second set 

of measurements, indicating flow rates may have been consistent through the irrigation season. In 2006, sites were measured 

in April.

Water pressure was simultaneously recorded during the flow 

test by using water-pressure data loggers to determine the variability in supply pressure and to determine an average pressure. 

Subsequent comparison of these average system water pressures 

with recorded daily pressures during the season indicated no 

substantial variations in average pressure at the sites. For this 

reason, it was not necessary to correlate water pressure with the 

quantity of water applied during individual irrigation events.

Figure 2. Sprinkler-timer data logger attached to sprinkler timer.

Table 4. Data-collection methods at urban study 1/4-acre residential lots and 5-acre estates.

[CSU, Colorado State University; USGS, U.S. Geological Survey]

Data collection method Collection frequency Data source

Sprinkler-timer log (2006 only) Every minute Data logger on sprinkler-timer

Handwritten log With changes in irrigation schedule Homeowner

Water pressure log Every 2 minutes Data logger on irrigation system input pipe

Flow rate per zone Start and end of season Field measurement by USGS

Measured precipitation Every 60 minutes at 6 sites 4 local weather stations and 2 CSU weather stations

Evapotranspiration Daily calculation from climate data 2 CSU weather stations

Irrigation audit Once per site at beginning of season CSU County Extension Agent

Soil moisture Start and end of season (2005), monthly (2006) USGS staff sample collection

Visual observation of lawn Monthly USGS staff

Sprinkler

timer 

Data

logger
8 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Precipitation

In each of the four study subdivisions, precipitation was 

measured hourly with a Spectrum Technologies weather station that incorporated a digital data logger. The subdivision 

precipitation gages were checked for calibration at the beginning of the 2005 season. In addition, two Internet-accessible 

CSU COAGMET Campbell Scientific weather stations 

(Grand Junction and Orchard Mesa; fig. 1) provided published 

hourly precipitation.

To determine the portion of precipitation available for 

infiltration into the ground, the measured precipitation data 

were corrected for runoff to determine “effective” precipitation 

using the U.S. Army Corps of Engineers Hydraulic Engineering 

Center-Hydrologic Modeling System (HEC-HMS) (U.S. Army 

Corps of Engineers, 2006). The HEC-HMS model accounts 

for rainfall intensity over time to estimate the runoff. In general, precipitation events exceeding 0.10 inch caused runoff, 

with intense events of short duration (less than 2 hours) causing the most runoff. Unless measured precipitation data are 

being discussed, precipitation in this report always refers to 

effective precipitation.

Soil-Moisture Core Samples

As a cross-check against calculated soil-moisture balances (see “Daily Soil-Moisture Balance” section of this 

report), soil-moisture samples were collected two times at each 

site in 2005, and monthly in 2006. The ASTM International 

laboratory methods for gravimetric soil-moisture collection 

were followed (ASTM International, 2006). Each sample 

consisted of a 1-inch diameter core of the top 12 inches of 

soil. A grid was overlaid on an aerial photograph of each site, 

and 10 sample locations were randomly selected each month 

at each site. The 10 cores were then composited into a single 

sample for analysis.

In the gravimetric method, a soil sample is weighed while 

wet (wet weight), then oven dried at 105°C for 24 hours, and 

then weighed dry (dry weight) (ASTM International, 2006). 

The weight lost during oven drying represents the initial soil 

moisture of the sample.

Soil moisture is calculated (Brady and Weil, 2002):

  

 v

g soil

water

 d s

s (1)

where

	 v is volumetric water content for sample 

depth d (inches),

soil is bulk density of soil (g/cm3),

water is density of water (1 g/cm3),

g is gravimetric soil moisture =

 wet weight – dry weight

dry weight – tare ,

and

d is depth of soil sample (inches).

Bulk soil density for each site was calculated in 2006 for 

all sites for both years by using a composite sample of known 

volume. Bulk soil density is determined by dividing the dry 

weight of the sample by its volume. 

Urban Park

Site Visits

The subdivision commons park was visited monthly to 

download data loggers, scan the handwritten logs, and collect 

soil core samples.

Irrigation Event Logs

The subdivision commons park was controlled by a standard residential Rainbird controller and was instrumented with 

a sprinkler-timer data logger. The handwritten log of irrigation events was maintained by the irrigation manager for the 

subdivision in case a backup to the data logger was needed. 

Water Pressure Logs

No log of water pressure was kept for this site because of 

limitations in the site piping.

Flow Rate Per Zone

Flow rate per zone for the subdivision commons park was 

measured with the GE acoustic flow meter in the same manner 

as at the residential sites.

Precipitation

Precipitation data for the subdivision commons park were 

collected by the weather station for that subdivision. Effective 

precipitation was calculated by subtracting estimated runoff 

from the raw precipitation values.

Water

pressure

sensor

Solar

powered

pressure

logger

 

Figure 3. Irrigation system pressure logger in the field.
Study Methods 9

Soil-Moisture Core Samples

Soil-moisture core samples for the subdivision commons 

park were collected using the same methods and schedule as 

for residential sites. Bulk density also was determined in the 

same manner as described for residential sites.

Urban Gated-Pipe Fields

Site Visits

The urban gated-pipe fields were visited monthly in 2006 

to download data loggers, scan handwritten logs, and collect 

soil-moisture core samples.

Irrigation Event Logs

The two privately owned gated-pipe fields (sites 15 

and 16) had an existing dedicated Bureau of Reclamation 

(BOR) totalizing flow meter at their shared irrigation watersupply lateral line. Because only one field was watered at 

a time (controlled by manually operated valves), inflow 

per field could be accurately recorded by the flow meter. 

The homeowner logged the flow meter reading at the start 

and end of watering for each field, as well as the dates and 

times of watering. The two privately owned fields had 12-inch 

broad-crested flumes installed in the tail-water ditches (fig. 4) 

for measuring runoff (tail-water flow) from the bottom of 

the fields. 

For inflow data to the CSU-operated gated-pipe fields 

(sites 7E and 7W), USGS installed a totalizing flow meter in 

the supply pipe to the field. The 12–inch broad-crested flume 

was installed at the downstream end of the field. A manual log 

was kept by the CSU staff to record irrigation event times, event 

durations, and beginning and ending flow-meter readings. 

The field was always watered in halves, with each half 

being watered on consecutive days. For analysis purposes, 

these two field halves were treated as two separate sites.

The broad-crested flumes have a rating that allows the 

depth of water in the throat of the flume to be converted to 

flow in cubic feet per second (ft3

/s). A data logger with a 

pressure sensor in a stilling well was used with each flume to 

record water depth in the throat of the flume. The difference 

between inflow and tail-water runoff yields net water applied 

to the field. Subtraction of cumulative ET during the event 

from the net water applied then gives deep percolation. 

At the two privately owned fields, a digital pressure 

gage was initially installed to record stage at each flume, but 

after the first four irrigation events both pressure gages failed. 

For the remaining irrigation events at each of the two fields, 

runoff was estimated for each event by using linear regression equations (Helsel and Hirsch, 2002), which were derived 

from inflow compared to runoff data for the four events with 

good (pressure gages were working) runoff records (fig. 5A, 

B). The regression equations are (a) y = 0.7635x – 0.1868, 

and (b) y = 0.0373x + 0.031 (Pearson correlation coefficients 

R2

 = 0.7319, R2 = 0.2172, respectively). Note that the axis 

scales are different in both graphs to allow for maximum resolution of the data points.

At the CSU-operated gated-pipe fields, the pressure 

gage stage recorder successfully recorded the first irrigation event for each field. Due to equipment failure, no runoff 

data were recorded for the next three irrigation events at 

each field. For the final event at each field, a strip-chart stage 

recorder was installed that collected good flume-stage data. 

A linear regression equation (Pearson correlation coefficient 

R2

 = 0.9999) (fig. 5C) was created using the four known good 

data points to estimate runoff for the three irrigation event 

pairs without measured runoff data. The regression equation is 

(c) y = 1.4774x – 0.8834. Note that the axis scales are different in each of the graphs to allow for maximum resolution of 

the data points.

Precipitation

Precipitation for the two privately owned gated-pipe 

fields was compiled from the CSU COAGMET Grand 

Junction weather station (approximately 3 miles from site). 

Precipitation for the CSU Orchard Mesa Research Center was 

compiled from the onsite CSU COAGMET Orchard Mesa 

weather station (Colorado State University, 2005–2007). 

Effective precipitation was calculated by subtracting estimated 

runoff from raw precipitation values.

Soil-Moisture Core Samples

For the three gated-pipe fields, soil-moisture core 

samples were collected using the same methods and schedule 

as for residential sites. Bulk density also was determined in the 

same manner as described for residential sites.

Figure 4. Broad-crested flume used to measure gated-pipe field 

tail-water runoff.
10 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Irrigation-Water Holding Ponds

A standard USGS staff plate was installed in each pond 

to allow measurement of pond stage, and the datum (elevation) 

of the staff plate was surveyed. At the end of the irrigation season, after the inflow of water to the pond had ceased, the stage 

levels were manually recorded approximately weekly. 

Daily pan evaporation data were obtained during the 

same period at the CSU Orchard Mesa Research Center. The 

Research Center operates the only Class A evaporation pan 

in the Grand Valley and is a distance of approximately 2, 7, 

and 9 miles, respectively, from the pond sites. Pan evaporation overstates actual lake evaporation and requires downward 

adjustment by use of a pan coefficient multiplier. For this 

study, the measured pan evaporation values were multiplied 

by 0.70, which is the published pan coefficient for western 

Colorado (Ward and Trimble, 2004). Precipitation data were 

obtained from the nearest CSU COAGMET Grand Junction 

and Orchard Mesa weather stations. Effective precipitation 

values were calculated by subtracting estimated runoff. No 

water accumulated in the ponds over the winter, after the 

canal inflow was shut off, so ground-water inflow probably 

does not occur.

2005 Irrigation Field Season

The 2005 irrigation field season started on April 15 and 

ran through November 29. Monitoring equipment was installed 

and irrigation water was available to all sites by April 15, but 

the initial soil-moisture samples were not collected until various dates in May. Because the initial soil moisture is the starting value for the daily soil-moisture balance calculations, it 

was assumed that the initial soil moistures were at field capacity on April 15 (Dan Champion, Colorado State University, 

oral commun., 2005). A late-season soil-moisture sample also 

was collected at each site for verification of soil moisture. 

The Paradise Hills pond was monitored for 22 days starting 

November 1.

At the end of the 2005 season, the data sets for six 

sites (numbers 3, 4, 5, 17, 19, and 20), composed of four 

bluegrass sites, one orchard grass site, and one native-plant 

site, were excluded from analysis because the handwritten 

logs were incomplete in three cases, and the others had 

excessive handwritten log errors when compared with the 

pressure logs.

2006 Irrigation Field Season

The 2006 irrigation field season ran from April 1 through 

November 25. All monitoring equipment was in place, and initial soil-moisture samples were collected for all sites by April 

1. Monthly soil-moisture samples were collected throughout 

the season. In November, an additional soil sample with a 

known volume was collected at each site (2005 and 2006) to 

calculate bulk density values.

Pond stage data were obtained at all three pond sites 

(Paradise Hills, Quail Run, and Chipeta Pines) for 25 days 

starting on November 1, from the shutdown of the irrigation 

canals until freezing of the CSU evaporation pan. At the end 

of the 2006 season, all sites had acceptable data sets, and no 

sites were excluded from analysis.

C Sites 7W and 7E

B Site 16

A Site 15

y = 0.7635x – 0.1868

R2

 = 0.7319

y = 0.0373x + 0.031

R2 = 0.2172

y = 1.4774x – 0.8834

R2 = 0.9999

0.60 0.70 0.80 0.90 1.00 1.10 1.20

INFLOW, IN ACRE-FEET

0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00

INFLOW, IN ACRE-FEET

0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70

INFLOW, IN ACRE-FEET

0

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

RUNOFF, IN ACRE-FEET

0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

RUNOFF, IN ACRE-FEET

0.82

0.84

0.86

0.88

0.90

0.92

0.94

0.96

0.98

1.00

RUNOFF, IN ACRE-FEET

Figure 5. Runoff linear regression equations for gated-pipe fields 

(A) site 15, (B) site 16, and (C) sites 7W and 7E, Grand Valley, western 

Colorado. Note that the axis scales are different on the three graphs.
Study Methods 11

Deep Percolation Calculations

Deep percolation was calculated in two ways:

•	 Daily soil-moisture balance calculation for residential 

lots, estates, and gated-pipe fields, and

•	 Pond-water balance calculation for irrigation-water 

holding ponds.

Calculation of the daily soil-moisture balance is preferable for reasons of accuracy, although collecting daily data 

requires an intensive effort. For this study, it was possible to 

obtain daily data for all residential sites, the commons park, 

and all gated-pipe field sites. Pond-water balance calculations 

were made by measuring stage changes in the pond during an 

observation period, while accounting for pan evaporation and 

total daily precipitation.

Daily total deep percolation calculations require the 

determination of a soil-moisture balance for the top 12 inches 

of soil at each study site. The bluegrass and orchard grass 

at the study sites had roots that were typically less than 

6 inches in depth, so the top 12 inches of soil was considered 

to encompass adequately the rooting depth of the grass. One 

site in the study contained native plants that probably had 

roots deeper than 12 inches, but this was not accounted for 

in soil-moisture calculations.

Soil-moisture balance is the sum of all inflows and all 

outflows into the soil column. The inflows include the portion 

of rainfall and irrigation applications that infiltrate the soil, as 

well as any ground-water infiltration. For the purpose of this 

study, ground-water inflow was assumed negligible and was 

not accounted for. The outflows include evapotranspiration 

(ET), surface runoff, and deep percolation.

Units of Measure

For agricultural use, irrigation-water application is 

traditionally reported in units of inches or acre-feet (Brady 

and Weil, 2002). An inch is the amount of applied water that 

would cover any given area an inch deep. To obtain a volume 

of water from inches, the area of coverage is required. Volume 

of irrigation water also is measured in acre-feet, which is the 

amount of water that would cover an area of 1 acre to a depth 

of 1 foot. All values of irrigation application, rainfall, and deep 

percolation for this study are reported in units of either inches 

or acre-feet.

Soil Moisture

A soil column is analogous to a sponge with a certain 

capacity to hold water in equilibrium. For agricultural purposes, there are several components of soil-moisture calculations (fig. 6) (Brady and Weil, 2002). Field Capacity (FC) 

is the quantity of water in inches remaining after a saturated 

column of soil has been allowed to drain (typically for 

24 hours) so that capillary forces holding the water in place are 

balanced by the force of gravity drawing the water downward. 

Soil moisture in excess of FC is commonly considered to drain 

from the soil column to become deep percolation. Permanent 

Wilting Point (PWP) is an amount of water in the soil column 

(in inches) below which the water is unavailable to the plant. 

Between FC and PWP is the Total Available Water (TAW) 

to the plant. Only a portion of the TAW is considered to be 

Readily Available Water (RAW). The percentage of TAW that 

is RAW is typically 40 percent and is a function of soil type 

(Duke, 1991). The transition point between readily available 

and not readily available water is called the ET stress point in 

this report.

FC and PWP values are soil specific. For typical clayloam soils such as those in the study area, FC = 4.59 inches 

of water and PWP = 2.54 inches (Duke, 1991). For loam 

soils, the published value for FC = 4.11 inches of water, and 

PWP = 1.60 inches. Some empirical corrections were made to 

FC and PWP at several sites and are discussed later in the section on Field Capacity Corrections.

Evapotranspiration

Evapotranspiration (ET) caused most of the daily soilmoisture loss at the study sites. For example, an ET value for 

a given day of 0.2 inch means that the crop and soil will lose 

0.2 inch of water that day due to ET processes. The ET can 

be calculated for any time period: hourly, daily, weekly, or 

for an entire season. The ET has two components: (1) direct 

evaporation of water from the soil and plant surfaces, and 

(2) transpiration of water through plant tissues.

Total

soil

moisture 

Not

readily

available

water

Unavailable

water 

Readily

available

water

(RAW)

Total

Available

Water 

(TAW) 

Field Capacity (FC)

(saturation

equilibrium state)

Evapotranspiration

stress point 

Permanent 

Wilting Point (PWP)

Zero moisture

Figure 6. Schematic diagram of soil-moisture characteristics.
12 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

ET is an energy process driven by solar heating and 

wind (advection) (Allen and others, 1998). ET is not commonly measured directly in agriculture settings. Rather, 

equations have been developed to calculate ET energy balance from meteorological data. Input data usually include 

solar radiation, windspeed, air temperature, and humidity. 

Various calibration factors are applied to tailor the equation 

to become crop and soil specific and to account for local 

meteorological conditions.

Urban Study Evapotranspiration Calculation

This study uses the Penman-Monteith (P-M) ET equation, which has been adopted by the United Nations Food and 

Agricultural Organization (FAO) and is defined in their FAO 

Irrigation and Drainage Paper 56 (FAO-56) (Allen and others, 

1998). Although several ET equations and variants have been 

developed, the FAO-56 P-M equation is currently (2008) considered to be the world standard. The FAO-56 P-M ET equation, while fully defined in the FAO-56 paper, is too complex 

to describe in detail here, and ET is normally calculated using 

software such as REF-ET (Allen, 2001). Each variant of the ET 

equation is based on a particular reference crop (either alfalfa or 

turf grass) and yields what is called a reference ET value. Traditionally, the alfalfa reference is called ETr

 and the turf-grass 

reference is called ETo

. The FAO-56 P-M equation is referenced 

to turf grass, which makes it well suited for use in this study. 

Agricultural ET equations are most often referenced to alfalfa.

To obtain the ET value for a particular crop (such as corn 

or wheat) from the reference ET, a crop coefficient is used to 

multiply the reference ET value. The ET equation used in this 

study is:

ETc = [(Kr × Ke) + (Ks × Kcb)] × ETo (2)

where

ETc is the crop-specific evapotranspiration,

[(Kr × Ke

) + (Ks × Kcb)] is the composite crop coefficient,

Kr is the soil evaporation stress factor,

Ke is the “soil evaporation coefficient” 

due to soil evaporation,

Ks is the crop stress factor,

Kcb is the “basal crop coefficient” due to 

plant transpiration,

and

ETo is the reference evapotranspiration.

Crop coefficients can be simple or complex, depending on the 

need for accuracy. The FAO-56 P-M method allows for the use 

of a dual crop coefficient with two terms: (1) a term for plant 

transpiration, Kcb, the “basal crop coefficient” and (2) a term 

for soil evaporation, Ke

, the “soil evaporation coefficient.” This 

dual crop coefficient technique allows for a separate determination of the effects on ET from the soil and the crop characteristics and is considered more accurate.

The basal crop coefficient, Kcb, is a calculated constant 

value (from a lookup table) for the particular crop (Allen and 

others, 1998, chapter 7). This constant value is then modified 

daily using a standard equation to account for local mean daily 

windspeed, mean daily minimum relative humidity, and mean 

plant height. The daily windspeed and relative humidity data 

for this study were compiled from local meteorological data 

from the nearest CSU COAGMET weather station. The basal 

crop coefficient lookup table value for turf grass is 0.90 (Allen 

and others, 1998, chapter 7).

The soil evaporation coefficient, Ke

, for a given crop is 

calculated in a standard equation to account for the percentage of exposed soil and the amount of water depleted from 

the soil. Ke

 is limited by a calculated upper limit for the 

total evaporation and transpiration, which is possible from a 

cropped surface (Allen and others, 1998, chapter 7).

A technique to gain further ET accuracy is to correct 

each of the dual terms of the crop coefficient for the “stress 

condition” of the crop and soil, respectively (Allen and others, 

1998, chapters 7 and 8). The crop stress factor, Ks

, is based 

on the premise that when soil moisture is in the RAW region 

of the TAW (see fig. 7A) between FC and PWP, ET functions 

normally, as calculated by the basic FAO-56 P-M equation 

(fig. 7A). When soil moisture falls below the ET stress point, 

however, the plant begins to conserve moisture and transpires 

less. It is assumed that below the RAW region, the ET declines 

linearly to zero at the PWP. Typical RAW for clay loams is 

40 percent of TAW (Duke, 1991).

Similar to the crop stress factor, a soil evaporation stress 

factor, Kr

, is based on the premise that due to the degree of 

wetness of the soil surface, there is a Readily Evaporable 

Water (REW) region between FC and PWP similar to the 

RAW zone. When soil moisture is within the REW, soil 

evaporation is predicted by the basic FAO-56 P-M equation 

(fig. 7B). Below the ET stress point, soil evaporation declines 

linearly to zero at the PWP. REW is typically considered to be 

40 percent of TEW (Allen and others, 1998, chapter 7).

The crop stress and soil evaporation coefficients have a 

substantial effect in reducing calculated daily ET when soil 

moisture is not maintained in the optimum range for evaporation and transpiration.

Natural Resources Conservation Service Monitoring 

and Evaluation Evapotranspiration Calculation

Descriptions of the ET calculation methods used in 

the NRCS M&E were not readily available. By interviewing 

the personnel (now mostly retired) from that study, it was 

determined that two ET equations were used during different periods, with a probable crossover time of 1989. From 

1985 until 1988, a software program called SCHED was 

used. SCHED was developed by Dale Heerman and associates at the Fort Collins Agricultural Research Service (ARS) 

office of USDA, and used a modified Penman alfalfa-crop 

reference ET equation with calibration coefficients for Scotts 

Bluff, Nebraska (Buchleiter and others, 1992). This equation 
Study Methods 13

was designed primarily for center pivot irrigation in eastern 

Colorado; however, applicability to the arid western Colorado 

climate is not known.

In 1989, the NRCS M&E apparently began using an 

alfalfa-referenced Jensen Haise ET equation on a personal 

computer (PC) spreadsheet (Harold Larsen, Colorado State 

University, oral commun., 2006). No details are known about 

the adjustment coefficients used with the Jensen Haise equation (Duke, 1991). A time-dependent crop coefficient based on 

stage of crop growth was used.

For the NRCS M&E, season-total ET values were calculated per monitored site for the years 1985 through 1998. 

No ET data were calculated per site for 1999 through 2002. 

One spreadsheet from 1999 was available that contained daily 

ET values from 1989 through 1999, calculated using data 

from two NRCS-operated weather stations (CSU Fruita and 

Orchard Mesa Experiment Stations). 

To test the validity of the NRCS M&E ET values, new 

ET values were calculated for 1993 to 1999 using archived meteorological data from the CSU COAGMET Web site (Colorado 

State University, 2005–2007) and the REF–ET software program 

(Allen, 2001). The REF–ET program provides ET values simultaneously from a number of different ET equations. By linear 

regression, it was determined that the NRCS M&E ET values 

were closely matched to the ET values generated with the 1982 

Kimberly-Penman Equation (Pearson correlation coefficient 

R2

 = 0.99). The CSU COAGMET Web site also uses the 1982 

Kimberly-Penman equation for published daily ET values.

Further comparison, using a scatterplot of the 1982 

Kimberly-Penman ET values with ET results for the same 

period of years calculated in REF–ET with the FAO-56 PenmanMonteith equation (the ET method used in this study), yielded a 

Pearson correlation coefficient of R2

 = 0.97. The 1982 KimberlyPenman method was thus an analytical bridge between the NRCS 

M&E ET method and the present study method. By this indirect 

method, it was determined that the historical NRCS M&E ET 

values for the time period of 1993 to 1999 were an acceptable 

match with the urban study ET values, and the calculation method 

was close enough to the current study ET method for the purposes 

of deep percolation comparisons.

Daily Soil-Moisture Balance

To calculate daily deep percolation, it is necessary to 

determine if soil moisture at the end of each day is greater than 

the field capacity. This is done by calculating a preliminary 

daily soil-moisture balance, disregarding deep percolation. 

Calculated soil moisture is then compared to the field capacity, and if the soil moisture is greater than field capacity, the 

difference is assumed to be deep percolation.

The equation for preliminary daily soil-moisture balance 

(ignoring ground-water inflow and deep percolation) is:

balancepreliminary = balanceprevious

+ precipitationdaily effective + irrigationdaily net – ETc (3)

where

balancepreliminary is the soil-moisture balance at the end of 

the current day before deep percolation 

is taken into account (inches),

balanceprevious is the soil-moisture balance at the end 

of the previous day (inches),

precipitationdaily effective is daily measured precipitation minus 

predicted runoff (inches),

irrigationdaily net isthe irrigation inflow multiplied by 

irrigation efficiency minus any tailwater flow (irrigation efficiency is 

the percentage of irrigation water 

flowing into an irrigation system that 

actually is applied to the plants and 

the ground) (inches),

and

ETc is the daily crop-specific 

evapotranspiration corrected 

for crop stress (inches).

Figure 7. (A) Crop stress factor Ks for transpiration portion of 

composite crop coefficient, and (B) soil evaporation factor Kr

for evaporation portion of composite crop coefficient.

B Soil Evaporation Stress Factor Kr

A Crop Stress Factor Ks

Not readily evaporable water Readily evaporable water (REW) EVAPOTRANSPIRATION STRESS POINT

Not readily available water Readily available water (RAW) EVAPOTRANSPIRATION STRESS POINT

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

PERCENT TOTAL EVAPORABLE WATER (TEW) Permanent

Wilting Point (PWP) 

Field Capacity

(FC)

PERCENT TOTAL AVAILABLE WATER (TAW) Permanent

Wilting Point (PWP) 

Field Capacity

(FC)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

SOIL EVAPORATION STRESS FACTOR (

Kr

)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROP STRESS FACTOR (

Ks

)
14 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

The daily deep percolation is then calculated:

deep percolationdaily = moisture balancepreliminary (4)

– field capacity

where

deep percolationdaily is the daily deep percolation 

(inches). (Negative values of deep 

percolation are treated as zero),

moisture balancepreliminary is the moisture balance before deep 

percolation is subtracted (inches),

and

field capacity is the saturated water-holding capacity 

of soil (a constant in inches).

The ending soil-moisture balance, balanceending, 

is calculated as:

balanceending = balancepreliminary – deep percolationdaily (5)

where

balanceending is the daily ending soil-moisture 

balance (inches),

balancepreliminary is the daily soil-moisture balance 

before deep percolation is 

subtracted (inches),

and

deep percolationdaily is the daily deep percolation (inches).

By incorporating the acreage, the volume of daily deep percolation in acre-feet can be calculated as:

deep percolationacre-feet =

deep percolationinches*irrigated areasacres

12inches per foot

(6)

where

deep percolationacre-feet is the deep percolation converted to 

acre-feet,

deep percolationinches is the deep percolation in inches,

and

irrigated areaacres is the acreage of irrigated area.

Daily Soil-Moisture Balance Graph

To visualize the daily changes in soil-moisture balance for a 

site, a graph was created for each site. A typical plot of daily soil 

moisture for bluegrass on a 1/4-acre residential site is shown in 

figure 8. The vertical axis represents inches of water, with positive 

values indicating inflows and negative values indicating outflows. 

The horizontal axis represents days of the irrigation season.

The inflows and outflows represented in this graph are:

•	 Orange trace line indicates the calculated daily soilmoisture balance;

•	 Blue vertical bars indicate the effective precipitation 

(measured less predicted runoff);

•	 Green vertical bars indicate the irrigation application, 

corrected for irrigation efficiency and tail-water flow;

•	 Blue-grey solid trace indicates the evapotranspiration 

(ET), corrected for crop stress;

Figure 8. Soil-moisture balance for bluegrass on 1/4-acre residential lot site 1, Grand Valley, western Colorado.

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.52

Apr 3

Apr 10Apr 17Apr 24

May 1May 8

May 15May 22May 29

June 5

June 12June 19June 26 July 3

July 10July 17July 24July 31

Aug 7

Aug 14Aug 21Aug 28

Sept 4

Sept 11Sept 18Sept 25

Oct 2

Oct 9

Oct 16

Oct 23Oct 30

Nov 6

Nov 13

2006

–1

0

1

2

3

4

5

6

INCHES OF WATER

Site 1

Chipeta Pines

3.16

4.62

3.70

4.69 4.52

4.14

3.64

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE
Study Methods 15

Table 5. Soil-moisture balance model parameters for sites that use daily calculations.

[Field capacity, saturated water-holding capacity of soil; permanent wilting point, point at which no water is available for evapotranspiration; irrigation efficiency, 

empirically determined efficiency of irrigation-water application; %, percent; basal crop coefficient, starting crop coefficient for FAO-56 Penman-Monteith dual crop 

coefficient calculations; exposed fraction of soil, % of soil without vegetation; readily available water, amount of water that plant can readily use for evapotranspiration; readily evaporable water, amount of water that soil can readily lose in evapotranspiration]

Site

number Vegetation Soiltype

Field

capacity

(inches)

Permanent

wilting

point

(inches)

Irrigation

efficiency

(%)

Basal crop

coefficient

(%)

Exposed

fraction

of soil

(%)

Readily

available

water/readily

evaporable

water

(%/%)

Crop

height

(inches)

Measured

bulk density

(grams per

cubic

centimeter)

1 Bluegrass Loam 4.59 2.54 75 90 5 40/40 4 1.46

2 Bluegrass Clay loam 4.59 2.54 75 90 5 40/40 4 1.55

6 Bluegrass Loam 4.59 2.54 75 90 5 40/40 4 1.70

7W Orchard grass Clay loam 4.59 2.54 70 90 50 60/60 12 1.57

7E Orchard grass Clay loam 4.59 2.54 70 90 50 60/60 12 1.57

8 Bluegrass Loam 4.59 2.54 75 90 5 40/40 4 1.49

9 Bluegrass Clay loam 4.59 2.54 90 90 5 40/40 4 1.58

10 Native Clay loam 4.59 0.50 75 90 20 40/40 4 1.65

11 Bluegrass Clay loam 4.59 2.54 65 90 5 40/40 4 1.46

12 Bluegrass Loam 4.59 2.54 90 90 5 40/40 4 1.57

13 Bluegrass Clay loam 4.59 2.54 75 90 5 40/40 4 1.55

14 Bluegrass Clay loam 4.59 2.54 75 90 5 40/40 4 1.46

15 Orchard grass Loam 4.59 2.54 70 90 50 60/60 12 1.55

16 Orchard grass Clay loam 4.59 2.54 70 90 50 60/60 12 1.55

17 Orchard grass Clay loam 4.59 1.00 75 90 5 40/40 4 1.56

18 Bluegrass Clay loam 4.59 2.54 70 90 5 40/40 4 1.65

21 Bluegrass Clay loam 4.59 2.54 50 90 5 40/40 4 1.55

22 Bluegrass Loam 4.59 2.54 55 90 5 40/40 4 1.57

•	 Red vertical bars indicate the deep percolation;

•	 Black dots with values indicate the measured soil moisture from core samples;

•	 Blue horizontal trace indicates the field capacity constant;

•	 Grey dashed trace indicates the evapotranspiration 

stress point constant;

•	 Brown horizontal trace indicates the permanent wilting 

point constant.

The soil-moisture balance (orange trace) is approximately fitted to the monthly soil-moisture values (black dots). This fit is 

affected by choices for field capacity, permanent wilting point, 

ET stress parameters, and particularly by irrigation efficiency. 

These parameters are discussed below.

Physics of the Daily Soil-Moisture Balance 

Calculation Technique

The daily soil-moisture balance spreadsheet calculation 

technique (equations 2–6 for this report) included a number of 

features to optimize the model to fit the physical system of an 

irrigated lawn. These features included:

•	 Correction of precipitation for estimated runoff 

as a function of rainfall intensity, soil type, and slope;

•	 Correction of irrigation-water application to account for 

losses due to leakage, evaporation, overspray, and runoff;

•	 Correction of evapotranspiration for crop stress 

and soil stress effects (already discussed);

•	 Calibration of soil-moisture balances by using gravimetric soil-moisture samples; and

•	 Adjustment (at some sites) of soil physics parameters 

(field capacity and wilting point) to better match 

field results.

The soil-moisture balance calculation technique input 

parameters are discussed below and are listed by site in 

table 5.

Soil Type

Soil type for each site determines the field capacity and wilting point (Duke, 1991). Soil types were determined graphically 

using the National Cooperative Soil Survey (NCSS) Web Soil 

Survey Web site (U.S. Department of Agriculture, 2007). Where 

two soil types occurred at a site, the proportions of each were 

determined and used to determine field capacity and wilting point 

for the site. The soil types provided by NCSS are for natural, 

undisturbed soil samples. Residential soil is typically disturbed 

by the construction process (wheel compression, backfilling, or 

mixing with gravel, sand, and construction debris), and further by 

soil amendments and changes made by the homeowner. Published 

soil types were used for the study sites, as field measurement of 

soil type was not practical. Some empirical refinements of soil 

physics parameters were made, as discussed herein.
16 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Bulk density is a direct multiplier in the gravimetric 

soil-moisture calculation, making accurate bulk density values 

essential to determining soil moisture. The study determined 

bulk density by direct soil sampling at each site. These measured bulk densities typically were slightly higher than the 

published values for the soil types (Duke, 1991). Measured 

bulk densities for the study sites are given in table 5.

Field Capacity Corrections

The field capacity (FC) value, in inches, for each site, is 

shown in table 5. Published values for field capacity are based 

on soil types and are determined by a broad range of conditions (Duke, 1991). It is possible that the published FC reference value for a soil type does not precisely match the actual 

conditions at a given site with a matching soil type. The original 

researchers who defined FC commented that “field capacity is 

affected by so many factors that, precisely, it is not a constant 

(for a particular soil), yet it does serve as a practical measure 

of soil water-holding capacity” (Veihmeyer and Hendrickson, 

1949). In this study, it was observed from the soil-moisture balance graphs that for sites with loam soils, an FC of 4.11 inches 

was systematically too low to explain the gravimetrically 

measured soil-moisture values at those sites. By arbitrarily 

raising the FC to that of clay loam (4.59 inches), the calculated 

soil-moisture balance more closely matched the gravimetrically measured values. Possible explanations for this effect are: 

(1) there is inaccuracy in the soil typing for those sites, and(or) 

the site is on an unmapped soil inclusion; or (2) the soil has 

been altered during construction and by the homeowner with 

soil amendments to improve turf quality and no longer performs 

like the native soil type (Frank Riggle, Natural Resources 

Conservation Service, written commun., 2007).

Considering the first of these possible explanations, within 

each soil-mapping unit there are areas of similar and(or) dissimilar soils called inclusions. These inclusions are usually 

described in the written soil survey narrative but are too small 

to show on maps. The soil type at an individual site may be 

different from the published type due to mapping or description errors, or the site may be located in a map unit inclusion 

(Frank Riggle, written commun., 2007).

The second possibility, changes to the native soil characteristics, would seem to be the most likely explanation. Fine-grained 

soils have larger field capacities than coarse-grained (sandy) soils. 

Thus, more water is available for actual ET from fine soils than 

coarse soils (Ritter, 2006). As discussed above, the bulk densities 

for most sites were higher than published values, which would be 

expected for soils with more fine grains.

Permanent Wilting Point Corrections

The permanent wilting point (PWP) was determined 

by the soil type at each site (table 5) from published values 

(Duke, 1991). In two cases, for orchard grass and for native 

plants (sites 17 and 10, respectively), the published PWP 

values did not allow the calculated soil-moisture balance to 

fall as low as the measured soil moistures were indicating. It 

was found that lowering the PWP allowed the calculated soil 

moisture to match closely the measured soil-moisture values. 

It is probable that for orchard grass and native plants, the root 

depth extends deeper than 12 inches, and thus the soil moisture in the top 12 inches of soil does not accurately reflect the 

moisture conditions that are available to the lower portion of 

the plant roots. This would imply that the PWP of the crop is 

not as quickly reached as for turf grass, whose roots are often 

only a few inches in depth.

Irrigation Efficiency

GIS was used to determine turf area (in square feet) for 

each site using 2001 to 2005 aerial photographs provided by 

Mesa County (Mesa County, Colo., 2005). Selected details 

were visually checked in the field for each site. Sprinkler 

overspray onto sidewalks and driveways was quantified one 

time for each site. The area of overspray was calculated and 

was used as a correction to the total irrigated area for the site. 

This had the effect of reducing the amount of water applied to 

the turf areas. Runoff into curbs and ditches also was observed 

one time for each site, but these values were considered to be 

included in the general number for irrigation efficiency. There 

was substantial overspray at a few sites, but relatively little 

runoff was observed at any of the sites.

Irrigation efficiency is the fraction of irrigation water 

available at the input to the irrigation system that actually ends 

up being applied to the crop and soil. It factors in bulk corrections to account for leakage in the pipes, wind losses, runoff, 

overspray, and crop canopy evaporation. Selection of different 

values of assumed irrigation efficiency has a striking effect on 

the calculated soil-moisture balance. Typical published irrigation efficiency values for agricultural sprinkler irrigation range 

from 50 to 90 percent (Howell, 2003); however, no published 

irrigation efficiencies for residential irrigation systems were 

found. A University of Florida residential irrigation researcher 

suggested residential irrigation efficiencies ranging from 50 

to 90 percent, with 75 percent as a starting point, would be 

appropriate (Michael Dukes, University of Florida, Gainesville, written commun., 2006).

By comparing the calculated soil-moisture values on the 

days that the monthly measured soil-moisture samples were 

collected in 2006, the irrigation efficiency percentages were 

increased or decreased to achieve the best overall visual fit 

of the calculated soil-moisture curve to the measured monthly 

soil-moisture values. Because the irrigation efficiency percentage is applied linearly to each daily soil-moisture calculation, it 

was not possible to fit the curve to every measured soil-moisture 

value. Some measured soil-moisture values remained outliers 

to the calculated soil-moisture curve, probably due to unknown 

inadequacies of the daily soil-moisture balance model or local 

anomalies of measured soil moisture. Table 5 lists the derived 

irrigation efficiency for each site. Irrigation efficiency values 

of 70 or 75 percent were appropriate at 13 of the 18 residential 

study sites, with the other 5 sites (9, 11, 12, 21, and 22) requiring 90, 65, 90, 50, and 55 percent, respectively. An examination 

of the characteristics of these five sites (table 2) does not yield 
Study Methods 17

any obvious reasons that explain their atypical derived irrigation 

efficiencies. Three of the sites are 1/4-acre lots with bluegrass, 

and the other two sites are 5-acre estates with bluegrass. All five 

sites used a mixture of pop-up spray heads and impulse heads. 

One site has been in existence for approximately 20 years, with 

the other four sites being under 5 years of age. It was not possible at any of the sites to judge the conditions of the pipes to 

determine if leaks were occurring.

Evapotranspiration Stress Correction Parameters

The ET stress correction parameters that are required for 

the FAO-56 Penman-Monteith ET calculation method were 

entered into the daily soil-moisture balance model once for 

each site. These parameters include the basal crop coefficient, 

percentage of readily available water, percentage of readily 

evaporable water, exposed fraction of soil, and crop height in 

inches. Table 5 shows the ET stress parameters selected for 

each site. The daily meteorology parameters for stress correction were entered in the model from the CSU COAGMET 

weather station data closest to each site.

Whole Subdivision Irrigation-Water Application 

and Deep Percolation

The total irrigated area in each of the three subdivisions 

was determined graphically, lot by lot, from color and infrared 

2001 to 2005 aerial photographs provided by Mesa County. 

The total subdivision areas were determined using the aerial 

photographs in GIS, and the percentage of irrigated land in each 

subdivision was calculated. The mean values of irrigation water 

applied and deep percolation were determined for bluegrassonly and all-vegetation type study sites in each subdivision. 

Mean site values were multiplied by total irrigated area in each 

subdivision to provide an estimate of the volume of irrigation 

water applied and deep percolation for each whole subdivision.

Holding-Pond Water Balance

Topographic Surveys

To calculate the seepage of pond water over time, it was necessary to create an area-capacity curve to relate the pond-water 

level (stage) to pond volume. The bathymetry of two of the ponds 

(Quail Run and Chipeta Pines) was surveyed while dry using total 

station surveying equipment (Anderson and Mikhail, 1998). The 

Paradise Hills pond was surveyed while full of water using acoustic Doppler depth-sounding equipment in conjunction with a 

high-accuracy GPS receiver (Wilson and Richards, 2006). Using 

the bathymetry data, a 3D model of each pond was created in 

ARCMap (Environmental Systems Research, Inc., 1999–2005). 

Using ARCMap 3D Analyst, the 3D model was then divided into 

0.1-meter (0.33-ft) horizontal surfaces, and the volume below 

each slice was calculated. These volumes were then plotted in 

Microsoft Excel to fit a 3d-order polynomial area-capacity equation of stage in relation to volume for each pond. The three pond 

area-capacity equations are:

Quail Run Pond:

V = –69.867 × S3

 + 1912.2 × S2 – 1448.3 × S + 231.39 (7)

(R2 = 0.9999),

Chipeta Pines Pond:

V = –39.914 × S3

 + 1153.9 × S2 – 500.65 × S + 74.649 (8)

(R2 = 0.9998),

Paradise Hills Pond:

V = –842.9 × S3

 + 11968 × S2 – 8603.8 × S + 727.71 (9)

(R2 = 0.9999),

where

V is pond volume in cubic feet,

S is pond stage in feet,

and

R2 is correlation coefficient, between 0 and 1, with 

1 being the best fit of the equation to the data.

Stage change during the observation period was converted to volume using the area-capacity curve. Seepage from 

the unlined pond was then estimated from the change in volume, accounting for pan evaporation and effective precipitation. Pond stage was observed for about 3 weeks in November, 

from the time irrigation water was cut off until freezing temperatures halted the ability to collect pan evaporation data.

Water Balance Calculation

The pond-water level (stage) was monitored after the 

irrigation water inflow to the pond was stopped at the end of 

the irrigation season; thus, irrigation water inflow equals zero 

in the water balance equation. In addition, pumping from the 

pond was discontinued, so there were only two components 

for outflow: evaporation and seepage. Because it was assumed 

that all the ponds in the study were perched, ground-water 

inflow could be ignored. Evaporation was measured using an 

evaporation pan. Seepage from the pond is calculated:

seepagepond = pond stagestarting – pond stageending

– total evaporation – precipitationdaily (10)

where

seepagepond is the water lost from the pond during 

observation period (inches),

pond stagestarting is the level of water at start of observation 

period (inches),

pond stageending is the level of water at end of observation 

period (inches),

total evaporation is the pan evaporation during observation 

period (inches),

and

precipitationdaily is the measured total daily precipitation 

(inches).

Seepage for an entire irrigation season was estimated 

by multiplying average daily seepage by the total number of 

season days.
18 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Salt-Loading Calculation

Salt loading in the Colorado River occurs when irrigation 

water percolates below the root zone, or seeps from a pond, and 

eventually reaches the river with salt that has leached from the 

Mancos Shale soils in the Grand Valley. Salt loading is expressed 

in tons of salt per acre-foot of deep percolation, and is calculated using a standard “loading factor” of 4.1 tons per acre-foot 

of deep percolation for the Grand Valley (Hedlund, 1994). The 

volume of deep percolation from a site is assumed by NRCS 

to be decreased by a “standard average return flow reduction.” 

This reduction corrects for deep percolation and seepage that is 

picked up and reused by lower lying irrigation systems, intercepted by deep rooted plants such as shrubs and trees, used by 

phreatophytic plants, evaporated in irrigation-induced wetlands, 

or used by vegetation along the irrigation drains. The standard 

average return flow reduction for the Grand Valley is 50 percent, 

a value administratively set by the NRCS for Utah, Wyoming, 

and Colorado (Frank Riggle, oral commun., 2007). This means 

that only 50 percent of the deep percolation at a site is assumed to 

contribute to salt loading at the river.

The salt loading equation is thus:

salt loading = salt loading rate 

× deep percolation × return flow factor, (11)

where

salt loading is the quantity of salt reaching the Colorado 

River (tons),

salt loading rate is the standard leaching rate, 4.1 tons per 

acre-foot for Grand Valley, 

deep percolation is the irrigation water reaching the groundwater table, equivalent to seepage for a 

pond (acre-feet),

and

return flow factor is the amount of water that is subsequently 

re-used for irrigation or used by vegetation 

(50 percent).

Sources of Measurement Calculation Errors

Principal sources of error in estimates of factors used to 

calculate deep percolation are shown in table 6. The types of 

errors fall into two categories: (1) inherent errors of instrumentation measurements; and (2) assumed irrigation application 

efficiency values.

Instrumentation Measurement Errors

The manufacturer’s specifications for accuracy of the 

various data collection instruments are shown in table 6. 

Because the instruments are relatively accurate, compared 

with other potential sources of error in the study, it was 

determined that instrument error could be disregarded for 

data analysis purposes.

Irrigation Application Efficiency Errors

The derivation for irrigation efficiency at each site was 

discussed in the Daily Soil-Moisture Balance section. The 

comparability or “fit” of the calculated soil-moisture curve to 

the measured soil-moisture core samples is very sensitive to the 

value selected for irrigation efficiency. As such, the selected 

value for irrigation efficiency directly affects the final deep 

percolation calculations. Because it was not possible to quantify 

irrigation efficiency, the irrigation efficiency value was adjusted 

to achieve the best visual fit of the soil-moisture balance curve 

to measured soil-moisture values for each site. Irrigation efficiency values selected for each site are shown in table 5.

Handwritten Event Log Errors

In 2006, homeowners kept a handwritten irrigation event 

log as a backup data source in case of data logger malfunctions. An indirect benefit of this backup data set was the ability to calculate an error rate for the 2006 handwritten logs that 

could be used to estimate similar errors for 2005 when data 

loggers were not used to automate logging of irrigation events. 

The monthly total of irrigation minutes was obtained from 

the sprinkler-timer data logger for each residential site. These 

totals were compared with the total number of irrigation minutes each homeowner recorded in his log. If the homeowner 

underreported irrigation minutes, this was considered a negative error, and overreported minutes by the homeowner was a 

positive error. A percentage error was calculated for the eight 

residential sites in 2006 (fig. 9).

In general, most homeowners reported irrigation records 

accurately, but several homeowners in 2006 tended to underreport irrigation events during the first 3 months of the season. 

It can be seen in figure 9 that most of the errors occurred 

in the beginning of the irrigation season, probably due to a 

learning curve for the homeowners in keeping the records. 

It was decided that this was an acceptable error rate due to 

the relative low application of water in the early months of 

the season, with little or no resulting deep percolation. No 

corrections were made to the 2005 irrigation event logs, 

although some underreporting could reasonably be assumed 

to have occurred.

Natural Resources Conservation Service 

Monitoring and Evaluation Data

The NRCS M&E deep percolation data for 1985 through 

2002 were available in hard copy form (about 1,000 pages 

of printed annual reports). There were no NRCS M&E sites 

monitored from 1996 through 1998. Table 7 summarizes the 

NRCS M&E data for all crop-type sites and for alfalfa-only 

sites. Many of the NRCS M&E sites were monitored for 

multiple successive years, and each year of data for a site is 

considered to be a separate record for analysis.
Study Methods 19

It was decided to include only NRCS M&E alfalfa-crop 

sites in the comparison with current study sites. Alfalfa was 

recommended as the best agricultural crop for comparison 

by the NRCS (Jim Currier, Natural Resources Conservation 

Service District Conservationist, oral commun., 2006) 

because alfalfa has been a widely grown crop in the Grand 

Valley and commonly is the agricultural crop that is converted to urban residential land use. Alfalfa also represents 

a substantial portion of the NRCS M&E data set and is one 

of the standard reference crops for ET calculations. In addition, 20 unique site locations (67 irrigation seasons of data) 

for alfalfa crops were included in the NRCS M&E data, 

representing a large data set for analysis. The number of 

sites studied per year ranged from 4 to 11. The longest period 

of study for any single site was 10 years. The alfalfa-crop sites 

included eight types of irrigation methods. Irrigated alfalfacrop field size ranged from 2 to 54 acres, with a mean size 

of 23.8 acres.

To preserve landowner anonymity, precise NRCS M&E 

site locations were not recorded, but all alfalfa-crop sites 

included in this analysis were located in the Grand Valley. 

Deep percolation results from the NRCS M&E were summarized by irrigation method, crop type, and field size. Ten 

annual reports listed individual irrigation event data per site, 

while the other five listed only annual totals for irrigation 

events per site.

Table 6. Principal sources of error in estimates of factors used to calculate deep percolation in urban study data.

[±, plus or minus; %, percent]

Data system Type of error Source of error estimate Error magnitude

Effect on 

deep

percolation

results

Sprinkler-timer data logger 

(2006 only)

Starting and ending 

time of event

Data scan rate is every 60 seconds 1 to 59 seconds maximum per 

event start or stop time

Small

GE Panametrics flow meter 

model PT878

Measurement Published specification of PT878 flow 

meter accuracy1

2

±2% Small

Spectrum Technologies 

Weather Station

Measurement Published specification for Weather

Station tipping bucket rain collector3

±2% Small

Irrigation application 

efficiency

Qualitative estimates Uncertainty in amount of leaks, wind 

effects, canopy evaporation, runoff, 

overspray

Unknown Large

1

GE Panametrics flow meter specifications accessed June 11, 2007, at http://www.gesensing.com/products/pt878gc.htm?bc=bc_panametrics

2 GE Panametrics flow meter requires a pipe full of liquid. No flow readings are possible unless the pipe is full.

3

Spectrum Technologies Weather Station specifications accessed June 11, 2007, at http://www.specmeters.com/WatchDog_IPM_Weather_Stations/

Plant_Disease_Weather_Station.html

Figure 9. Comparison of handwritten subdivision irrigation event logs to automated logger 

data for eight sites, 2006 season.

Homeowner

over-reported

irrigation minutes

Homeowner

under-reported

irrigation minutes

Site 18

Site 9

Site 17

Site 11

EXPLANATION

Site 21

Site 12

Site 1

Site 8

2006

April May June July August September October November

–80

–70

–60

–50

–40

–30

–20

–10

0

10

20

PERCENT ERROR1

1

Percent error is the deviation of handwritten

 log from data logger, in minutes per month.
20 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Comparison between Natural Resources 

Conservation Service Monitoring and Evaluation 

Data and Urban Study Data

Comparisons between the two studies included annual 

water application and deep percolation rates. Grand Valley 

alfalfa-crop sites (20 sites, 67 irrigation seasons of data) were 

extracted from the NRCS M&E data for comparison. Data for 

several NRCS M&E alfalfa-crop sites were excluded because 

of erroneous or missing data.

Effects of Conversion of Agricultural 

Land to Urban Land on Deep Percolation 

of Irrigation Water

Residential Lots, Estates, and Parks Results

A data summary for the urban homes and parks for the 

2005 season is listed in table 8. The 2006 season summary is 

listed in table 9.

Table 7. Summary of Natural Resources Conservation Service 

Monitoring and Evaluation (NRCS M&E) sites and seasons for all 

crop types and for alfalfa crops, 1985–2002.

[ET, evapotranspiration; -, no data applicable]

NRCS 

M&E

year

Total number

of all

crop type

sites studied

Total number

of alfalfa-crop

sites studied

Annual/

per irrigation

event ET data

available

Annual/

per irrigation

event data

recorded

1985 1

15 14 Yes Annual

1986 16 4 Yes Annual

1987 16 4 Yes Annual

1988 18 6 Yes Per event

1989 19 9 Yes Per event

1990 25 7 Yes Per event

1991 23 9 Yes Per event

1992 24 11 Yes Per event

1993 21 7 Yes Per event

1994 12 5 Yes Annual

1995 7 1 Yes Annual

1996 0 - - -

1997 0 - - -

1998 0 - - -

1999 1

11 10 Yes Per event

2000 1

6 10 No Per event

2001 1

2 10 No Per event

2002 2 1

0 No Per event

Totals 217 67

1

Some sites omitted due to missing or invalid data.

Example Soil-Moisture Balance Charts

Several soil-moisture balance charts are shown to illustrate typical results of calculations of deep percolation for the 

study. A complete set of soil-moisture balance charts for the 

study is included in Appendix 1.

2005 Soil-Moisture Charts

Figure 10 is the 2005 soil-moisture chart for site 2, a 

1/4-acre residential lot. The vegetation is bluegrass. Cumulative deep percolation for the season was 13.7 inches (table 8). 

Two soil-moisture samples were collected in 2005, in May 

and in September. This homeowner irrigates on a regular 

basis, with a day or two between each application. The 

soil-moisture balance exceeds field capacity starting in July 

and continuing in August, creating deep percolation. Most 

irrigation events during July, August, and September create 

deep percolation because of excessive irrigation application. The early season deep percolation in May could be 

attributed to the assumption that initial soil moisture was 

at field capacity. In the second week of July, the irrigation 

quantity was increased from about 0.5 inch per application 

to about 0.8 inch, leading to deep percolation at almost every 

irrigation application. There is a good fit of the calculated 

soil-moisture balance curve to the two measured soil-moisture 

core sample values.

Figure 11 is the 2005 soil-moisture chart for site 9, a 5-acre 

estate. The vegetation is bluegrass. Cumulative deep percolation 

for the season was 2.4 inches (table 8). This homeowner managed water application carefully, generally keeping the moisture 

balance around the ET stress point. Very little deep percolation 

occurred except at the beginning and end of the season. The early 

season deep percolation in May is attributable to the assumption 

of initial soil moisture being at field capacity, and the late-season 

deep percolation was caused by a combination of reduced ET 

and continued irrigation. There is a good fit of the calculated 

soil-moisture balance to the two measured soil-moisture core 

sample values.

Figure 12 is the 2005 soil-moisture chart for site 6, a subdivision commons park. The vegetation is bluegrass. Cumulative deep percolation for the season was 0.1 inch (table 8). The 

irrigation application rate was relatively low at about 0.3 inch 

every 2 or 3 days, keeping the soil moisture well below the 

readily available water level most of the season. Only 0.1 inch 

of deep percolation occurred at this site. There is a generally 

good fit of the calculated soil-moisture balance to the measured soil-moisture core sample values.

2006 Soil-Moisture Charts

Figure 13 is the 2006 soil-moisture chart for site 8, a 

1/4-acre residential lot. The vegetation is bluegrass. Cumulative deep percolation for the season was 0.8 inch (table 9). 

The homeowner typically irrigated every second or third day 
Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation of Irrigation Water 21

and kept the soil-moisture balance generally just below the ET 

stress point. The only calculated deep percolation occurred in 

October when almost 1 inch of effective precipitation fell in 

one day, as measured at the local subdivision weather station. 

The homeowner suspended irrigation during this wet period, 

so all the deep percolation during that time was attributable 

to rainfall. There is a generally good fit of the calculated soilmoisture balance to the measured soil-moisture core sample 

values, with the exception of the last sample collected in 

October. At that time, the calculated soil moisture was higher 

than the actual soil moisture, likely due to the precipitation 

that fell in early October. The most probable explanation for 

Table 8. Summary of data and results of deep percolation calculations for 2005 irrigation season for residential lots, estates, and 

urban parks.

[Net irrigation applied, irrigation applied multiplied by irrigation efficiency; stress corrected ET, evapotranspiration corrected for crop stress; effective precipitation, 

measured precipitation minus predicted runoff]

Site number

(fig. 1, table 1) Site type Vegetation

Irrigated

area

(acres)

Net

irrigation

applied

(inches)

Stress

corrected ET

(inches)

Effective 

precipitation

(inches)

Total deep

percolation

(inches)

Total deep

percolation

(acre-feet)

1 1⁄4-acre residential lot Bluegrass 0.1 45.1 37.5 2.7 10.2 0.10

2 1⁄4-acre residential lot Bluegrass 0.1 43.8 35.6 5.5 13.7 0.14

6 Commons park Bluegrass 0.5 22.5 25.3 2.7 0.1 0.00

8 1⁄4-acre residential lot Bluegrass 0.1 17.2 21.4 2.7 0.1 0.00

9 5-acre estate lot Bluegrass 1.1 31.4 33.4 4.5 2.4 0.23

10 5-acre estate lot Native plants 0.6 5.0 12.9 4.5 0.0 0.00

11 5-acre estate lot Bluegrass 0.1 26.7 32.1 4.5 0.2 0.00

12 1⁄4-acre residential lot Bluegrass 0.1 25.5 30.9 5.0 0.0 0.00

13 1⁄4-acre residential lot Bluegrass 0.1 36.0 40.8 5.5 1.4 0.02

14 5-acre estate lot Bluegrass 1.0 28.9 32.7 5.2 2.9 0.24

18 5-acre estate lot Bluegrass 0.8 39.7 41.2 5.2 3.5 0.24

22 1⁄4-acre residential lot Bluegrass 0.1 27.6 31.0 5.0 1.2 0.01

Mean 0.4 29.1 31.2 4.4 3.0 0.08

Table 9. Summary of data and results of deep percolation calculations for 2006 irrigation season for residential lots, estates, and urban parks.

[Net irrigation applied, irrigation applied multiplied by irrigation efficiency; stress corrected ET, evapotranspiration corrected for crop stress; effective 

precipitation, measured precipitation minus predicted runoff]

Site number

(fig. 1, table 1) Site type Vegetation

Irrigated

area

(acres)

Net

irrigation

applied

(inches)

Stress

corrected ET

(inches)

Effective

precipitation

(inches)

Total deep

percolation

(inches)

Total deep

percolation

(acre-feet)

1 1/4-acre residential lot Bluegrass 0.1 38.5 42.6 7.1 2.8 0.03

6 Commons park Bluegrass 0.5 25.1 47.1 7.1 0.8 0.03

8 1/4-acre residential lot Bluegrass 0.1 26.6 32.2 7.1 0.8 0.00

9 5-acre estate lot Bluegrass 1.1 40.1 43.1 7.0 3.1 0.29

10 5-acre estate lot Native plants 0.6 4.2 12.1 7.0 0.0 0.00

11 5-acre estate lot Bluegrass 0.1 37.0 38.4 7.0 5.2 0.06

12 1/4-acre residential lot Bluegrass 0.1 31.0 38.1 7.2 0.2 0.00

17 5-acre estate lot Orchard grass 1.7 29.8 34.6 7.0 1.3 0.19

18 5-acre estate lot Bluegrass 0.8 43.1 44.0 7.0 5.4 0.37

21 1/4-acre residential lot Bluegrass 0.1 28.9 35.4 6.8 0.2 0.00

Mean 0.5 30.4 36.8 7.0 2.0 0.10

this is that precipitation runoff was understated for this wet 

period, leading to calculated soil-moisture balances that were 

artificially high.

Figure 14 is the 2006 soil-moisture balance chart for 

site 18, a 5-acre estate. The vegetation is bluegrass. Cumulative 

deep percolation for the season was 5.4 inches (table 9). This 

homeowner applies approximately 0.3 inch of water nearly 

daily, resulting in a soil-moisture balance that stays well above 

the ET stress point after May. The soil-moisture balance exceeds 

field capacity frequently during July, August, September, and 

October, creating deep percolation on many days. The largest 

daily deep percolation occurrence coincided with the heavy 
22 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Figure 10. Soil-moisture balance for bluegrass on 1/4-acre residential lot site 2, Grand Valley, western Colorado.

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.54

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24

July 1July 8

July 15July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16

Sept 23Sept 30

Oct 7

Oct 14Oct 21

INCHES OF WATER

–2

–1

0

1

2

3

4

5

6

3.58

4.54

Site 2

Paradise Hills

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.01

Apr 15

Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10

June 17June 24

July 1July 8

July 15July 22July 29

Aug 5

Aug 12

Aug 19

Aug 26

Sept 2Sept 9

Sept 16Sept 23

Sept 30

Oct 7

Oct 14

Oct 21

Oct 28

2005

INCHES OF WATER

–1

0

1

2

3

4

5

6

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Site 9

Quail Run

4.01

4.62

Figure 11. Soil-moisture balance for bluegrass on 5-acre estate site 9, Grand Valley, western Colorado.
Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation of Irrigation Water 23

Figure 12. Soil-moisture balance for bluegrass on subdivision commons park site 6, Grand Valley, 

western Colorado.

Figure 13. Soil-moisture balance for bluegrass on 1/4-acre residential site 8, Grand Valley, western 

Colorado.

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.9

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24

July 1July 8

July 15July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23Sept 30

Oct 7

Oct 14Oct 21Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

3.36

2.9

Site 6

Chipeta Pines

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.76

2006

Apr 3

Apr 10Apr 17Apr 24

May 1May 8

May 15May 22May 29

June 5

June 12

June 19

June 26

July 3

July 10

July 17

July 24July 31 Aug 7

Aug 14Aug 21Aug 28

Sept 4

Sept 11

Sept 18

Sept 25

Oct 2Oct 9

Oct 16

Oct 23

Oct 30

Nov 6

Nov 13

INCHES OF WATER

–1

0

1

2

3

4

5

6

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE 2.76

3.76

2.83

3.37 3.47

3.78

Site 8

Chipeta Pines
24 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

precipitation the first week of October. There is a generally 

good fit of the calculated soil-moisture balance to the measured 

soil-moisture core sample values, with the exception of the early 

August soil sample, which is above field capacity. Because deep 

percolation had been occurring often for several weeks prior to 

that date, it is quite possible that the soil was saturated on the 

sampling day. Soil moisture of this magnitude is feasible on a 

temporary basis if the soil is saturated and has not returned to 

the equilibrium level of field capacity.

Figure 15 is the 2006 soil-moisture balance chart for 

site 10, a 5-acre estate. The vegetation is native plants. There 

was no deep percolation during the season. Irrigation application of about 0.2 inch occurred approximately weekly, resulting in no deep percolation. It should be noted that the PWP for 

this site was set to 0.50 inch to obtain the best visual fit of the 

calculated soil-moisture balance curve to the measured soilmoisture core sample values. The justification for this change 

in permanent wilting point is that native plants have deeper 

root systems and desert-adapted foliage and therefore are 

more tolerant of low soil moisture at the 0–12 inch depth zone 

sampled in this study than bluegrass.

Figure 16 is the 2006 soil-moisture balance chart for 

site 17, a 5-acre estate. The vegetation is orchard grass. Cumulative deep percolation for the season was 1.3 inches. Irrigation 

application of about 0.4 inch occurred approximately every 

3 to 4 days until early June, resulting in no deep percolation. 

Then starting in June, daily irrigation was scheduled, with 

smaller amounts on several consecutive days punctuated with 

a larger amount (about 0.5 inch) every few days, again with no 

deep percolation occurring. The soil-moisture balance stays 

well under the ET stress point until the fall rains begin. The 

first deep percolation of the season occurred in the first week 

of October during several precipitation events that pushed soil 

moisture beyond field capacity. It should be noted that the 

PWP was lowered to 1.0 inch for this site. There is a generally 

good fit of the calculated soil-moisture balance to the measured soil-moisture core sample values, with the exception of 

the samples collected in September, October, and November. 

At those times, the calculated soil moisture was generally 

greater than the actual soil moisture, likely due to the periodic 

precipitation that fell starting in September and continuing 

through October. The most probable explanation for this difference is either: (1) precipitation runoff was understated for 

this wet period, leading to calculated soil-moisture balances 

that were artificially high; or (2) actual ET was greater than 

calculated during this period.

Urban Gated-Pipe Field Results

Figure 17 is the 2006 soil-moisture balance chart for 

site 15, a privately owned gated-pipe field. The vegetation is 

orchard grass. The irrigation events occurred about every 2 or 

3 weeks. Each of the seven irrigation events applied water in 

sufficient quantity to result in a total of 12.0 inches of deep 

percolation (table 10).

At the two privately owned fields (sites 15 and 16) during 

the 2006 season, there were seven and eight irrigation events, 

respectively. At the CSU-operated field (sites 7W and 7E), there 

were five pairs of irrigation events for the 2006 season. The 

results for gated-pipe fields are shown in table 10. Sites 7W and 

Figure 14. Soil-moisture balance for bluegrass on 5-acre estate site 18, Grand Valley, western Colorado.

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.77

2006

Apr 1 Apr 8

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24

July 1July 8

July 15July 22July 29 Aug 5

Aug 12Aug 19Aug 26

Sept 2 Sept 9

Sept 16Sept 23Sept 30

Oct 7

Oct 14Oct 21Oct 28

Nov 4

Nov 11 Nov 18Nov 25

INCHES OF WATER

–1

0

1

2

3

4

5

6

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

3.47

3.77

4.58

5.20

3.85

4.52 4.08

Site 18

Quail Run
Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation of Irrigation Water 25

Figure 15. Soil-moisture balance for native plants on 5-acre estate site 10, Grand Valley, western 

Colorado.

Figure 16. Soil-moisture balance for orchard grass on 5-acre estate site 17, Grand Valley, western 

Colorado.

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

1.13

2006

Apr 1Apr 8

Apr 15Apr 22Apr 29

May 6

May 13

May 20May 27

June 3

June 10June 17June 24 July 1

July 8

July 15July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23Sept 30

Oct 7

Oct 14

Oct 21Oct 28

Nov 4

Nov 11

INCHES OF WATER

–1

0

1

2

3

4

5

6

PERMANENT

WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE

BALANCE

1.37

0.82 0.80

1.13

2.76

2.47

2.57

Site 10

Quail Run

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.73

2006

Apr 8

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24 July 1

July 8

July 15 July 22July 29 Aug 5

Aug 12Aug 19Aug 26 Sept 2 Sept 9

Sept 16Sept 23

Sept 30 Oct 7

Oct 14

Oct 21

Oct 28 Nov 4

Nov 11Nov 18Nov 25

Apr 1 INCHES OF WATER

–1

0

1

2

3

4

5

6

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE

BALANCE

2.46

1.85 2.25

2.73

2.27

3.50

2.66

Site 17

Quail Run
26 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

7E had no cumulative deep percolation for the season, while 

sites 15 and 16 had 12.0 and 20.1 inches, respectively. The lack 

of deep percolation at sites 7W and 7E may be because the irrigation operator for the site is a professional agriculturist, whereas 

the owner/operator of sites 15 and 16 is not. The deep percolation 

results for sites 15 and 16 may be more indicative of urban gatedpipe fields that are operated as a hobby, although the sample sizes 

in this study are too small for statistical significance to be inferred 

from these results.

Analysis of Residential Sites, Parks, 

and Gated-Pipe Fields

For data analysis purposes, irrigation-water application 

and deep percolation results were combined in two ways: 

(1) bluegrass-only sites and (2) sites with all-vegetation types 

(bluegrass, orchard grass, and native plants). Bluegrass-only 

sites were separated for analysis because turf is the most common irrigated land cover associated with residential development; therefore, turf is the best measure of the effect of landuse conversion from agricultural to urban.

Bluegrass-Only Sites

Summary statistics for the irrigated areas of all bluegrass residential lots, estates, and urban parks are shown in 

table 11 for 19 irrigation seasons of data collected at 12 sites. 

Mean effective precipitation was 5.5 inches, the median was 

5.5 inches, and the standard deviation was 1.6. Mean ET was 

35.1 inches, median ET was 35.4 inches, and the standard 

deviation was 6.0. The mean value for irrigation water 

applied was 32.4 inches, while the median was 31.0 inches, 

with a standard deviation of 7.9. Mean deep percolation for 

Figure 17. Soil-moisture balance for orchard grass on gated-pipe field site 15, Grand Valley, western 

Colorado.

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.22

2006

Apr 1

Apr 8

Apr 15Apr 22Apr 29

May 6

May 13May 20

May 27

June 3

June 10June 17June 24 July 1

July 8

July 15

July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23Sept 30 Oct 7

Oct 14Oct 21

Oct 28 Nov 4

Nov 11

Nov 18

Nov 25

INCHES OF WATER

–4

–3

–2

–1

0

1

2

3

4

5

6

2.87

3.43

2.34 1.83

3.85 4.21

3.22

Site 15

Gated-pipe field

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED

EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Table 10. Summary of data and results of deep percolation calculations for 2006 irrigation season for gated-pipe fields.

[Net irrigation applied, irrigation applied multiplied by irrigation efficiency; stress corrected ET, evapotranspiration corrected for crop stress; effective precipitation, 

measured precipitation minus predicted runoff]

Site

number

Site

type Vegetation

Number of

irrigation

events

Irrigated

area

(acres)

Net irrigation

applied

(inches)

Stress

corrected ET

(inches)

Effective

precipitation

(inches)

Total deep

percolation

(inches)

Total deep

percolation

(acre-feet)

7W Gated-pipe field Orchard grass 5 1.0 9.9 15.7 5.8 0.0 0.00

7E Gated-pipe field Orchard grass 5 1.0 8.8 14.6 5.8 0.0 0.00

15 Gated-pipe field Orchard grass 7 1.0 26.4 21.5 7.0 12.0 0.95

16 Gated-pipe field Orchard grass 8 2.1 35.8 22.9 7.0 20.1 3.57

Mean 6.3 1.3 20.2 18.7 6.4 8.0 1.13
Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation of Irrigation Water 27

bluegrass irrigation was 2.8 inches. Median deep percolation was 1.4 inches, and the standard deviation was 3.7. 

Summary statistics by site type for bluegrass-only sites are tabulated in table 12. Figure 18 shows a dot chart of the same data 

for deep percolation in inches by site type. The mean seasonal 

deep percolation was 3.1 inches and 3.2 inches for 1/4-acre lots 

and 5-acre estates, respectively (table 12). Figure 19 shows a 

dot chart of irrigation-water application in inches by site type. 

The park may have been more closely managed to conserve 

water than the residential lots. Water application was much less, 

ranging from about 23 to 25 inches as compared to about 17 to 

45 inches for residential sites. In addition, deep percolation was 

low for the park (mean 0.4 inch).

Statistical Tests for Bluegrass-Only Sites

Two types of nonparametric statistical tests were used to 

determine if a study result could be explained by (1) another 

variable, or (2) by the groups into which the study result could be 

categorized. For example, the first type of test, Kendall’s Tau, can 

indicate whether the amount of net irrigation applied explains the 

deep percolation results at a site. The second type of test, KruskalWallis, can indicate whether the particular vegetation type at a site 

resulted in significantly different amounts of deep percolation. 

Kendall’s Tau test involves calculating the correlation between 

two continuous variables and indicates a significant correlation 

when the 2-tailed p-value is less than 0.05. Kruskal-Wallis Test 

measures whether the medians of discrete groups of variables are 

Table 11. Summary statistics for bluegrass-only vegetation on residential lots, estates, and urban parks.

[Effective precipitation, measured precipitation minus predicted runoff; evapotranspiration, evapotranspiration corrected for crop stress; irrigation 

applied, irrigation applied multiplied by irrigation efficiency]

Statistic Effective precipitation

(inches)

Evapotranspiration

(inches)

Irrigation applied

(inches)

Deep percolation

(inches)

Minimum 2.7 21.4 17.2 0.0

25th percentile 4.8 31.6 26.7 0.2

Median 5.5 35.4 31.0 1.4

Mean 5.5 35.1 32.4 2.8

75th percentile 7.0 39.6 39.1 3.3

Maximum 7.2 44.0 45.1 13.7

Standard deviation 1.6 6.0 7.9 3.7

Table 12. Summary statistics by site type for bluegrass-only vegetation on residential lots, estates, and urban parks.

[Total irrigation seasons, sum of all irrigation seasons for all sites studied; net irrigation applied, irrigation applied multiplied by irrigation efficiency; 

stress corrected ET, evapotranspiration corrected for crop stress; effective precipitation, measured precipitation minus predicted runoff]

Site type/

(total irrigation

seasons)

Statistic

Irrigated

area

(acres)

Net irrigation

applied

(inches)

Stress

corrected ET

(inches)

Effective

precipitation

(inches)

Deep

percolation

(inches)

1/4-acre residential lot (10) Minimum 0.05 17.2 21.4 2.7 0.0

25th percentile 0.05 26.9 31.3 5.0 0.2

Median 0.08 29.9 35.5 5.5 1.0

Mean 0.09 32.0 34.6 5.4 3.1

75th percentile 0.12 37.9 38.0 7.0 2.4

Maximum 0.14 45.1 42.6 7.2 13.7

Standard deviation 0.04 8.8 6.1 1.7 4.8

5-acre Estate (7) Minimum 0.14 26.7 32.1 4.5 0.2

25th percentile 0.48 30.2 33.1 4.9 2.6

Median 0.82 37.0 38.4 5.2 3.1

Mean 0.74 35.3 37.9 5.8 3.2

75th percentile 1.07 39.9 42.2 7.0 4.3

Maximum 1.12 43.1 44.0 7.0 5.4

Standard deviation 0.43 6.3 5.1 1.2 1.8

Park (2) Minimum 0.45 22.5 25.3 2.7 0.1

25th percentile 0.45 23.2 26.7 3.8 0.2

Median 0.45 23.8 28.1 4.9 0.4

Mean 0.45 23.8 28.1 4.9 0.4

75th percentile 0.45 24.5 29.5 6.0 0.6

Maximum 0.45 25.1 30.9 7.1 0.8

Standard deviation 0.00 1.8 3.9 3.1 0.5
28 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

different in a statistically significant manner. Kruskal-Wallis does 

not indicate which group is different, only that the group medians 

are significantly different when the p-value is less than 0.05, indicating that the null hypothesis (each group median is identical) 

fails (Helsel and Hirsch, 2002).

Because the data from the study are skewed with respect 

to a normal distribution, the two selected statistical tests are 

non-parametric, meaning that they are resistant to the effect of 

non-normally distributed data. In both of these tests, the data 

are ranked from lowest to highest, and the distribution of the 

ranks is tested rather than the data values themselves. (Helsel 

and Hirsch, 2002).

For bluegrass-only sites, Kruskal-Wallis tests were 

performed on deep percolation results in relation to site type 

and study year and on effective precipitation in relation to 

study year. The results are listed in table 13. Data groups 

were considered to have different distributions (or medians) if the p-value was less than 0.05. Results indicate that 

deep percolation was not statistically different in each study 

year (p-value = 0.6797); deep percolation is not statistically 

different for each site type (p-value = 0.1573); and effective precipitation is statistically different in each study year 

(p-value = 0.0003).

Kendall’s Tau correlation tests were performed on deep 

percolation in relation to several of the continuous variables 

(net irrigation applied, irrigated acres, effective precipitation, 

and ET) to measure the correlation between deep percolation 

and each variable (table 14). Additionally, ET was tested for 

correlation in relation to net irrigation applied. A strong correlation was considered to exist if the tau statistic was greater than 

0.60. Deep percolation is strongly correlated with net irrigation 

applied (Tau = 0.71). Deep percolation did not strongly correlate 

with irrigated acres, effective precipitation, or ET. The ET correlated strongly (Tau = 0.74) with net irrigation applied.

Of interest is whether deep percolation is related causally to precipitation events, as one might suspect from the 

soil-moisture balance graphs that indicated deep percolation is 

more common during rainy periods. For some sites (for example, site 8 data for early October in figure 13), deep percolation (red columns) seem to occur simultaneously with effective 

precipitation events (blue columns). However, the Kendall’s 

Tau test does not indicate a correlation, and a scatterplot of 

SITE TYPE

DEEP PERCOLATION, IN INCHES 0

6

8

10

14

16

2

12

4

1/4-acre

lots

5-acre

estates

Parks

EXPLANATION

Sample size

Data value greater than 3.0 times the

 interquartile range outside the box

Data value 1.5 to 3.0 times the interquartile

range outside the box

Largest data value within 1.5 times the

 interquartile range above the box

75th percentile

Median (50th percentile)

25th percentile

Smallest data value within 1.5 times the

 interquartile range below the box

Data point shown when sample size is

 4 or less

Interquartile

range

(7)

(10) (7) (2)

Figure 18. Deep percolation by site type for bluegrass-only 

vegetation sites, Grand Valley, western Colorado.

SITE TYPE

1/4-acre

lots

5-acre

estates

Parks

IRRIGATION WATER APPLICATION, IN INCHES 0

5

10

15

20

25

30

35

40

45

50

EXPLANATION

Sample size

Data point

Median (50th percentile)

(2)

Figure 19. Irrigation-water application by site type for 

bluegrass-only vegetation sites, Grand Valley, western Colorado.
Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation of Irrigation Water 29

Table 13. Kruskal-Wallis correlation test results for bluegrassonly vegetation on residential lots, estates, and urban parks.

[Study year, 2005 or 2006; site type, residential lots, estates, and parks; effective 

precipitation, measured precipitation minus predicted runoff; Kruskal-Wallis 

statistic, test statistic for correlation; p-value, test is significant at the 95 percent 

level if less than (<) 0.05]

Independent

variable

Dependent

variable

Number of

samples

KruskalWallis

statistic

p-value

Study year Deep percolation 19 0.17 0.6797

Site type Deep percolation 19 3.70 0.1573

Study year Effective precipitation 19 13.39 0.0003

Table 14. Kendall’s Tau correlation test results for bluegrassonly vegetation on residential lots, estates, and urban parks.

[Net irrigation applied, irrigation applied multiplied by irrigation efficiency; 

irrigated acres, acres under irrigation; effective precipitation, measured precipitation minus predicted runoff; evapotranspiration, evapotranspiration corrected 

for crop stress; Kendall’s Tau statistic, rank correlation value; 2-tailed p-value, 

significant at the 95 percent level if less than (<) 0.05]

Independent

variable

Dependent

variable

Number of

samples

Kendall’s

Tau

statistic

2-tailed

p-value

Net irrigation

applied

Deep percolation 19 0.71 <0.0001

Irrigated acres Deep percolation 19 0.27 0.1060

Effective

precipitation

Deep percolation 19 0.11 0.5250

Evapotranspiration Deep percolation 19 0.50 0.0029

Net irrigation

applied

Evapotranspiration 19 0.74 <0.0001

deep percolation in relation to effective precipitation (fig. 20), 

shows no obvious correlation. Clearly, when the soil-moisture 

balance is already high due to regular irrigation, a substantial 

input of additional water from precipitation could put the 

soil-moisture balance above field capacity, resulting in deep 

percolation. For this data set, however, the physical mechanism of precipitation contributing to deep percolation cannot 

be verified statistically.

All-Vegetation Sites

Summary statistics for effective precipitation, evapotranspiration, irrigation applied, and deep percolation from 26 irrigation 

seasons of data for residential lots, estates, urban parks, and gatedpipe fields are provided in table 15. The mean effective precipitation was 5.7 inches, the median was 5.8 inches, and the standard 

deviation was 1.5 inches. The mean evapotranspiration was 

30.8 inches, the median was 32.4 inches, and the standard deviation was 9.7 inches. The mean irrigation applied was 28.3 inches, 

while the median was 28.9 inches, with a standard deviation of 

11.6. Deep percolation ranges from 0 to 20.1 inches; mean deep 

percolation was 3.4 inches and the median was 1.3 inches, with a 

standard deviation of 5.1 inches.

1

Effective precipitation is measured precipitation minus predicted runoff

0

2

4

6

8

10

12

14

16

DEEP PERCOLATION, IN INCHES

0 1 2 3 4 5 6 7 8

EFFECTIVE PRECIPITATION,1 IN INCHES

Figure 20. Deep percolation relative to effective precipitation for 

bluegrass-only vegetation sites, Grand Valley, western Colorado.

Summary statistics are tabulated by site type in table 16. 

Figure 21 shows a dot chart of deep percolation grouped by 

site type. Figure 22 shows a dot chart of irrigation-water application plotted by site type.

Statistical Tests for All-Vegetation Sites

Kruskal-Wallis tests were performed on deep percolation in relation to several categorical variables (study year, 

vegetation type, and site type). A Kruskal-Wallis test also was 

performed for effective precipitation in relation to study year 

(table 17). Results indicate effective precipitation is different 

each year with a high statistical significance (p-value < 0.0001). 

Otherwise, deep percolation is not statistically different related 

to study year, vegetation type, or site type.

Kendall’s Tau correlation tests were performed for deep 

percolation in relation to several of the continuous variables 

(net irrigation applied, irrigated acres, effective precipitation, and ET). In addition, ET was tested for correlation 

with net irrigation applied (table 18). As would be expected, 

deep percolation correlated with net irrigation applied 

(p < 0.0001). In addition, ET correlated with net irrigation 

applied (p < 0.0001). Deep percolation correlates with ET 

(p = 0.0011) but does not correlate with irrigated acres or 

effective precipitation.

Whole Subdivision Deep Percolation 

and Irrigation-Water Application

To better understand the effects of agricultural land conversion to urban land on deep percolation in the Grand Valley, deep 

percolation and irrigation water applied were estimated (using 

aerial photographs) for all of the irrigated turf acres within 

the three subdivisions based on the bluegrass-site results and 

based on the all-vegetation type site (bluegrass, orchard grass, 

and native plant) results. Two of the subdivisions consisted of 

1/4-acre residential lots, and the third subdivision consisted of 
30 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

5-acre estates. All five sites in the two 1/4-acre lot subdivisions 

had bluegrass. Four of the six sites in the 5-acre estate subdivision had bluegrass, one site had orchard grass, and one site had 

native plants. Gated-pipe sites were not included in the whole 

subdivision analysis. A summary of deep percolation estimates 

for bluegrass-only and all-vegetation type sites is provided in 

table 19.

To compare results for deep percolation, irrigationwater application, and salt loading between agricultural and 

urban sites, irrigated land areas must be equivalent. This 

requires taking into account the percentage of a total subdivision development that is irrigated land. The column “Total 

Developed Acreage of Subdivision” in table 19 is the gross 

land area of the subdivision, including impervious and other 

Table 15. Summary statistics for all-vegetation types on residential lots, estates, urban parks, and 

gated-pipe sites.

[Effective precipitation, measured precipitation minus predicted runoff; evapotranspiration, evapotranspiration corrected 

for crop stress; irrigation applied, irrigation applied multiplied by irrigation efficiency]

Statistic Effective precipitation

(inches)

Evapotranspiration

(inches)

Irrigation applied

(inches)

Deep percolation

(inches)

Minimum 2.7 12.1 4.2 0.0

25th percentile 5.0 23.5 25.2 0.1

Median 5.8 32.4 28.9 1.3

Mean 5.7 30.8 28.3 3.4

75th percentile 7.0 38.0 36.8 3.4

Maximum 7.2 44.0 45.1 20.1

Standard deviation 1.5 9.7 11.6 5.1

Table 16. Summary statistics by site type for all-vegetation types on residential lots, estates, urban parks, and gated-pipe fields.

[Total irrigation seasons, sum of all irrigation seasons for all sites studied; net irrigation applied, irrigation applied multiplied by irrigation efficiency; stress 

corrected ET, evapotranspiration corrected for crop stress; effective precipitation, measured precipitation minus predicted runoff]

Site type/

(total irrigation

seasons)

Statistic

Irrigated

area

(acres)

Net irrigation

applied

(inches)

Stress

corrected ET

(inches)

Effective

precipitation

(inches)

Deep

percolation

(inches)

1/4-acre residential lot (10) Minimum 0.05 17.2 21.4 2.7 0.0

25th percentile 0.05 26.9 31.3 5.0 0.2

Median 0.08 29.9 35.5 5.5 1.0

Mean 0.09 32.0 34.6 5.4 3.1

75th percentile 0.12 37.9 38.0 7.0 2.4

Maximum 0.14 45.1 42.6 7.2 13.7

Standard deviation 0.04 8.8 6.1 1.7 4.8

5-acre estate (10) Minimum 0.14 4.2 12.1 4.5 0.0

25th percentile 0.56 27.3 32.2 4.7 0.5

Median 0.82 30.6 34.0 6.1 2.6

Mean 0.80 28.6 32.5 5.9 2.4

75th percentile 1.10 39.0 40.5 7.0 3.4

Maximum 1.70 43.1 44.0 7.0 5.4

Standard deviation 0.48 13.7 11.4 1.2 2.0

Park (2) Minimum 0.45 22.5 25.3 2.7 0.1

25th percentile 0.45 23.2 26.7 3.8 0.2

Median 0.45 23.8 28.1 4.9 0.4

Mean 0.45 23.8 28.1 4.9 0.4

75th percentile 0.45 24.5 29.5 6.0 0.6

Maximum 0.45 25.1 30.9 7.1 0.8

Standard deviation 0.00 1.8 3.9 3.1 0.5

Gated-pipe field (4) Minimum 0.95 8.8 14.6 5.8 0.0

25th percentile 0.98 9.6 15.4 5.8 0.0

Median 0.99 18.2 18.6 6.4 6.0

Mean 1.27 20.2 18.7 6.4 8.0

75th percentile 1.28 28.8 21.8 7.0 14.0

Maximum 2.13 35.8 22.9 7.0 20.1

Standard deviation 0.58 13.1 4.1 0.7 9.8
Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation of Irrigation Water 31

SITE TYPE

1/4-acre

lots

5-acre

estates

Parks Gated-pipe

fields

DEEP PERCOLATION, IN INCHES

0

5

10

15

20

25

EXPLANATION

Sample size

Data point

Median (50th percentile)

(2)

(10) (10) (2) (4)

Figure 21. Deep percolation by site type for all-vegetation type 

sites, Grand Valley, western Colorado.

Figure 22. Irrigation-water application by site type for allvegetation type sites, Grand Valley, western Colorado.

A summary of estimated irrigation water applied is 

provided in table 20 for bluegrass-only and all-vegetation 

type subdivision data. The mean irrigation water applied for 

the bluegrass-only sites was 33.55 inches during the irrigation season, and the mean irrigation water applied for the 

all-vegetation type sites was 30.96 inches. A mean seasontotal estimated rate of 2.80 acre-feet per irrigated acre and 

1.05 acre-feet per developed acre (including impervious areas) 

of irrigation water applied was calculated for bluegrass-only 

sites, and a mean estimated rate of 2.56 acre-feet per irrigated acre and 0.98 acre-foot per developed acre of irrigation 

water applied was calculated for all-vegetation type sites in 

the subdivisions.

Irrigation-Water Holding-Pond Seepage Results 

Seepage results for the four irrigation seasons of irrigationwater holding-pond data are provided in table 21. The mean 

seepage for the four ponds during the observation period was 

16.29 inches, after adjusted pan evaporation and effective precipitation were accounted for. This equated to a mean seepage 

of water from the four irrigation seasons of 0.83 acre-foot during the 22- to 25-day period of observation.

Projected over an entire 214-day irrigation season, a 

mean seepage of 143.30 inches, or a rate of 11.94 acre-feet 

per acre of pond surface area, would be anticipated from 

these ponds. This is a small sample size with data collected 

over a short time period; therefore, the results are not conclusive. However, the amount of seepage from the 1.086-acre 

nonirrigated surfaces. It was estimated that for the three 

subdivisions, 37 percent (mean) of the developed acreage 

was irrigated (table 19). The remaining acreage was covered 

with nonirrigated or impermeable surfaces such as streets, 

houses, alleys, and sidewalks. The percentage of irrigated 

acreage ranged from 29 to 51 percent by subdivision. The 

measured results for urban study subdivisions are referred 

to as the values for “irrigated acreage,” while the calculated 

values that have been reduced to reflect the percent irrigated 

area are referred to as the values for “developed acreage.” 

For a subdivision, the “developed acreage” values for deep 

percolation, irrigation-water application, and salt loading are 

calculated by multiplying the “irrigated acreage” values by the 

percentage of irrigated area in that particular subdivision. It 

was assumed in this report that the NRCS M&E alfalfa-crop 

site acreages were 100 percent irrigated.

The mean deep percolation for study sites in the three 

subdivisions ranged from 2.46 to 5.12 inches, with an overall mean of 3.60 inches for bluegrass and 3.25 inches for 

all-vegetation type sites for the irrigation season. A mean 

season-total estimated deep percolation of 0.30 acre-foot per 

irrigated acre and 0.12 acre-foot per acre of developed area 

(including impervious surfaces) was calculated for bluegrassonly sites, and a mean season-total estimated deep percolation of 0.27 acre-foot per irrigated acre and 0.11 acre-foot per 

developed acre was calculated for all-vegetation type sites 

in the subdivisions. Essentially, inclusion of about 1 acre of 

the non-bluegrass irrigated area of Quail Run subdivision 

accounts for the slight difference between bluegrass-only 

and all-vegetation results.

SITE TYPE

1/4-acre

lots

5-acre

estates Parks Gated-pipe fields

IRRIGATION WATER APPLICATION, IN INCHES

0

5

10

15

20

25

30

35

40

45

50

EXPLANATION

Sample size

Data point

Median (50th percentile)

(2)

(10) (10) (2) (4)
32 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Paradise Hills pond is estimated to be 11.70 (10.77 × 1.086) 

and 9.99 acre-feet per season (9.20 × 1.086) in 2005 and 2006, 

respectively. By comparison, irrigation practices for the entire 

34.91 acres of irrigated bluegrass in the subdivision were 

estimated to generate about 15.1 acre-feet of deep percolation per season (table 19, 0.22 acre-foot per acre × 68.7 acres 

total developed area). A more rigorous approach to quantify 

seepage from perched ponds would be to measure continuously the inflow (from canal) and outflow (pumping) from 

the ponds over the irrigation season and correct for daily pan 

evaporation and precipitation. From this limited data set, it 

appears unlined ponds contribute far greater seasonal seepage, or deep percolation per unit area (mean 143.30 inches, 

table 21) than do the bluegrass urban sites (mean 2.8 inches, 

table 11) and gated-pipe fields (mean 8.0 inches, table 16) in 

the study. From these data, it seems that preventing seepage 

or deep percolation from ponds (by lining, for example) could 

yield large reductions in overall seepage and associated salt 

loading as land converts to residential uses from other uses 

such as agriculture.

Natural Resources Conservation Service 

Monitoring and Evaluation Deep Percolation 

and Irrigation-Water Application

The NRCS M&E alfalfa-crop data set contains 67 irrigation seasons of data. Figure 23 shows the frequency distribution of sites grouped by field-size category. Most sites were 

40 acres or less in size.

Thirteen irrigation methods were represented in the NRCS 

M&E dataset, although only eight are represented among the 

alfalfa-crop sites used for comparison in this study (fig. 24). For 

alfalfa-crop sites, the predominant irrigation methods were concrete ditch to siphon tubes (n = 27 irrigation seasons of data), 

pipeline to gated-pipe (n = 8 irrigation seasons of data), pipeline 

to gated surge (n = 11 irrigation seasons of data), and side roll 

sprinkler (n = 13 irrigation seasons of data).

The frequency distribution of irrigation-water application for alfalfa-crop sites is shown in figure 25. Every site 

applied between 11 and 70 inches per irrigation season, except 

1 site that applied 160 inches (not shown in figure 25 and not 

included for data analysis).

The frequency distribution of annual deep percolation 

for alfalfa-crop sites is shown in figure 26. Most sites had 

29 inches or less of deep percolation per irrigation season.

Summary statistics for the alfalfa-crop sites are shown 

in table 22. Mean irrigation water applied was 45.4 inches 

and ranged from 18.9 to 69.6 inches, which generated a mean 

seasonal deep percolation of 15.2 inches. Summary values for 

acre-feet per acre also are shown in table 22.

A boxplot of NRCS M&E alfalfa-crop irrigation seasontotal net (total inflow minus total outflow) irrigation-water 

application by irrigation method is shown in figure 27. The 

median quantity of water applied for the three most common 

irrigation methods was 49.2 inches for concrete ditch siphon 

tubes, 43.6 inches for pipeline to gated surge, and 37.1 inches 

for side roll sprinkler. A Kruskal-Wallis test of irrigationwater application in relation to irrigation method showed that 

irrigation-water application is statistically different for each 

type of irrigation method (p = 0.0161).

Boxplots of NRCS M&E alfalfa-crop irrigation seasontotal deep percolation by irrigation method is presented in 

figure 28. A Kruskal-Wallis test of deep percolation compared 

to irrigation method showed that deep percolation is statistically different for each type of irrigation method (p = 0.0083). 

Median deep percolation spanned a wide range from less than 

5 inches for the side roll sprinkler irrigation method to about 

22 inches for pipeline to gated-pipe irrigation method.

Boxplots of NRCS M&E alfalfa-crop irrigation seasontotal net (total inflow less total outflow) irrigation-water application rate by site acreage is shown in figure 29. A Kendall’s 

Tau correlation test of net irrigation-water application in relation to field size showed no strong correlation (tau = –0.07). 

Water application was fairly consistent among field-size 

categories with median values ranging from about 45 inches 

to just over 50 inches.

Table 17. Kruskal-Wallis test results for all-vegetation types on 

residential lots, estates, urban parks, and gated-pipe fields.

[Study year, 2005 or 2006; site type, residential lots, estates, parks, and gatedpipe fields; effective precipitation, measured precipitation minus predicted 

runoff; Kruskal-Wallis statistic, test statistic for correlation; p-value, test is 

significant at the 95 percent level if less than (<) 0.05]

Independent

variable

Dependent

variable

Number of

samples

KruskalWallis

statistic

p-value

Study year Deep percolation 26 0.07 0.7964

Vegetation type Deep percolation 26 4.13 0.1270

Site type Deep percolation 26 0.86 0.8349

Study year Effective precipitation 26 19.14 <0.0001

Table 18. Kendall’s Tau correlation test results for all-vegetation 

types on residential lots, estates, urban parks, and gated-pipe 

fields.

[Net irrigation applied, irrigation applied multiplied by irrigation efficiency; 

irrigated acres, acres under irrigation; effective precipitation, measured 

precipitation minus predicted runoff; evapotranspiration, evapotranspiration 

corrected for crop stress; Kendall’s Tau statistic, rank correlation value; 

2-tailed p-value, significant at the 95 percent level if less than (<) 0.05]

Independent

variable

Dependent

variable

Number of

samples

Kendall’s

Tau

statistic

2-tailed

p-value

Net irrigation 

applied

Deep percolation 26 0.67 <0.0001

Irrigated acres Deep percolation 26 0.14 0.3291

Effective 

precipitation

Deep percolation 26 0.13 0.3935

Evapotranspiration Deep percolation 26 0.46 0.0011

Net irrigation 

applied

Evapotranspiration 26 0.77 <0.0001
Table 19. Whole subdivision estimates of mean deep percolation for bluegrass-only sites and all-vegetation type sites. [Total developed acreage of subdivision, total acreage of subdivision including irrigated and nonirrigated portions; all-vegetation types, includes bluegrass, orchard grass, and n

Subdivision Site type Number of homes in subdivision Total developed acreage of subdivision Total irrigated acreage in subdivision Percent irrigated area in subdivision Bluegrass-only mean season-total deep percolation All-veg

season-to

Mean deep percolation for study sites1 (inches) Estimated deep percolation rate for total irrigated acreage2 (acre-feet per acre) Estimated deep percolation rate for total developed acreage3 (acre-feet per acre) Mean deep percolation for study sites1 (inches) Esti

perc

for to

(

p

Chipeta Pines 1/4-acre lots 63 21.14 6.03 29 2.46 0.21 0.06 42.46 

Paradise Hills 1/4-acre lots 157 68.72 34.91 51 5.12 0.43 0.22 45.12 Quail Run 5-acre estates 33 131.72 41.62 32 3.22 0.27 0.08 2.17 

Sums 253 221.58 82.56 Mean 84 73.86 27.52 37 3.60 0.30 0.12 3.25 

1Based on data collected at individual bluegrass-only or all-vegetation sites during 2005 and 2006 for each subdivision. 2Estimated deep percolation rate for total irrigated acreage = mean deep percolation for subdivision study sites divided by 12 inches per foot. 3Estimated deep percolation rate for total developed acreage = estimated deep percolation rate for total irrigated acreage multiplied by percent irrigated acreage in subdivision4All-vegetation values are the same as bluegrass-only because bluegrass was the only irrigated vegetation type included in the study for this subdivision.
Table 20. Whole subdivision estimates of mean irrigation water applied for bluegrass-only sites and all-vegetation type sites. [Total developed acreage of subdivision, total acreage of subdivision including irrigated and nonirrigated portions; all-vegetation types, includes bluegrass, orchard grass, and na

Subdivision Site type Number of homes in subdivision Total developed acreage in subdivision Total irrigated acreage for subdivision Percent irrigated acreage in subdivision Bluegrass-only vegetation season-total irrigation-water application All-veg

season-total ir

Mean irrigation- water application for study sites1 (inches) Estimated irrigation- water application rate for total irrigated acreage2 (acre-feet per acre) Estimated irrigation- water application rate for total developed acreage3 (acre-feet per acre) Mean irrigation- water application for study sites1 (inches) 

Chipeta Pines 1/4-acre lots 63 21.14 6.03 29 29.18 2.43 0.69 429.18 

Paradise Hills 1/4-acre lots 157 68.72 34.91 51 36.23 3.02 1.53 436.23 Quail Run 5-acre estates 33 131.72 41.62 32 35.26 2.94 0.93 26.83 

Sums 253 221.58 82.56 Mean 84 73.86 27.52 37 33.55 2.80 1.05 30.96 

1Based on data collected at individual bluegrass-only or all-vegetation sites during 2005 and 2006 for each subdivision. 2Estimated irrigation-water application rate for irrigated acreage = mean irrigation-water application for study sites divided by 12 inches per foot. 3Estimated irrigation-water application rate for total developed acreage = estimated irrigation-water application rate for total irrigated acreage multiplied by percent irrigated 4All-vegetation values are the same as bluegrass-only, because bluegrass was the only irrigated vegetation type included in the study for this subdivision. Table 21. Seepage results for irrigation-water holding ponds, 2005–2006. [Surface area of pond, water surface area at beginning of observations; adjusted pan evaporation, measured pan evaporation multiplied by mean annual pan coefficient of 0.70 fo

lake evaporation; seepage during test period, observed stage change minus total pan evaporation plus total daily precipitation; estimated deep percolation per season, seepage dunumber of days observed multiplied by number of days per irrigation season] Year Pond (site number) Surface area of pond (acres) Number of days observed Observed stage change (inches) Adjusted pan evaporation (inches) Total daily precipitation (inches) Seepage during test period (inches) Seepage during test period (acre-feet) Estimate

deep percolatio

per seaso

(inches

2005 Paradise Hills (26) 1.086 22 14.64 1.47 0.12 13.29 1.20 129.29 

2006 Paradise Hills (26) 1.086 25 14.40 1.70 0.19 12.89 1.17 110.36 

2006 Quail Run (27) 0.318 25 24.12 1.70 0.19 22.61 0.60 193.57 

2006 Chipeta Pines (28) 0.157 25 17.88 1.70 0.17 16.35 0.35 139.98 

Mean 0.662 17.76 1.64 0.17 16.29 0.83 143.30 

1Pond seepage is assumed to be equivalent to deep percolation for this analysis.2An irrigation season is approximately 214 days.
Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation of Irrigation Water 35

Figure 23. Frequency distribution of field sizes included in Natural Resources Conservation Service 

Monitoring and Evaluation (NRCS M&E) alfalfa-crop sites (1985–2002).

Figure 24. Frequency distribution of irrigation methods for 

Natural Resources Conservation Service Monitoring and 

Evaluation (NRCS M&E) alfalfa-crop sites (1985–2002).

Figure 25. Frequency distribution of irrigation-water application for Natural Resources 

Conservation Service Monitoring and Evaluation (NRCS M&E) alfalfa-crop sites (1985–2002).

0–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40 41–45 46–50 51–55 56–60

FIELD SIZE, IN ACRES 

0

2

4

6

8

10

12

14

NUMBER OF IRRIGATION SEASONS16

18

0–10 11–20 21–30 31–40 41–50 51–60 61–70 71–80

IRRIGATION WATER APPLICATION, IN INCHES

0

2

4

6

8

10

12

14

16

18

20

NUMBER OF IRRIGATION SEASONS

Ported

concrete ditch

Side roll

sprinkler

Ported

concrete surge

Pipeline to

gated surge

IRRIGATION METHOD

Pipeline to

gated pipe

Concrete ditch

to siphon tubes

Earth ditch

to siphon tubes

Gated pipe

(includes flextube)

NUMBER OF IRRIGATION SEASONS

0

5

10

15

20

25

30
36 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Boxplots of NRCS M&E alfalfa-crop site annual deep percolation by site acreage is shown in figure 30. A Kendall’s Tau 

correlation of deep percolation by site acreage showed no strong 

correlation (tau = –0.10). Deep percolation was somewhat more 

variable than water application by field-size category with medians ranging from less than 5 inches to greater than 20 inches.

Comparison of Results Between this Study 

and the Natural Resources Conservation Service 

Monitoring and Evaluation

There are two questions of interest for understanding the 

effect on salt loading from the conversion of agricultural land 

to urban land: (1) what quantity of irrigation water is applied 

to crops and turf grass, respectively, and (2) how much deep 

percolation is generated for a given acre of agricultural land 

compared to an acre of urban land in the Grand Valley? 

A related question is whether bluegrass, which is the 

predominant type of irrigated vegetation in the subdivisions 

in the Grand Valley, has a different pattern of irrigation-water 

application and deep percolation than does the mix of vegetation types found in the 5-acre estates.

DEEP PERCOLATION, IN INCHES

0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49

0

2

4

6

8

10

12

14

NUMBER OF IRRIGATION SEASONS

Figure 26. Frequency distribution of deep percolation for Natural Resources Conservation Service 

Monitoring and Evaluation (NRCS M&E) alfalfa-crop sites (1985–2002).

Table 22. Summary statistics for Natural Resources Conservation Service Monitoring and Evaluation (NRCS M&E) alfalfa-crop sites 

(1985–2002).

[Total irrigation seasons, sum of all irrigation seasons for all sites studied]

Total irrigation

seasons

Statistic

Irrigation water 

applied

(inches)

Irrigation water applied

(acre-feet per

irrigated acre)

Deep percolation

(inches)

Deep percolation

(acre-feet per

irrigated acre)

67 Minimum 18.9 1.58 0.0 0.00

67 25th percentile 36.6 3.05 6.1 0.50

67 Median 46.8 3.79 15.2 1.30

67 Mean 45.4 3.79 15.2 1.27

67 75th percentile 54.9 4.57 23.0 1.92

67 Maximum 69.6 5.80 39.2 3.27

67 Standard deviation 1.4 1.02 10.4 0.92

Deep Percolation and Irrigation-Water 

Application Rates

Results from this study indicated that 37 percent (mean) 

of the developed subdivision acreage was irrigated (table 19). 

The remainder either was covered with streets, houses, and 

sidewalks or was not intensively irrigated. By comparison, an 

estimated 2 to 3 percent of agricultural land is nonirrigated, 

although the NRCS M&E only included irrigated acreage in 

its monitoring (Frank Riggle, written commun., 2007). For 

this reason, in the current analysis the agricultural alfalfa-crop 

fields in the NRCS M&E were assumed to be 100 percent 

irrigated. Because this urban study measured water application 

and deep percolation on vegetated surfaces only and these are 

on average only 37 percent of a subdivision’s total developed 

area, a direct comparison between an acre of subdivision 

land and an acre of agricultural land requires application of 

a 37-percent correction factor for water application and deep 

percolation results for the residential subdivision sites.

Table 23 lists summary statistics for comparison of deep 

percolation between agricultural alfalfa-crop and urban bluegrassonly and all-vegetation type sites in subdivisions. The estimated 

mean deep percolation for the urban study bluegrass-only sites 
Effects of Conversion of Agricultural Land to Urban Land on Deep Percolation of Irrigation Water 37

Figure 27. Season-total irrigation-water application by irrigation 

method for Natural Resources Conservation Service Monitoring 

and Evaluation (NRCS M&E) alfalfa sites (1985–2002).

Ported

concrete ditch

Side roll

sprinkler

Ported

concrete surge

Pipeline to

gated surge

IRRIGATION METHOD

Pipeline to

gated pipe

Gated pipe

Concrete ditch

to siphon tubes

Earth ditch

to siphon tubes

IRRIGATION WATER APPLICATION, IN INCHES

0

20

40

60

80

EXPLANATION

Sample size

Data value greater than 3.0 times the

 interquartile range outside the box

Data value 1.5 to 3.0 times the interquartile

 range outside the box

Largest data value within 1.5 times the

 interquartile range above the box

75th percentile

Median (50th percentile)

25th percentile

Smallest data value within 1.5 times the

 interquartile range below the box

Data point shown when sample size is

 4 or less

Interquartile

range

(11)

(4) (27) (1) (1) (2) (8) (11) (13)

Ported

concrete ditch

Side roll

sprinkler

Ported

concrete surge

Pipeline to

gated surge

IRRIGATION METHOD

Pipeline to

gated pipe

Gated pipe

Concrete ditch

to siphon tubes

Earth ditch

to siphon tubes

DEEP PERCOLATION, IN INCHES

0

10

20

40

50

30

EXPLANATION

Sample size

Data value greater than 3.0 times the

 interquartile range outside the box

Data value 1.5 to 3.0 times the interquartile

 range outside the box

Largest data value within 1.5 times the

 interquartile range above the box

75th percentile

Median (50th percentile)

25th percentile

Smallest data value within 1.5 times the

 interquartile range below the box

Data point shown when sample size is

 4 or less

Interquartile

range

(11)

(4) (27) (1) (1) (2) (8) (11) (13)

Figure 28. Distribution of season-total deep percolation by irrigation 

method for Natural Resources Conservation Service Monitoring and 

Evaluation (NRCS M&E) alfalfa-crop sites (1985–2002).

is 0.12 acre-foot per developed acre, while the estimated mean 

deep percolation for all-vegetation type sites in subdivisions is 

slightly lower at 0.11 acre-foot per developed acre. The mean 

deep percolation for alfalfa-crop sites in the NRCS M&E is 

1.27 acre-feet per irrigated acre, which is 10.6 times greater than 

the mean deep percolation for bluegrass-only sites and 11.5 times 

greater than for all-vegetation types in the three subdivisions. For 

equivalent land areas, as land is converted from agricultural to 

urban residential uses, this represents a potential deep percolation reduction of 91 percent for all-vegetation type sites and a 

potential reduction of 91 percent for bluegrass-only sites. Worth 

noting, the NRCS M&E data represent 11 irrigation seasons of 

weather conditions, while the current study included only 2 years 

of irrigation seasons.

Table 24 shows summary statistics for comparison of 

irrigation-water application between agricultural alfalfa-crop 

and urban bluegrass and all-vegetation types. Again, the raw 

urban study irrigation-water application values are multiplied by 

37 percent for this comparison, so the results give acre-feet per 

developed acre. The mean irrigation-water application rate for 

bluegrass-only sites is 1.05 acre-feet per developed acre, while the 

mean irrigation-water application rate for all-vegetation type sites 

in subdivisions is 0.98 acre-foot per developed acre. The mean 

application rate for alfalfa-crop sites is 3.79 acre-feet per acre per 

irrigated acre, a ratio of 3.6 times more irrigation water applied 

for agriculture compared to bluegrass-only sites, and a ratio of 3.9 

times more irrigation water applied compared to the all-vegetation 

type sites in subdivisions. For equivalent land areas, compared 

with agricultural sites, this represents a reduction of 74 percent 

in irrigation-water application for all-vegetation type sites and a 

reduction of 72 percent for bluegrass-only sites in subdivisions.
38 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Figure 29. Season-total irrigation-water application by site 

acreage for Natural Resources Conservation Service Monitoring 

and Evaluation (NRCS M&E) alfalfa sites (1985–2002).

Figure 30. Distribution of season-total deep percolation by site 

acreage for Natural Resources Conservation Service Monitoring 

and Evaluation (NRCS M&E) alfalfa-crop sites (1985–2002).

FIELD SIZE, IN ACRES

1–45–9

10–1415–2021–2425–2930–34

35–39

40–4445–49

50–above

IRRIGATION WATER APPLICATION, IN INCHES

10

30

40

60

80

20

50

70

EXPLANATION

Sample size

Data value greater than 3.0 times the

 interquartile range outside the box

Data value 1.5 to 3.0 times the interquartile

 range outside the box

Largest data value within 1.5 times the

 interquartile range above the box

75th percentile

Median (50th percentile)

25th percentile

Smallest data value within 1.5 times the

 interquartile range below the box

Data point shown when sample size is

 4 or less

Interquartile

range

(7)

(2) (7) (12) (15) (0) (7) (2) (15) (0) (7)(0)

FIELD SIZE, IN ACRES

1–4

5–9

10–14

15–20

21–24

25–29

30–34

35–39

40–44

45–49

50–above

DEEP PERCOLATION, IN INCHES

0

20

40

50

10

30

EXPLANATION

Sample size

Data value greater than 3.0 times the

 interquartile range outside the box

Data value 1.5 to 3.0 times the interquartile

 range outside the box

Largest data value within 1.5 times the

 interquartile range above the box

75th percentile

Median (50th percentile)

25th percentile

Smallest data value within 1.5 times the

 interquartile range below the box

Data point shown when sample size is

 4 or less

Interquartile

range

(7)

(2) (7) (12) (15) (0) (7) (2) (15) (0) (7)(0)

It is the conclusion of this study that conversion of agricultural land to residential subdivisions in the Grand Valley of 

western Colorado decreases irrigation-water application and 

its associated deep percolation. This is true when comparing 

equivalent irrigated areas and even more so considering the 

subdivisions in this study only irrigated about 37 percent of 

the total land area as compared to 97–98 percent of the agricultural land (alfalfa) that is taken out of production.

Salt Loading

Relative salt loading for urban and agricultural land is 

listed in table 25. The mean salt loading rate to the Colorado 

River from bluegrass-only study sites is 0.25 ton per developed acre and 0.22 ton per developed acre for all-vegetation 

types, assuming that 37 percent of urban land is irrigated 

land. The slightly decreased mean for all-vegetation types is 

explained by the inclusion of native plant and orchard grass 

sites, which have lower deep percolation. Mean salt loading 

for NRCS M&E alfalfa-crop sites was 2.60 tons per acre. 

As a ratio, compared with alfalfa-crop sites, this represents a 

salt loading reduction factor of 10.4 for bluegrass-only sites 

and a reduction factor of 11.8 for all-vegetation type sites. 

Subdivision irrigation-water holding ponds were estimated to 

generate about 24.48 tons per surface acre of salt loading, a 

ratio increase over agricultural alfalfa-crop sites of 9.4.

Thus, the conversion of agricultural land to residential urban 

subdivisions results in substantially lower irrigation-water application, deep percolation, and salt loading per developed acre, with 

the exception of urban unlined ponds. Control of deep percolation from unlined ponds that are created to support residential 

irrigation could be an increasingly important factor to consider for 

minimizing irrigation-induced salt loading to the Colorado River.
Summary 39

Table 23. Deep percolation comparison statistics for urban 

study whole subdivision bluegrass-only sites, all-vegetation type 

sites, and Natural Resources Conservation Service Monitoring 

and Evaluation (NRCS M&E) alfalfa-crop sites.

Urban study estimated

deep percolation for total

developed acreage

(acre-feet per developed acre)

NRCS M&E deep 

percolation

(acre-feet per

irrigated acre)

Bluegrass-only

vegetation

All-vegetation 

types

Alfalfa-crop

sites

Minimum 0.06 0.06 0.00

Mean 0.12 0.11 1.27

Maximum 0.22 0.22 3.27

Table 24. Irrigation-water application comparison statistics for 

urban study whole subdivision bluegrass-only sites, all-vegetation 

type sites, and Natural Resources Conservation Service Monitoring 

and Evaluation (NRCS M&E) alfalfa-crop sites.

Urban study estimated

irrigation water application

for total developed acreage

 (acre-feet per developed acre)

NRCS M&E irrigationwater application

(acre-feet per

irrigated acre)

Bluegrass-only

vegetation

All-vegetation

types

Alfalfa-crop

sites

Minimum 0.69 0.69 1.58

Mean 1.05 0.98 3.79

Maximum 1.53 1.53 5.80

Table 25. Salt loading comparisons for urban study whole 

subdivision sites and Natural Resources Conservation Service 

Monitoring and Evaluation (NRCS M&E) sites.

[Mean salt loading, mean deep percolation multiplied by salt loading factor 

multiplied by standard return factor]

Category

Mean deep

percolation

(acre-feet per

developed 

acre)

Salt

loading

factor

 (tons per

acre-feet)1

Standard

return

factor2

Mean salt

loading

(tons per

developed

acre)

Urban bluegrassonly vegetation 

subdivisions

0.12 4.1 0.5 0.25

Urban all-vegetation 

type subdivisions

0.11 4.1 0.5 0.22

Subdivision 

irrigation-water 

holding ponds

11.94 4.1 0.5 24.48

NRCS M&E 

alfalfa-crop sites

1.27 4.1 0.5 2.60

1

Salt loading factor is the quantity of salt reaching the Colorado River in tons 

of salt per acre-foot of irrigation-induced deep percolation (Hedlund, 1994).

2

Standard Return Factor is a correction for deep percolation and seepage 

that is picked up and reused by lower lying irrigation systems, intercepted 

by deep-rooted plants such as shrubs and trees, used by phreatophytic plants, 

evaporated in irrigation-induced wetlands, or used by vegetation along the 

irrigation drains. The value of 0.50 is a standard determined administratively 

by NRCS for use in Utah, Colorado, and Wyoming (Frank Riggle, Natural 

Resources Conservation Service, oral commun., 2007).

Summary

The conversion of agricultural land to urban residential land 

is associated with rapid population growth in the Grand Valley of 

western Colorado. Information regarding the effects of this landuse conversion on deep percolation, irrigation-water application, 

and associated salt loading to the Colorado River is needed to 

support water-resource planning and conservation efforts.

The Natural Resources Conservation Service (NRCS) 

assessed deep percolation and estimated salt loading derived 

from irrigated agricultural lands in the Grand Valley in a 1985 

to 2002 monitoring and evaluation study (NRCS M&E). 

The U.S. Geological Survey (USGS), in cooperation with 

the Colorado River Salinity Control Forum and the Mesa 

Conservation District, quantified (the “urban study”) the current 

(2005–2006) deep percolation and irrigation-water application 

characteristics of 1/4-acre residential lots and 5-acre estates, 

urban parks, and urban orchard grass fields in the Grand Valley 

and compared the results to NRCS M&E results from alfalfacrop sites. In addition, pond seepage from three irrigation-water 

holding ponds was estimated. Salt loading was estimated for the 

urban study results and the NRCS M&E results using standard 

salt loading factors for the Grand Valley. A daily soil-moisture 

balance calculation technique was used at all urban study irrigated sites. Deep percolation was defined as any water infiltrating below the top 12 inches of soil. Deep percolation occurred 

when the soil-moisture balance in the first 12 inches of soil 

exceeded the field capacity for the soil type at each site.

Historically, agriculture in the Grand Valley has been 

provided with ample and inexpensive irrigation water, which is 

canal-delivered from the Colorado and Gunnison Rivers. Geologically, the Grand Valley is underlain by the Mancos Shale, 

which is a nonpoint source for salt and trace elements such as 

selenium. Deep percolation of irrigation waters in the Grand 

Valley leaches considerable salt and selenium from Mancos 

Shale-derived soils and Mancos Shale bedrock. Upstream from 

the irrigated area of the Grand Valley, selenium concentrations 

are generally less than 1 part per billion (ppb), while downstream from irrigated areas, selenium concentrations commonly exceed 4.6 ppb, the State of Colorado chronic standard 

for aquatic life.

The urban study quantified the 2005 and 2006 irrigationwater application and deep percolation characteristics at 

21 urban sites, yielding 30 irrigation seasons of data. The Grand 

Valley urban study sites consisted of seven residential lots, six 

5-acre estates, one urban park, four orchard grass fields (using 

gated-pipe flood irrigation), and three irrigation-water holding 

ponds. Twelve of the 18 residential sites had bluegrass, 5 had 

orchard grass, and 1 had native western Colorado plants. Results 

were reported separately for the bluegrass-only sites and for 

all-vegetation type urban study sites. Deep percolation, irrigation-water application, and salt loading also were estimated for 

a complete irrigation season at three of the subdivisions using 

mean site data from each subdivision.

Irrigated urban study sites were instrumented with digital 

data loggers to record the activity of sprinkler-timers, water 

pressure, and water-flow rate per irrigation zone. In addition, 
40 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

homeowners kept written logs of their irrigation activities. Precipitation was measured at each subdivision and at two Grand 

Valley locations and was corrected for storm runoff effects. 

Soil-moisture samples were collected periodically for determination of gravimetric soil-moisture content and soil bulk 

density. Orchard grass fields had water inflow measured with 

flow meters and outflow (tail-water) measured with broadcrested flumes. Irrigation-water holding ponds were equipped 

with USGS staff plates for water-level (stage) measurements.

 Daily evapotranspiration (ET) was calculated from 

local climate data using the dual crop-coefficient version 

of the Penman-Monteith equation, as described in the Food 

and Agriculture Organization (FAO) irrigation and drainage 

paper 56. The dual crop coefficients were corrected daily 

for crop stress. The NRCS M&E deep percolation and ET 

calculation methods were examined and were determined to 

be comparable to the urban study calculation methods for 

purposes of deep percolation comparison.

A daily soil-moisture balance calculation technique was 

created for each irrigated urban site that included moisture 

inputs (precipitation and irrigation-water application) and 

outputs (evapotranspiration, runoff, and deep percolation). 

Parameters in the soil-moisture balance model accounted for 

irrigation water runoff and overspray, irrigation efficiency, 

crop height, and soil physics (soil type, field capacity, and 

wilting point). Gravimetric soil-moisture sample values were 

correlated periodically with the calculated soil-moisture balance values. Whole subdivision irrigation-water application 

and deep percolation were estimated using mean values for 

the urban study sites in each subdivision and the total irrigated 

acreage for each subdivision.

An area-capacity curve was created for each irrigationwater holding pond by topographic survey. The area-capacity 

curves allowed conversion of pond stage to volume of pond 

water. Deep percolation (seepage) from irrigation-water holding ponds was calculated from pond-water volume changes 

and local pan evaporation after the end of the irrigation season.

Salt loading in the Colorado River was calculated for the 

urban study sites and the NRCS M&E alfalfa-crop sites using 

standard loading factors and standard average return-flow 

reductions for the Grand Valley. NRCS M&E alfalfa-crop sites 

were selected for comparison with the urban study-site results 

because alfalfa is a widely grown crop in the Grand Valley and 

commonly is the agricultural crop that is grown prior to urban 

residential land-use conversion. Alfalfa also represents a substantial portion of the NRCS M&E data set and is one of the 

standard reference crops for evapotranspiration calculations.

To compare results for deep percolation, irrigation-water 

application, and salt loading between agricultural and urban 

sites, the irrigated land areas must be equivalent. This requires 

taking into account the percentage of a total subdivision development that is irrigated land. It was estimated, using aerial 

photographs of three subdivisions, that 37 percent (mean) of 

the developed subdivision acreage was irrigated (the balance 

being nonirrigated or impermeable surfaces). The percentage 

irrigated acreage ranged from 29 to 51 percent by subdivision. 

The measured results for urban study subdivisions are referred 

to as the values for “irrigated acreage,” whereas the calculated 

values which have been reduced to reflect the percent irrigated 

area are referred to as the values for “developed acreage.” 

For a subdivision, the “developed acreage” values for deep 

percolation, irrigation-water application, and salt loading are 

calculated by multiplying the “irrigated acreage” values by the 

percentage of irrigated area in that particular subdivision. For 

the urban study, it was assumed that the NRCS M&E alfalfacrop site acreages were 100 percent irrigated.

The mean season-total deep percolation for bluegrassonly sites at the three residential subdivisions was 0.30 acrefoot per irrigated acre and 0.12 acre-foot per developed acre. 

The season-total deep percolation for all-vegetation types at 

the three subdivisions was 0.27 acre-foot per irrigated acre and 

0.11 acre-foot per developed acre. On the basis of a limited 

data set, mean season-total irrigation-water holding-pond deep 

percolation (seepage) was estimated to be 11.94 acre-feet per 

surface acre.

The mean season-total irrigation-water application for 

bluegrass-only sites in subdivisions was 2.80 acre-feet per 

irrigated acre and 1.05 acre-feet per developed acre. The mean 

season-total irrigation-water application for all-vegetation type 

sites at residential subdivisions was 2.56 acre-feet per irrigated 

acre and 0.98 acre-foot per developed acre.

Mean season-total deep percolation for alfalfa-crop sites 

in the NRCS M&E was 1.27 acre-feet per acre. For equivalent 

land areas, the conversion of land from agricultural to urban 

residential uses results in a potential deep percolation reduction of 91 percent for bluegrass-only sites and a potential 

reduction of 91 percent for all-vegetation type sites. Mean 

season-total irrigation-water application for alfalfa-crop sites 

in the NRCS M&E was 3.79 acre-feet per developed acre. For 

equivalent land areas, this represents a reduction of 72 percent 

in irrigation-water application for bluegrass-only sites compared with agricultural sites and a reduction of 74 percent for 

all-vegetation type sites.

Salt loading to the Colorado River for urban bluegrassonly sites was estimated to be 0.25 ton per developed acre and 

for all-vegetation type sites was estimated to be 0.22 ton per 

developed acre. Mean salt loading for NRCS M&E alfalfacrop sites was 2.60 tons per acre. As a ratio, compared with 

alfalfa-crop sites, this represents a salt-loading reduction factor 

of 10.4 for bluegrass-only sites, and a reduction factor of 11.8 

for all-vegetation type sites. Salt loading from ponds was estimated to be 24.48 tons per surface acre, a ratio increase over 

agricultural alfalfa-crop sites of 9.4.

Thus, the conversion of agricultural land to residential 

urban subdivisions results in substantially lower irrigationwater application, lower deep percolation, and less salt loading 

per developed acre, with the exception of urban unlined ponds. 

Control of deep percolation from unlined ponds that are 

created to support residential irrigation could be an increasingly important factor to consider for minimizing irrigationinduced salt loading to the Colorado River.
References Cited 41

References Cited

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

1998, Crop evapotranspiration—Guidelines for computing 

crop water requirements: Irrigation and drainage paper 56, 

Rome, Food and Agriculture Organization of the United 

Nations, 290 p.

Allen, R.G., 2001, REF-ET—Reference evapotranspiration calculation software, Version 2.0 for Windows: accessed April 11, 

2005, at URL http://www.kimberly.uidaho.edu/ref-et/

Anderson, J.M., and Mikhail, E.M., 1998, Surveying theory 

and practice (7th ed.): New York, WCB/McGraw-Hill, 

1166 p.

ASTM International, 2006, Standard test methods for laboratory determination of water (moisture) content of soil and 

rock by mass: West Conshohocken, Pennsylvania, ASTM, 

v. 04.08, D2216–05, 7 p.

Brady, N.C., and Weil, R.R., 2002, The nature and properties of soils (13th ed.): Upper Saddle River, New Jersey, 

Prentice-Hall, 959 p.

Buchleiter, G.W., Duke, H.R., and Heerman, D.F., 1992, 

USDA-ARS irrigation scheduling program SCHED, 

Version 2.01: Fort Collins, Colorado, USDA Agricultural 

Research Service, Colorado State University Agricultural 

Engineering Research Center.

Bureau of Reclamation, 1986, Colorado River Basin Salinity 

Control Project, Grand Valley Unit—Stage Two Development: Grand Junction, Colorado, six numbered sections.

Butler, D.L., Wright, W.G., Stewart, K.C., Osmundson, B.C., 

Krueger, R.P., and Crabtree, D.W., 1996, Detailed study 

of selenium and other constituents in water, bottom sediment, soil, alfalfa, and biota associated with irrigation 

drainage in the Uncompahgre Project area and in the Grand 

Valley, west-central Colorado, 1991–93: U.S. Geological 

Survey Water-Resources Investigations Report 96–4138, 

136 p.

Colorado State University, 2005–2007, CoAgMet Monthly 

Summary Data Access: accessed April 25, 2007, at URL 

http://ccc.atmos.colostate.edu/~coagmet/

Duke, H.R., ed., 1991, Scheduling irrigations—A guide for 

improved irrigation water management through proper 

timing and amount of water application: Denver, Colorado, 

U.S. Department of Agriculture, Soil Conservation Service, 

76 p.

Environmental Systems Research, Inc., 1999–2005, ArcMap, 

version 9.1: Redlands, Calif.

Hedlund, J.D., 1994, Salt primer—Water and salt budgets: 

Portland, Oregon, Soil Conservation Service, West National 

Technical Service Center, 60 p.

Helsel, D.R., and Hirsch, R.M., 2002, Statistical methods in 

water resources: U.S. Geological Survey Techniques of 

Water-Resources Investigations, book 4, chap. A3, 510 p.

Howell, T.A., 2003, Irrigation efficiency in Encyclopedia 

of Water Science, Stewart, B.A., Howell, T.A., eds.: New 

York, Marcel-Dekker, Inc., p. 467–472.

Leopold, R.P., 2003, Soil moisture measurement by feel and 

appearance in Encyclopedia of water science, Stewart, 

B.A., Howell, T.A., eds.: New York, Marcel-Dekker, Inc., 

p. 847–851.

Mesa County, Colo., 2005, Real property public information retrieval service: accessed May 10, 2007, at URL: 

http://assessor.mesacounty.us/parsearch.aspx

Ritter, M.E., 2006, The physical environment—An introduction to physical geography: accessed June 16, 2007, at URL 

http://www.uwsp.edu/geo/faculty/ritter/geog101/textbook/

hydrosphere/water_balance_1.html

U.S. Army Corps of Engineers, 2006, Hydrologic modeling system software HEC-HMS, version 3.1.0: accessed 

January 22, 2007, at URL http://www.hec.usace.army.mil/

software/hec-hms/

U.S. Census Bureau, 2007, State and metropolitan area

data book—2006: accessed April 25, 2007, at URL 

http://www.census.gov/prod/2006pubs/smadb/

smadb-06tableb.pdf

U.S. Department of Agriculture, 1986–2003, Grand Valley 

salinity program monitoring and evaluation annual reports: 

Grand Junction, Colorado, Soil Conservation Service, 

Grand Junction Area Office, variously paginated.

U.S. Department of Agriculture, 2007, National Cooperative 

Soil Survey (NCSS) web soil survey: accessed July 12, 

2006, at URL http://websoilsurvey.nrcs.usda.gov/app/

Veihmeyer, F.J., and Hendrickson, A.H., 1949, The application of some basic concepts of soil moisture to orchard 

irrigation: Proceedings of Washington State Horticultural 

Association, v. 45, p. 25–41.

Ward, A.D., and Trimble, S.W., 2004, Environmental hydrology (2d ed.): Boca Raton, Florida, Lewis Publishers, 475 p.

Wilson, G.L., and Richards, J.M., 2006, Procedural documentation and accuracy assessment of bathymetric maps and 

area/capacity tables for small reservoirs: U.S. Geological 

Survey Scientific Investigations Report 2006–5208, 24 p. 

plus oversize figures.

Appendixes
44 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Appendix 1. Season Water Balance Charts for All Sites

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.34

2005

Apr 15

Apr 22

Apr 29

May 6

May 13

May 20

May 27

June 3

June 10

June 17

June 24

July 1

July 8

July 15

July 22

July 29

Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30

Oct 7

Oct 14

Oct 21

Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.34 3.57

Site 1

Chipeta Pines

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.58

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24

July 1

July 8

July 15July 22July 29

Aug 5

Aug 12Aug 19

Aug 26

Sept 2Sept 9

Sept 16Sept 23Sept 30

Oct 7

Oct 14

Oct 21

INCHES OF WATER

–2

–1

0

1

2

3

4

5

6

3.58

4.54

Site 2

Paradise Hills

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A1. Soil-moisture balance for 2005 season, site 1, Grand Valley, western Colorado.

Figure A2. Soil-moisture balance for 2005 season, site 2, Grand Valley, western Colorado.
Appendix 1. Season Water Balance Charts for All Sites 45

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.90

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24

July 1July 8

July 15July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16

Sept 23

Sept 30

Oct 7

Oct 14Oct 21Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.36

2.90

Site 6

Chipeta Pines

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.91

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24

July 1July 8

July 15July 22July 29

Aug 5

Aug 12

Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23

Sept 30 Oct 7

Oct 14Oct 21

Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

2.91

3.66

Site 8

Chipeta Pines

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A3. Soil-moisture balance for 2005 season, site 6, Grand Valley, western Colorado.

Figure A4. Soil-moisture balance for 2005 season, site 8, Grand Valley, western Colorado.
46 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.01

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24 July 1

July 8

July 15

July 22

July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23Sept 30

Oct 7

Oct 14Oct 21Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

4.01

4.62

Site 9

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

1.78

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24 July 1

July 8

July 15July 22July 29

Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16Sept 23Sept 30

Oct 7

Oct 14Oct 21Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

1.78

2.43

Site 10

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A5. Soil-moisture balance for 2005 season, site 9, Grand Valley, western Colorado.

Figure A6. Soil-moisture balance for 2005 season, site 10, Grand Valley, western Colorado.
Appendix 1. Season Water Balance Charts for All Sites 47

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.58

2005

Apr 15

Apr 22

Apr 29

May 6

May 13

May 20

May 27

June 3

June 10

June 17

June 24

July 1

July 8

July 15

July 22

July 29

Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30

Oct 7

Oct 14

Oct 21

Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

4.45

4.58

Site 11

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.59

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24 July 1

July 8

July 15July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2

Sept 9

Sept 16Sept 23Sept 30 Oct 7

Oct 14Oct 21

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.59 3.53

Site 12

Grandview

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A7. Soil-moisture balance for 2005 season, site 11, Grand Valley, western Colorado.

Figure A8. Soil-moisture balance for 2005 season, site 12, Grand Valley, western Colorado.
48 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.48

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24 July 1

July 8

July 15July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23Sept 30 Oct 7

Oct 14Oct 21

INCHES OF WATER

–1

0

1

2

3

4

5

6

4.46 4.48

Site 13

Paradise Hills

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE

BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.27

2005

Apr 15

Apr 22

Apr 29

May 6

May 13

May 20

May 27

June 3

June 10

June 17

June 24

July 1

July 8

July 15

July 22

July 29

Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30

Oct 7

Oct 14

Oct 21

Oct 28

INCHES OF WATER

–2

–1

0

1

2

3

4

5

6

4.51

Site 14

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

4.27

Figure A9. Soil-moisture balance for 2005 season, site 13, Grand Valley, western Colorado.

Figure A10. Soil-moisture balance for 2005 season, site 14, Grand Valley, western Colorado.
Appendix 1. Season Water Balance Charts for All Sites 49

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.22

2005

Apr 15

Apr 22

Apr 29

May 6

May 13

May 20

May 27

June 3

June 10

June 17

June 24

July 1

July 8

July 15

July 22

July 29

Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30 Oct 7

Oct 14

Oct 21

Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

1.71

2.22

Site 17

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.13

2005

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24

July 1July 8

July 15

July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23Sept 30 Oct 7

Oct 14

Oct 21

Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

4.13

4.60

Site 18

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A11. Soil-moisture balance for 2005 season, site 17, Grand Valley, western Colorado.

Figure A12. Soil-moisture balance for 2005 season, site 18, Grand Valley, western Colorado.
50 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.74

2005

Apr 15

Apr 22

Apr 29

May 6

May 13

May 20

May 27

June 3

June 10

June 17

June 24 July 1

July 8

July 15

July 22

July 29

Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30

Oct 7

Oct 14

Oct 21

Oct 28

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.26

2.74

Site 22

Grandview

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.69

2006

Apr 3

Apr 10Apr 17Apr 24

May 1May 8

May 15May 22May 29

June 5

June 12June 19June 26 July 3

July 10

July 17

July 24

July 31 Aug 7

Aug 14Aug 21Aug 28

Sept 4

Sept 11Sept 18Sept 25

Oct 2

Oct 9

Oct 16

Oct 23

Oct 30

Nov 6

Nov 13

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.16

4.62

3.70

4.69 4.52

4.14

3.64

Site 1

Chipeta Pines

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A13. Soil-moisture balance for 2005 season, site 22, Grand Valley, western Colorado.

Figure A14. Soil-moisture balance for 2006 season, site 1, Grand Valley, western Colorado.
Appendix 1. Season Water Balance Charts for All Sites 51

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.49

2006

Apr 4

Apr 11

Apr 18

Apr 25

May 2

May 9

May 16

May 23

May 30

June 6

June 13

June 20

June 27 July 4

July 11

July 18

July 25 Aug 1

Aug 8

Aug 15

Aug 22

Aug 29

Sept 5

Sept 12

Sept 19

Sept 26 Oct 3

Oct 10

Oct 17

Oct 24

Oct 31

Nov 7

Nov 14

INCHES OF WATER

–1

0

1

2

3

4

5

6

2.93

3.49

3.90

3.40

3.63

2.68

Site 6

Chipeta Pines

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.17

2006

Apr 8

Apr 15

Apr 22 Apr 29

May 6

May 13

May 20

May 27 June 3

June 10

June 17

June 24 July 1

July 8

July 15

July 22

July 29 Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30 Oct 7

Oct 14

Oct 21

Oct 28 Nov 4

Nov 11

Nov 18

Nov 25

Apr 1 INCHES OF WATER

–2

–1

0

1

2

3

4

5

6

2.73

4.17

2.41

3.62

2.10

3.73

2.72

Site 7 West

Gated pipe

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A15. Soil-moisture balance for 2006 season, site 6, Grand Valley, western Colorado.

Figure A16. Soil-moisture balance for 2006 season, site 7 west, Grand Valley, western Colorado.
52 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.17

2006

Apr 1 Apr 8

Apr 15

Apr 22

Apr 29

May 6

May 13

May 20

May 27

June 3

June 10

June 17

June 24 July 1 July 8

July 15

July 22

July 29 Aug 5

Aug 12 Aug 19 Aug 26

Sept 2 Sept 9

Sept 16

Sept 23

Sept 30 Oct 7

Oct 14

Oct 21

Oct 28 Nov 4

Nov 11

Nov 18 Nov 25

INCHES OF WATER

–2

–1

0

1

2

3

4

5

6

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

2.73 SOIL MOISTURE BALANCE

4.17

2.41

3.62

2.10

3.73

2.72

Site 7 East

Gated pipe

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.78

2006

Apr 3

Apr 10Apr 17Apr 24

May 1May 8

May 15May 22May 29

June 5

June 12June 19June 26 July 3

July 10

July 17

July 24July 31 Aug 7

Aug 14Aug 21Aug 28

Sept 4

Sept 11Sept 18Sept 25 Oct 2

Oct 9

Oct 16

Oct 23Oct 30

Nov 6

Nov 13

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.76

2.83

3.37 3.47

3.78

2.76

Site 8

Chipeta Pines

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A17. Soil-moisture balance for 2006 season, site 7 east, Grand Valley, western Colorado.

Figure A18. Soil-moisture balance for 2006 season, site 8, Grand Valley, western Colorado.
Appendix 1. Season Water Balance Charts for All Sites 53

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.05

2006

Apr 1 Apr 8

Apr 15

Apr 22

Apr 29

May 6

May 13

May 20

May 27

June 3

June 10

June 17

June 24

July 1

July 8

July 15

July 22

July 29 Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30

Oct 7

Oct 14

Oct 21

Oct 28

Nov 4

Nov 11

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.20

4.26

4.67

4.05

3.88 4.26 4.03

Site 9

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

1.13

2006

Apr 1

Apr 8

Apr 15Apr 22Apr 29

May 6

May 13

May 20

May 27

June 3

June 10June 17June 24 July 1

July 8

July 15July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23

Sept 30 Oct 7

Oct 14

Oct 21

Oct 28

Nov 4

Nov 11

INCHES OF WATER

–1

0

1

2

3

4

5

6

1.37

0.82 0.80

1.13

2.76

2.47

2.57

Site 10

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE

BALANCE

Figure A19. Soil-moisture balance for 2006 season, site 9, Grand Valley, western Colorado.

Figure A20. Soil-moisture balance for 2006 season, site 10, Grand Valley, western Colorado.
54 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.28

2006

Apr 1Apr 8

Apr 15Apr 22Apr 29

May 6

May 13May 20May 27 June 3

June 10

June 17

June 24 July 1

July 8

July 15

July 22July 29

Aug 5

Aug 12Aug 19Aug 26

Sept 2Sept 9

Sept 16Sept 23Sept 30 Oct 7

Oct 14Oct 21Oct 28

Nov 4

Nov 11

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.24

4.28

3.80

2.72

3.47

4.09

3.52

Site 11

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.69

2006

Apr 1 Apr 8

Apr 15

Apr 22 Apr 29

May 6

May 13

May 20

May 27 June 3

June 10 June 17 June 24 July 1

July 8

July 15

July 22

July 29

Aug 5

Aug 12

Aug 19

Aug 26

Sept 2 Sept 9

Sept 16

Sept 23

Sept 30

Oct 7

Oct 14

Oct 21 Oct 28

Nov 4

Nov 11

Nov 18 Nov 25

INCHES OF WATER

–1

0

1

2

3

4

5

6

4.72

3.78 3.94

3.28

4.17 3.78

3.69

Site 12

Grandview

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A21. Soil-moisture balance for 2006 season, site 11, Grand Valley, western Colorado.

Figure A22. Soil-moisture balance for 2006 season, site 12, Grand Valley, western Colorado.
Appendix 1. Season Water Balance Charts for All Sites 55

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.34

2006

Apr 1

Apr 8

Apr 15

Apr 22

Apr 29 May 6

May 13

May 20

May 27

June 3

June 10

June 17

June 24 July 1 July 8

July 15

July 22

July 29 Aug 5

Aug 12

Aug 19

Aug 26 Sept 2

Sept 9

Sept 16

Sept 23

Sept 30 Oct 7

Oct 14 Oct 21

Oct 28

Nov 4

Nov 11

Nov 18

Nov 25

INCHES OF WATER

–4

–3

–2

–1

0

1

2

3

4

5

6

2.87

3.43

2.34 1.83

3.85 4.21

3.22

Site 15

Gated pipe

PERMANENT WILTING POINT

FIELD CAPACITY EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED

 EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

3.07

2006

Apr 1

Apr 8

Apr 15 Apr 22

Apr 29

May 6

May 13

May 20

May 27 June 3

June 10

June 17

June 24

July 1 July 8

July 15

July 22

July 29 Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30 Oct 7

Oct 14

Oct 21

Oct 28 Nov 4

Nov 11 Nov 18

Nov 25

INCHES OF WATER

–6

–4

–2

0

2

4

6

8

2.09

3.30

4.52

3.69

3.03 3.07 2.92

Site 16

Gated pipe

PERMANENT WILTING POINT

FIELD

CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED

EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A23. Soil-moisture balance for 2006 season, site 15, Grand Valley, western Colorado.

Figure A24. Soil-moisture balance for 2006 season, site 16, Grand Valley, western Colorado.
56 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

2.73

2006

Apr 1 Apr 8

Apr 15

Apr 22

Apr 29 May 6

May 13

May 20

May 27

June 3

June 10

June 17 June 24 July 1

July 8

July 15

July 22

July 29 Aug 5

Aug 12

Aug 19

Aug 26

Sept 2 Sept 9

Sept 16

Sept 23

Sept 30 Oct 7

Oct 14

Oct 21

Oct 28 Nov 4

Nov 11

Nov 18

Nov 25

INCHES OF WATER

–1

0

1

2

3

4

5

6

2.46

1.85 2.25

2.73

2.27

3.50

2.66

Site 17

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.07

2006

Apr 1

Apr 8

Apr 15

Apr 22

Apr 29

May 6

May 13

May 20

May 27 June 3

June 10

June 17

June 24

July 1

July 8

July 15

July 22

July 29 Aug 5

Aug 12

Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23

Sept 30

Oct 7

Oct 14

Oct 21

Oct 28

Nov 4

Nov 11

Nov 18

Nov 25

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.77

4.58

5.20

3.85

4.52

4.08

3.47

Site 18

Quail Run

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A25. Soil-moisture balance for 2006 season, site 17, Grand Valley, western Colorado.

Figure A26. Soil-moisture balance for 2006 season, site 18, Grand Valley, western Colorado.
Appendix 1. Season Water Balance Charts for All Sites 57

Effective precipitation—Measured

 precipitation minus predicted runoff

Net irrigation

Deep percolation

Sample soil moisture and value 

EXPLANATION

4.07

2006

Apr 1 Apr 8

Apr 15 Apr 22Apr 29

May 6

May 13May 20May 27

June 3

June 10June 17June 24

July 1

July 8

July 15

July 22July 29 Aug 5

Aug 12Aug 19

Aug 26

Sept 2

Sept 9

Sept 16

Sept 23Sept 30

Oct 7

Oct 14Oct 21Oct 28

Nov 4

Nov 11Nov 18Nov 25

INCHES OF WATER

–1

0

1

2

3

4

5

6

3.43

4.32 3.98

2.91

4.07

3.21

3.60

Site 21

Paradise Hills

PERMANENT WILTING POINT

FIELD CAPACITY

EVAPOTRANSPIRATION

STRESS POINT 

 STRESS CORRECTED EVAPOTRANSPIRATION

SOIL MOISTURE BALANCE

Figure A27. Soil-moisture balance for 2006 season, site 21, Grand Valley, western Colorado.
58 Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado

Appendix 2. Supplemental Data CD–ROM (in pocket, back of report)

The supplemental data CD–ROM contains a Microsoft® Office Excel 2003 spreadsheet with the following worksheets:

Worksheet tab name Worksheet title

Title Page Title Page

Daily Soil-Moisture Data Daily Soil-Moisture Balance Data for Urban Study Sites

2005 Site Weather Data 2005 Weather Data and Sources for Urban Study Sites

2006 Site Weather Data 2006 Weather Data and Sources for Urban Study Sites

Grand Junction Coagmet 2005 Grand Junction COAGMET Daily Weather Data for 2005

Grand Junction Coagmet 2006 Grand Junction COAGMET Daily Weather Data for 2006

Orchard Mesa Coagmet 2006 Orchard Mesa COAGMET Daily Weather Data for 2006

NRCS Study Data Natural Resources Conservation Service (NRCS) Monitoring & Evaluation (M&E) Data for All Agricultural Sites

Publishing support provided by:

Denver Publishing Service Center, Denver, Colorado

Manuscript approved for publication, May 16, 2008

Edited by Mary A. Kidd

Graphics and layout by Joy Monson

For more information concerning this publication, contact:

Director, USGS Colorado Water Science Center

Box 25046, MS 415

Denver, CO 80225

(303) 236-4882

Or visit the Colorado Water Science Center Web site at:

http://co.water.usgs.gov/

This publication is available online at:

http://pubs.usgs.gov/sir/2008/5086
Printed on recycled paper

J.W. Mayo—Estimating the Effects of Conversion of Agricultural Land to Urban Land in the Grand Valley, Western Colorado—USGS/SIR 2008–5086