evaluation of groundwater quality

EVALUATION OF GROUNDWATER QUALITY

IN CASTRO, DAWSON, AND TERRY COUNTIES

By

AJAY RAMACHANDRAN, B.Tech.

A THESIS

IN

CIVIL ENGINEERING

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfilhnent of the Requirements for

the Degree of

MASTER OF SCIENCE

IN

CIVIL ENGINEERING

Approved

Chairperson of the Committee

Accepted

Dean of the Graduate School

August, 2004

ACKNOWLEDGEMENTS

Extending gratitude after a work is completed can be a tricky business. One

cannot possibly name on paper each and everyone he/she is thankfiil to. Mere constrains

in space prevents me from thanking everyone. Therefore, this is by no means the full list.

First and foremost, I would like to thank Professor Ken Rainwater, my thesis

chair, for allowing me to work on this project and for his endless encouragement and

guidance. My sincere acknowledgements go to Dr. David Thompson for participating in

my thesis committee.

I also would like to thank Dr. Kevin MuUigan and Lucia Barbato, the charming

couple írom the Department of Economics and Geography at Texas Tech. They

introduced me into the world of GIS and helped me gain interest in this fîeld.

I extend my appreciation to Harvey Everheart and Jason Coleman, the managers

of Mesa Underground Water Conservation District and South Plains Undergroimd Water

Conservation District, respectively, for their help in data acquisition and for their answers

to my many queries.

My list would be incomplete if I do not mention Kavitha Pulugundla, my research

coUeague and good friend, for her afifable manner and constant support throughout the

project.

Needless to say, I am ever thankful to my family in India for their etemal

guidance and aid. Last but not least, I am indebted to my Maker and remain His obedient

servant.

n

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ii

LISTOFTABLES vii

LIST OF FIGURES viii

CHAPTER

L INTRODUCTION 1

1.1 Problem Statement 2

1.2 Objectives 3

II LITERATURE REVIEW 4

2.1 Study Counties 4

2.1.1 Castro County 4

2.1.1.1 General Description 4

2.1.1.2 History and Settlement 6

2.1.1.3 MainCrops 8

2.1.1.4 Soils 11

2.1.2 Dawson County 11


2.1.2.2 History 13

2.1.2.3 MainCrops 14

2.1.2.4 Soils 14

2.1.3 TerryCounty 19

ni


2.1.3.2 History 20

2.1.3.3 MainCrops 21

2.1.3.4 Soils 21

2.2 The Ogallala Aquifer 26

2.2.1 General Geology 26

2.2.2 Docknm Aquifer 28

2.3 Other Related Research 28

2.4 Geographic Information Systems 32

2.4.1 GISTools 33

III METHODS 34

3.1 Data Sources and Collection 34

3.1.1 Texas Water Development Board 34

3.1.2 LocalWaterDistricts 35

3.1.2.1 High Plains Underground Water Conservation District 35

3.1.2.2 Mesa Underground Water Conservation District 37

3.1.2.3 South Plains Underground Water Conservation District.... 40

3.1.3 Natural Resources Conservation Service 40

3.2DataCompilation 43

3.2.1 GIS Applications 43

3.2.2 Other Software Used 44

3.3 Map Generation 45

IV

3.3.1 Weli Location Display 45

3.3.2 Surface Interpolation 47

3.3.3 GenerationofContours 48

IV RESULTS 50

4.1 Water Quality Parameters 50

4.1.1 Physico-Chemical Parameters 50

4.1.2 Chemical Parameters 50

4.2 Water Quality Analysis 60

4.2.1 Domestic and Irrigation Applications 63

4.2.1.1 Castro County 63

4.2.1.2 Dawson County 79

4.2.1.3 Terry County 97

4.3LandUse 114

4.3.1 Land Use-Water Quality Correlation 122

4.3.1.1 Castro County 122

4.3.1.2 Dawson County 132

4.3.1.3 Terry County 142

4.4 Effect of Water Table Depths 159


4.4.2 Dawson County 168

4.4.3 Terry County 178

4.5 Comparison of Groundwater Quality across Counties 188

V CONCLUSIONS AND RECOMMENDATIONS 192

5.1 Conclusions 192


5.1.2 DawsonCounty 194

5.1.3 TerryCounty 196

5.2 Recommendations 197

REFERENCES 199

VI

LIST OF TABLES

2.1 Irrigated Acreage of Selected Crops 8

2.2 Selected Crop Acreage from 1990 to 2002, Castro County 9

2.3 Selected Crop Acreage from 1990 to 2002, Dawson County 16

2.4 Selected Crop Acreage from 1990 to 2002, Terry County 23

3.1 Wells and their Data Sources, Castro County 35

3.2 Keys to Tables 3.3 and 3.4 37

3.3 Wells and their Data Sources, Dawson County 39

3.4 Wells and their Data Sources, Terry Coimty 42

4.1 Water Quality Parameters, Castro County 51

4.2 Water Quality Parameters, Dawson County 52

4.3 Water Quality Parameters, Terry Coimty 55

4.4 Sodium Hazard of Water Based on SAR Values 59

4.5 RSC Values in Irrigation Water 60

4.6 Critical Values for Crops in Irrigation Water 62

4.7 Drinking Water and Irrigation Water Limits (mg/L except pH and % Na) 62

4.8 Comparison of Water Quality Parameters under Irrigated and Nonirrigated Areas. 123

4.9 Water Quality Values with Table Depths and Areas 178

4.10 Means from t-test for Select Parameters 190

Vll

LIST OF FIGURES

2.1 Counties inthe Llano Estacado Water Planning Region 5

2.2 Main Cities in Castro County 7

2.3 Crop Acreages, Castro Coimty 10

2.4 Castro County Soil Series 12

2.5 Main Cities in Dawson County 15

2.6 Crop Acreages, Dawson County 17

2.7 Dawson Coimty Soil Series 18

2.8 Main Cities in Terry County 22

2.9 Crop Acreages, Terry County 24

2.10 Terry County Soil Series 25

2.11 MapShowingExtentofOgallala Aquifer 27

3.1 Location of Wells in Castro County 36

3.2 Location of Wells in Dawson County 38

3.3 Location of Wells in Terry County 41

4.1 Alkalinity Levels, Castro Coimty 64

4.2 Nitrate Levels, Castro County 65

4.3 Fluoride Levels, Castro County 66

4.4 Chloride Levels, Castro County 67

4.5 TDS Levels, Castro County 68

4.6 RSC Levels, Castro County 69

vni

4.7 pH Levels, Castro County 70

4.8 Calcium Levels, Castro County 71

4.9 Magnesium Levels, Castro Coimty 72

4.10 Potassixmi Levels, Castro County 73

4.11 Sodium Levels, Castro County 74

4.12 Hardness Levels, Castro County 75

4.13 Percent Sodium Levels, Castro County 76

4.14 SAR Levels, Castro County 77

4.15 Sulfate Levels, Castro County 78

4.16 TDS Levels, Dawson County 80

4.17 Nitrate Levels, Dawson County 81

4.18 Fluoride Levels, Dawson County 82

4.19ChlorideLevels, DawsonCoimty 83

4.20 Sodium Levels, Dawson County 84

4.21 Percent Sodixmi Levels, Dawson County 85

4.22 SAR Levels, Dawson County 86

4.23 Alkalinity Levels, Dawson County 87

4.24 Hardness Levels, Dawson County 88

4.25 Iron Levels, Dawson County 89

4.26 Sulfate Levels, Dawson County 90

4.27 pH Levels, Dawson County 91

4.28 Calciimi Levels, Dawson Coimty 92

IX

4.29 Magnesium Levels, Dawson Coimty 93

4.30 Potassium Levels, Dawson County 94

4.31 TDS Levels, Terry County 98

4.32 TDS Levels >1000 mg/L, Terry County 99

4.33 Nitrate Levels, Terry County 100

4.34 Chloride Levels, Terry County 101

4.35 Chloride Levels >250 mg/L, Terry County 102

4.36 Fluoride Levels, Terry County 103

4.37 Sulfate Levels, Terry County 104

4.38 Sodium Levels, Terry County 105

4.39 Alkalinity Levels, Terry County 106

4.40 Percent Sodium Levels, Terry Coxmty 107

4.41 SAR Levels, Terry County 108

4.42 Calcium Levels, Terry County 109

4.43 Magnesium Levels, Terry Coxmty 110

4.44 Potassixmi Levels, Terry County 111

4.45 Hardness Levels, Terry County 112

4.46 Irrigated Acreage, Castro County 116

4.47 Irrigated Acreage, Dawson County 117

4.48 Irrigated Acreage, Terry County 118

4.49 Land Use Pattems in Castro County 119

4.50 Land Use Pattems in Dawson County 120

4.51 Land Use Pattems in Tôrry County 121

4.52 Castro Coimty, Irrigated vs. Nonirrigated Acreages 128

4.53 Alkalinity Levels >300 mg/L, Castro County 129

4.54 Fluoride Levels >4 mg/L, Castro County 130

4.55 RSC Levels >1.25, Castro County 131

4.56 Dawson County, Irrigated vs. Nonirrigated Acreages 133

4.57 Chloride Levels >250 mg/L, Dawson County 134

4.58 Nitrate Levels >44.3 mg/L, Dawson County 135

4.59 TDS Levels >500 mg/L, Dawson County 136

4.60 Fluoride Levels >4 mg/L, Dawson County 139

4.61 Sodium Levels >100 mg/L, Dawson County 140

4.62SARLevels>10,DawsonCounty 141

4.63 Hardness Levels >400 mg/L, Dawson Coimty 143

4.64 Calcium Levels >200 mg/L, Dawson County 144

4.65 Sulfate Levels >250 mg/L, Dawson County 145

4.66 Iron Levels >0.3 mg/L, Dawson County 146

4.67 Alkalinity Levels >300 mg/L, Dawson County 147

4.68 Magnesium Levels >150 mg/L, Dawson Coxmty 148

4.69 Percent Sodium Levels >60%, Dawson County 149

4.70 Terry Coimty, Irrigated vs. Nonirrigated Acreages 150

4.71 Chloride Levels >250 mg/L, Terry County 152

4.72 TDS Levels >500 mg/L, Terry County 153

XI

4.73 Sulfate Levels >250 mg/L, Terry County 154

4.74 Saline Lakes in Terry County, and High Chloride Levels 156

4.75 Saline Lakes in Terry County, and High TDS Levels 157

4.76 Nitrate Levels >44.3 mg/L, Terry County 158

4.77 Fluoride Levels >4 mg/L, Terry Coimty 160

4.78 Hardness Levels >400 mg/L, Terry County 161

4.79 Percent Sodium Levels >60%, Terry County 162

4.80 Alkalinity Levels >300 mg/L, Terry County 163

4.81 Water Table Depths, Castro County 165

4.82 Water Table Depths, Dawson County 166

4.83 Water Table Depths, Terry County 167

4.84 Chloride Levels >250 mg/L and Under 50 ft Depth, Dawson County 169

4.85 Chloride Levels >250 mg/L with Depth from 50 to 100 ft, Dawson.County 170

4.86 Chloride Levels >250 mg/L and Under 100 to 150 ft range, Dawson.County 171

4.87 TDS Levels >1000 mg/L and Under 50 ft Depth, Dawson County 172

4.88 TDS Levels >1000 mg/L with Depth írom 50 to 100 ft, Dawson.County 173

4.89 TDS Levels >1000 mg/L and Under 100 to 150 ft Range, Dawson.County 174

4.90 Nitrate Levels >44.3 mg/L and Under 0 to 50 ft Depth, Dawson_County 175

4.91 Nitrate Levels >44.3 mg/L and Under 50 to 100 ft Range, Dawson.County 176

4.92 Nitrate Levels > 44.3 mg/L and Under 100 to 150 ft Range, Dawson.County 177

4.93 Chloride Levels >250 mg/L with Depth Under 50 ft, Terry County 179

4.94 Chloride Levels >250 mg/L with Depth from 50 to 100 ft, Terry.County 180

xu

4.95 Chloride Levels >250 mg/L with Depth from 100 to 150 ft, Terry.County 181

4.96 TDS Levels >1000 mg/L with Depth Under 50 ft, Terry County 182

4.97 TDS Levels >1000 mg/L with Depth from 50 to 100 ft, Terry.County 183

4.98 TDS Levels >1000 mg/L with Depth from 100 to 150 ft, Terry.County 184

4.99 Nitrate Levels >44.3 mg/L with Depth from 0 to 50 ft, Terry.County 185



4.102 TrilinearDiagram forthe Study Counties 189

xni

CHAPTERI

INTRODUCTION

Groundwater is a valuable water resource due to its availability and quality. It is

available in large enough quantities to supply 50% of the United States population (EPA,

1985), Contamination of groundwater has occurred in every state and is being detected

with increasing frequency. Legislation, such as the Resource Conservation and Recovery

Act (RCRA) and the Comprehensive Environmental Response, Compensation, and

Liability Act (CERCLA), have concentrated on the vulnerability of groundwater

resources to contamination from hazardous materials. These acts have helped realize the

large expenses and complicated procedures needed to clean up contaminated

groimdwater. Prevention of groundwater contamination has thus become a high priority.

Engineers spend a large portion of their time analyzing and manipulating spatial

data in order to implement groundwater protection plans (Hickey and Wright, 1990),

These data exist in different forms, such as maps, reports, graphs, tables, and

computerized databases. Though the data are available, it is often diffîcult to understand

the spatial relationships between variables. Geographic information systems (GIS) have

proven to be eflfective for integrating and relating large amounts of different data types

obtained from different sources and compiled on different scales. GIS applications for

land use planning, natural resource management, site selection, census mapping, and

event mapping have established GIS as a feasible altemative to manual methods of

spatial analyses.

1.1 Problem Statement

To maintain a high quality of life, clean water is a primary requirement. The same

logic also can be extended to producing an adequate quantity of food and fîber. The

Southem High Plains of Texas has limited surface water resources and depends primarily

on the approximate 80,000 water wells in the region to support its demand of irrigated

agriculture, feed lots, and rural water supply (Wood, 2000). The Ogallala Aquifer is the

principal water source, accounting for as much as 98% of all water used in the Llano

Estacado region (Stovall et al., 2001). Researchers at the Texas Tech University Water

Resources Center (TTUWRC) were concemed with the groundwater quality of the

aquifer as it is applied to various purposes. This project aimed at investigating the efifects

of land use (irrigated or not) on water quality parameters. To obtain faster and more

flexible results than previous efforts, modem mapping tools, such as GIS, were

incorporated.

The plan for the project involved selection of target counties, coUection of water

quality data, development of GIS maps, and inclusion of statistical analysis to determine

the relationship between water quality parameters and land use, The time and fimding for

the project allowed for selection of three counties in the region—Castro, Dawson, and

Terry counties. Dawson and Terry counties were selected because a single water

conservation district exists for each coxmty, namely the Mesa Underground Water

Conservation District (MUWCD) in Dawson County and the South Plains Underground

Water Conservation District (SPUWCD) in Terry Coimty. Castro County was the third

county selected, and it is included in the High Plains Undergroimd Water Conservation

District (HPUWCD), which is Texas's fu-st groundwater management district. The mam

criteria in selecting the counties were that they were primarily dominated by irrigation

and had similar hydrologic data available.

The results from this project wiU be helpfiil to the Texas Water Development

Board (TWDB) and to the Llano Estacado Regional Water Planning Group (LERWPG)

because of their roles as water planning agencies. This project can be helpful to farmers

and other groundwater users in identifying areas containing waters suitable for irrigation

and/or domestic applications. The research also can aid in determining the positions and

placement of future wells for any purpose. The results can be of assistance to the

participating groundwater districts as they consider water quantity and quality issues.

1.2 Obiectives

The overall purpose of the project was to describe the groundwater quality

variations in Castro, Dawson, and Terry coxmties using modem mapping and

mathematical tools, The main objectives of the study were to:

• CoUect the existing water quality, land use, and depth to water data from the

three counties.

• Constmct GIS coverages for the coUected data.

• Determine if water quality differs between land uses and groundwater depth.

• Test the applicability of the groundwater for irrigation and drinking purposes.

GIS soflware was used to integrate data and demonstrate that GIS software is usefiil and

efficient for these purposes.

CHAPTERII

LITERATURE REVIEW

2.1 Studv Counties

2.1.1 CastroCountv

2.1,1.1 General Description

Castro County is located in the westem Panhandle region of the Texas High

Plains (Bruns, 1974). The county is bordered on the west by Parmer County, on the north

by Deaf Smith and Randall coimties, on the east by Swisher Covmty, and on the south by

Lamb and Hale counties (Figure 2.1). The coimty was named after Henri Castro, who

was consul general to Paris for the Republic of Texas. Castro Coimty was formed on

August 21, 1876, from Bexar District and organized in 1891. The population of the

county in 2000 was 8,285 (U.S. Census Bureau, 2001). Castro County is withm the High

Plains Underground Water Conservation District (HPUWCD) No. 1. The water district

was founded in 1951 and includes all or part of 15 counties.

The total land area of the county is approximately 898 square miles (U.S. Census

Bureau, 2001). The land surface elevation ranges from 3,500 feet above sea level to about

4,000 feet above sea level. The center of the county lies approximately at 34°32* north

latitude and 102* 20' west longitude. The average annual precipitation is around 17.7

inches (Abbe, 1996). Average minimum temperature is 22° F in the month of January

and the average maximum temperature is 93° F in July. About 500 playas are present in

the county. The Ogallala Formation in the coimty ranges from about 100 to 600 feet in

DEAF SMITH

PARMER

BAILEY

COCHRAN

YOAKUM

RANDALL

CASTRO SWISHER

LAMB

HOCKLEY

HALE

LUBBOCK

TERRY

GAÍNES

LYNN

DAWSON

BRISCOE

FLOYD

CROSBY

GARZA

MOTLEY

DICKENS

0 50100 200 300 400 . ^^

Counties under LERWPG

N

Figure 2.1. Counties in the Llano Estacado Water Planning Region.

thickness (TWDB, 1988). The saturated sand and gravel at the base of the formation,

known locally as the Ogallala Aquifer, serves as the source of groundwater for nearly all

irrigation in the county. The total water use was 503,792 acre-feet in 2000, all of which

was by groundwater supply (TWDB, 2001).

2.1.1.2 History and Settlement

The area known today as Castro County was the erstwhile hunting grounds of

Native Americans, such as the Comanches, Kiowas, and Apaches, who hunted bison,

deer and antelope (Bruns, 1974). When first organized, the entire county was open range.

The 1880s saw the coming of ranchers in the area. Homesteaders arrived in about 1898,

when State land was opened to settlement.

Extensive farming and ranching began in the early part of the twentieth century

(Bruns, 1974). The drought of the 1930s saw many farmers leave the coimty due to lack

of rainfall and low market prices. Irrigation from wells began in the early forties. Post

World War II saw the county's economy boom as irrigation water brought stability to the

local farming economy. By the 1980s the coimty was classifíed as one of Texas' most

productive agricultural coimties. The county boasts of several large vegetable-processing

plants, a fertilizer industry and many cotton gins. Most people in the county live in the

towns and communities, with Dimmitt the most populous city, Other towns in the coimty

include Nazareth, Hart, Summerfíeld, Easter, Flagg, and Sunnyside (Figure 2.2).

&imingrfigld. I g jumm

Flagg • ^385

0 2.5 5 10 15 20 Miles

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State Highway

Main Cities

City Limits

Figure 2.2. Main Cities In Castro County

2.1.1.3 MainCrops

Com and wheat and are the main crops grown in the county (USDA, 1999).

Irrigated cotton, sorghum, vegetables, soybeans, and sugar beets are also grown (Tables

2.1 and 2.2). Grain sorghum and wheat are also dryfarmed, with the majority of the

dryfarmed acreage in the northeastem part of the county. The total irrigated area was

299,220 acres in 2000 (TWDB, 2001). The irrigated acreage data were obtained fi-om two

sources. Table 2.1 shows data complied by the USDA, published m 1999. Table 2.2

contains information on irrigated and nonirrigated acreage for all years from 1990 to

2002, the source being the National Agricultural Statistics Service (NASS, 2003). Figures

2.3 (a) to (e) show the yearly irrigated and nonirrigated acreages for the main crops, in

Castro County. Com production in the coimty peaked to as high as 106,500 acres in 1994

and declined gradually after that. Irrigated cotton acreage was much higher than

nonirrigated cotton throughout the period. A similar trend was observed for sorghimi,

though the production was comparatively lesser than other crops. Irrigated wheat

production was more than nonirrigated in most years, with peak nonirrigated production

of 57,700 acres occurring in 1997.

Table 2.1, Irrigated acreage of selected crops (USDA, 1999).

Crop

Com

Cotton

Peanuts

Sorghum

Soybeans

Wheat

Hay-Alfalfa and others

Castro

1992

74,461

20,980

-

15,282

11,372

52,854

3,390

1997

84,736

50.946

-

12,740

2,765

53,605

4,363

Dawson

1992

-

21,186

365

7,274

-

-

17

1997

-

40,169

16,103

4,188

-

809

407

Terry

1992

-

58,172

2,121

41,695

1,035

3,605

883

1997

-

103,902

13,928

7,378

1,135

3,676

559

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10

2.1.1.4 Soils

A soil association is a landscape that has a distinctive proportional pattem of soils

(Bruns, 1974). It usually comprises one or more major soils and at least one minor soil,

and they are named for the major soils. There are six soil associations in Castro County,

namely PuUman association, Olton association, Estacado association, Estacado-Berda-

Bippus association, Acuff association, and Lipan-Estacado association (USDA, 1995).

The different associations are displayed in Figure 2.4.

The PuUman association makes up about 51 percent of the county (Bruns, 1974).

Most of the dryfarmed acreage is within this association. The OUon association accoimts

for about 31 percent of the coxmty. This association is mainly used for irrigated crops.

The Estacado association makes up approximately 8 percent of the coxmty, and most of

the association is in cuhivation. The Estacado-Berda-Bippus association makes up about

6 percent of the county. This association is mostly used for rangeland, with a few small

areas suited for irrigated cropland. The Acuff association comprises about 2 percent of

the county, with most of the association used for cuUivation. The Lipan-Estacado

association also accoimts for approximately 2 percent of the county. The association is

mostly used for rangeland.

2.1.2 Dawson Countv

2.1.2.1 General Description

Dawson County lies in the southem part of the High Plains, about sixty miles

south of Lubbock (Sanders, 1960). The center point of the county is located at 32°45*

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north iatitude and 101''57' west longitude (Figure 2.1). The square-shaped county

comprises 902 square miles. The land surface elevation ranges from about 2,600 to 3,200

feet above mean sea level (Gelin and Odintz, 1996). The average annual precipitation is

around 16.1 inches. The average minimum temperature is aroimd 28° F in January with

the maximum average in July being 94° F. The population of the county was 14,985 in

2000 (U.S. Census Bureau, 2001). Lamesa is the county seat, located in the center of the

county. The county was organized in 1905 and named forNicholas Dawson, w^o fought

in the Battle of San Jacinto.

The Mesa Underground Water Conservation District (MUWCD), formed in

January 1990, serves Dawson Coxmty (TAGD, 2003). The district measures water levels

in 187 wells annually for baseline comparison. Besides, the district also performs water

quality monitoring programs on 52 wells for baseline comparison.

2.1.2.2 History

Comanche Indians lived in the area now known as Dawson County tiU as late as

1870 (Sanders, 1960). The Indians' favorite campsites were along the caprock

escarpment, which had many fresh-water springs. Thousands of buffalo, antelope, and

quail roamed the area west of the escarpment. White himters of bufifalo came to the area

aroimd 1874. A large ranch was formed in 1878. This ranch along vdth three others

covered the entire county tili 1900.

The first decade of the twentieth century saw dramatic growth in the county

(Sanders, 1960). Population and farming activities began to increase steadily. Cotton

13

became the main crop. Irrigation was introduced in the county in the late I940s, The

county was second in terms of cotton production in the state in 1980 (Gelin and Odintz,

1996). The main tovms in the county include Lamesa, Ackerly, O'Donnell, and Los

Ybanez (Figure 2.5).

2.1.2.3 MainCrops

Data on irrigation and selected crops harvested are given in Tables 2.1 and 2.3. A

relative inconsistency is seen in the values reported by each source. Cotton is the main

crop grown in the county, with nonirrigated acreage greater than irrigated (USDA, 1999).

Peanuts and grain sorghum are also grown. Growth of peanuts increased steadily from

1994, with peak production of 28,900 acres in 1998. Sorghum production remained

steady with occasional high productions in 1996 and 1998. Throughout the period,

nonirrigated sorghum production was more than irrigated. Wheat is also grown in the

coxmty. The yearly irrigated and nonirrigated acreages for the major crops are shown in

Figures 2.6 (a) to (d). The irrigated land acreage has increased steadily since 1992 with

total irrigated land about 72,250 acres m 2000 (TWDB, 2001). The percent increase m

irrigated acreage between 1990 and 2000 was 155%.

2.1.2.4 Soils

The different soil types in the county are displayed in Figure 2.7 (USDA, 1994).

The soils in Dawson County are in two kinds of landscapes — High Plains and RoUing

Plains (Sanders, 1960). The High Plains occupy approximately 90 percent of the county.

14

'Donnell

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State Highway

U.S. Highway

• Main Cities

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Figure 2.5. Main Cities in Dawson County,

15

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17

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The High Plains associations have a wider variety of soils. Sandy soils (Brownfieid-

Amarillo associations) make up the westem part of the county. The high elevations of the

northwestem part of the coxmty are characterized by deep sands (Tivoli-Brownfield

associations). The middle of the county extending north and south is composed of mixed

land soils (Amarillo fine sandy loams). Limy soils (Portales-Arch associations) are

present in the north-central and southwestem parts of the coimty.

The RoUing Plains associations take up two areas (Mansker-Potter and Bippus-

Abilene) in the eastem part of the county (Sanders, 1960), The soils on the RoUing Plains

are mainly very deep and well drained, characterized by the Bippus-Abilene association,

The Potter series consists of shallow, grayish-brown calcareous soils, occurring in the

escarpment that separates the High Plains fi'om the RoUing Plains.

2.1.3 Terrv Countv

2.1.3.1 General Description

Terry County is located in the southem part of the High Plains of west Texas

(Hunt and Leffler, 1996). The location of Terry County in shown in Figure 2.1. The land

area of the county is approximately 899 square miles. The county was created fi'om Bexar

Territory in 1876 and named for Col. Benjamin Franklin Terry, commander of the Eighth

Texas Cavalry in the Civil War. Brownfield, which is on U.S Highway 380, about 45

miles east of Texas-New Mexico state line, is the county seat. There were 12,761 people

in the county in 2000 (U.S. Census Bureau, 2001).

19

The center of the county lies approximately at 38°10* north latitude and 102^21*

west longitude (Hunt and Leffler, 1996). The elevation ranges from about 3,100 to 3,600

feet above mean sea level. The land in the county is mostly level with many playas and

draws. Average annual rainfall is around 17.2 inches. The average minimum temperature

in January is 26° F and the average maximum in July is 93°F.

The South Plams Underground Water Conservatíon District (SPUWCD)

represents whole of Terry County and a part of Hockley County. The SPUWCD was

formed in April 1991 (SPUWCD, 2003). The district covers about 902 square miles of

the Southem High Plains of Texas. The district monitors water levels in approximately

125 wells annually, with water quality testing being done in 98 domestic wells and 40

irrigation wells.

2.1.3.2 History

The area that is now Terry County was once the favorite hunting ground of the

Comanche Indians tiU the mid-nineteenth century (Sanders, 1962). Buffalo hunters

moved into the area around the 1870s and opened the land to ranch settlement. The 1890s

saw the settlers begin to establish farms, and in 1904 the county was organized with

Brownfield as the county seat.

The population and the number of farms began to increase gradually by 1930. In

1930 there were 1,458 farms and the populatíon was 8,883, compared to 2,236 and 274

respectively, in 1920 (Hunt and Leffler, 1996). The oil and gas industry was vital to the

economic growth of the county. Since its discovery in 1944, oil production continued to

20

increase with a peak of 12,282,000 barrels of petroleum production m 1974. In 1991,

Terry Coimty was one of the top cotton producers in the state. Groimdwater is readily

available to irrigate much of the coxmty. Towns in the county include Brownfield,

Meadow, Wellman, Union, and Tokio (Figure 2.8).

2.1.3.3 Main Crops

Acreage values for some selected crops harvested in the county are shown in

Tables 2.1 and 2.4. Cotton is the most important crop harvested in the county (USDA,

1999), foUowed by peanuts. Irrigated cotton was more than nonirrigated in most years

(Figure 2.9b). Peanut production increased from 1996, attaining a peak of 43,600 acres in

1998 (Figure 2.9c). Sorghum, wheat, soybeans, and com are the other crops grown.

Nonirrigated sorghum production was higher than irrigated in all years (Figure 2.9d)

Around 171,800 acres of land were irrigated cropland in 2000 (TWDB, 2001).

2.1.3.4 Soils

The general soil map of Terry County is shown in Figure 2.10(USDA, 1995).

Deep sandy soils (Brownfield-Tivoli fine sands) are present in the extreme northwestem

part of Terry County (Sanders, 1962). Sandy land soils (Amarillo-Brownfield soils) are

scattered throughout the coimty especially in the southem two-thirds. The central and

northeastem parts of the county are chiefly comprised of mixed land (Amarillo fine sandy

loams). There are three large salt lakes in the eastem part of the county, and large areas of

limy soils (Portales-Drake soils) are present in the areas adjacent to the lakes. The north-

21

Miles

State Highway

U.S. Highway

• Main Cities

City Limits \C3a^

Figure 2.8. Main Cities in Terry County.

22

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24

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25

central portion of the county along the county line is made up of the hard land (AmariUo-

Portales soiis, chiefly loams).

2.2 The Ogallala Aquifer

The major aquifer present in all the three study counties is the Ogallala Aquifer

(Figure 2.11). It is the principal water-bearing unit of the High Plains Aquifer System.

The formation underlies the following states: South Dakota, Nebraska, Colorado,

Wyoming, Kansas, Oklahoma, Texas, and New Mexico (Weeks et al., 1988). The

Ogallala Formation ranges in thickness on the Southem High Plains from less than 10

feet in the south to about 800 feet northeast of AmariUo (Reeves and Reeves, 1996).

About 98% of all water used in the Llano Estacado region, which contains the three

counties for this project, was from the Ogallala (LERWPG, 2000).

2.2.1 General Geology

Water in the Ogallala Aquifer is unconfmed, contained in the pore spaces of

unconsolidated or partly consolidated sediments (Wyatt et al., 1976). The formation

mainly consists of interfmgering bodies of fme to coarse sand, gravel, silt, and clay. The

deposition began in the late Miocene to early Pliocene eras. The upper part of the

formation consists of layers of resistant caliche known as "caprock". The aquifer overlies

rocks of Permian, Triassic, Jurassic, and Cretaceous strata. The Ogallala Formation is

characterized typically of unconsolidated, fme to coarse grained, gray to red sand, clay,

and silt.

26

Extentofthe Aquifer

0 50 100 200 300 500 , —3 Miles

Figure 2.11. Map showing extent of Ogallala Aquifer

Source: USGS, 2003

27

The Canadian River cuts deeply through the Ogallala Formation in the northem

part of the High Plains area (Wyatt et al., 1976). As a result, the northem and southem

plains have been separated. The Ogallala is generally fresh, typically with about 300 to

1,000 mg/L of dissolved solids, including primarily calcium, magnesium, and bicarbonate

(TWDB, 1988). Chloride concentrations are high in the groundwater near large saline

playa lakes and other areas in the Southem High Plains where the water table is shallow.

2.2.2 Dockum Aquifer

A minor aquifer that lies beneath the Ogallala Formation in Texas is the Dockimi

aquifer (TWDB, 2003). The Santa Rosa Formation, the primary water-bearing zone in the

Dockum group, consists of 700 feet of sand and conglomerate interbedded with layers of

silt and shale. Groxmdwater in the aquifer is of poor quality, with TDS concentrations

more than 60,000 mg/L in the deepest parts of the aquifer. Even the most salt tolerant

crops cannot withstand its high levels of sodium and salinity (SPUWCD, 2003).

2.3 Other Related Research

Early investigations about the Ogallala Aquifer concentrated mainly on the

amount of groundwater in storage, pumpage, and water level declines (TWDB, 1993). A

brief discussion on the water quality was included in some reports. The Texas State

Board of Water Engineers (1944) brought out a report on record of wells and springs, and

water analyses in Terry County. Records of 195 wells, drillers' log of 28 wells, and

chemical analyses of 134 wells were included in the publication. The water quality

28

analyses were performed by chemists with the Quality of Water Division of the Federal

Geological Survey. A map was included that showed the locations of wells.

Water quality in the Southem High Plains was discussed by Cronin (1961,1969)

from analyses of samples collected during the 1950s. Forty-two samples were coUected

from various types of wells throughout the Southem High Plains in 1955 and 1956.

Dissolved solids content exceeded 500 mg/L in 25 of the 42 samples, and was more than

1000 mg/L in 5 samples. Other parameters analyzed included iron, manganese,

magnesium, chloride, sulfate, nitrate and fluoride. This report pointed at poor water

quality, with more instances of higher fluoride content. The fluoride values were more

than 1.5 ppm (mg/L), the then recommended limit, in 38 of the 42 samples. The report

also analyzed SAR, the sodium hazard. The SAR values indicated that the water was

suitable for irrigation purposes. Although there was mention of one irrigation well 10

miles south of Brownfield in Terry Coimty, no ftirther information was available on

either the well mmíibers or number of wells where sampling was done, in the area (three

study counties inclusive).

Reeves and MiUer (1978) validated these findings and reported on high TDS and

chloride levels in the Southem High Plains. The study was initiated in the early 1970s to

depict poor water areas and to establish a base-year water quality database for Ogallala

groxmdwater in the Southem High Plains. The assessment was conducted in a 27-coxmty

area of west Texas, inclusive of all the three study counties in the present project. A total

of 1597 samples from all the counties were used. Samples were coUected from windmiUs,

irrigation, municipal, and domestic wells. Nitrates, chlorides, and dissolved solids were

29

the constituents analyzed. Nitrate levels greater than 45 mg/L were clustered in the south-

east part of the Southem High Plains. Maps depicted location of wells in counties where

the water quality was poor. For instance, chloride ion distribution in groundwater was

iUustrated in Terry Coimty with the well positions. However, there was no mention on the

number of wells taken into consideration from each county.

The other previous efforts in mapping of water quality parameters are briefed in

this paragraph. Mapping of excessive TDS and chloride levels were provided by Knowles

et al. (1984) for all the coxmties in the High Plains of Texas. A considerable number of

wells shown in the maps were from the three study counties. The report was part of a

regional groundwater study of the High Plains initiated in 1978 by the Texas Department

of Water Resources. The report also provided maps showing water levels, and the

saturated thickness of the Ogallala created from data from 3800 wells. The data set later

became part of TWDB database. Gutentag et al. (1984) mapped the TDS and sodium

concentrations for the entire Ogallala range from South Dakota to Texas. Data were

obtained from more than several thousand reported chemical analyses of water samples

from the entire region (Krothe et al., 1982). Fifteen representative samples for TDS and

twenty-eight representative samples for sodium were listed. There was no specific

information on individual wells taken into consideration.

The Texas Water Commission (1989) published maps of TDS, chlorides, nitrates,

and sulfate levels in the Ogallala, from data provided by TWDB groundwater quality

system. The effort was part of a study of groundwater quality in Texas to observe natural

and man-made conditions and their effect on the water quality. Since this research was

30

statewide, all three study counties were also included.

A more detailed water quality evaluation of the Ogallala Aquifer was performed

by TWDB using samples from more than 700 representative wells in the High Plains,

between 1989 and 1992 (TWDB, 1993). The study was to coUect and assess water quality

data in each of the nine major aquifers in Texas as part of a six-year routine monitoring

cycle. Samples were analyzed for major cations, anions, and nutrients. These major

cation and anion data were compared with data from 1970s to quantify the increase or

decrease in water quality over time. The presence of trace inorganic elements, pesticides,

and radionuclides were also determined for the first time in the report. Municipal,

industrial, and irrigation wells were taken to be representative of the water quality. The

three study counties were included. Most of the poorer water quality occurred in the

southeast portion of the Southem High Plains, which constitutes Dawson and Terry

coimties. TDS and chloride levels were of bad quality especially in the 4-county area of

Terry, Lynn, Gaines, and Dawson. Fluoride composition was above the recommended

limits in much of the Southem High Plains, extending from Lamb County in the north to

Howard County in the south. The same trend was also observed in the case of selenium.

Higher nitrate values were seen to be clustered in Lynn and Dawson counties. The main

conclusion was that the water quality in the Northem High Plains had comparatively

improved while the southem part experienced a more significant deterioration, in the

twenty-year period.

Besides these, proper mention must also be made of the National Water Quality

Assessment (NAWQA) program by the USGS. The program began forming in the early

31

1980s, and by 1986 a pilot phase was started to test and refme assessment concepts

(Leahy and Wilber, 1991). Since 1991, the program has implemented interdisciplinary

investigations in more than 50 of the nation's surface and groundwater resources of major

hydrologic systems (USGS, 2003). A total number of 48 wells were sampled in the

Southem High Plams of Texas and New Mexico as part of the program in 2001. About

240 constituents were analyzed. The number of wells taken into account from Castro,

Dawson, and Terry counties were, respectively, one, two, and three. AU wells from the

study area counties except one exceeded the TDS limit of 500 mg/L. Three wells were

above the EPA guideline of 4 mg/L for fluoride. Nitrate concentrations were below the

limit of 10 mg/L-N (44.3 mg/L-NOa) in all the wells sampled.

All previous research (barring work by Texas Board of Water Engineers in Terry

County in 1944) concentrated on a much wider basis—statewide, or covering the

Southem High Plains, or the entire High Plains area. This present research focused on a

countywide area to evaluate the groundwater quality.

2.4 Geographic Information Svstems

A GIS is based on mapping software to visuaiize, manipulate, analyze, and

display spatial data by combining different layers of information about a location (ESRI,

2001). A GIS ofifers powerfiil tools for the coUection, storage, management, and display

of map-related information. A digital map created by GIS wiU have points that represent

features on the map such as cities, lines that denote features such as roads, and small

polygons that indicate areal features like lakes. The database can have information on

32

where the point is located, how long the road is, and the volume occupied by the lake.

Each piece of information is contained in a layer, and the user can either tum on or off

the layers to view the contents according to their requirements.

A GIS can be used to map quantities and densities, find what is inside a feature,

find what is nearby, and also map changes (ESRI, 2001). A distinct advantage of GIS is

improved organizational integration. Data sets can be linked together by a common

parameter, such as addresses, which helps other users to share the data. Better decisions

can be reached using this software, as information can be presented clearly in the form of

a map and an accompanying report. Lastly, GIS maps are much more flexible than the

traditional manual version.

2.4.1 GISTools

There are a variety of GIS tools that are being incorporated. Spatial and

spatiotemporal models of land surfaces, climactic phenomena, soil properties, and water

quality from measured data can all be created using spatial analysis (Wilson et al., 2000).

Spatial analysis allows work with raster-based GIS data to perform integrated raster-

theme based analysis and to overlay, query, and display multiple raster themes. ArcGIS

Spatial Analyst can be used to perform a wide range of powerfiil spatial modeling and

analysis features (Ormsby et al., 2001). With this tool, a user can convert feature themes

to grid themes, and create continuous surfaces from point features. Cell-based map

analysis and grid classification with display are also possible. Contours, slope and aspect

maps and hiUshades of these features can be generated using Spatial Analyst.

33

CHAPTER ni

METHODS

3.1 Data Sources and CoUection

3.1.1 Texas Water Development Board (TWDB)

The major water quality data source was the Texas Water Development Board

(TWDB) and its Texas Natural Resources Information System (TNRIS) database.

Groundwater quality information provided includes state well number, date of sampling

event, time, coUection remarks, reliability of sampling method, coUecting agency, lab-

calculated pH, total alkalinity, hardness, specific conductance, sodium adsorption ratio

(SAR), residual sodium carbonate (RSC), total dissolved solids (TDS), and major anions

and catíons (TWDB, 2002). The board also contains informatíon on some analyses where

infrequent constituents (metals and organics) were measured. More than 92,500 total

analyses from 50,800 groundwater sites are entered in the database; close to 287,000

infrequent constituents have also been entered.

The TNRIS is a division of the TWDB used as a clearing house of maps, aerial

photos, and digital natural resources data (TWDB, 2002). Digital data for irrigated

acreage of all the three study countíes were obtained from its website. The data used was

based on the year 1994, the latest year available.

34

3.1.2 Local Water Districts

3.1.2.1 HighPlainsUnderground WaterConservationDistrictNo. 1 (HPUWCD)

Though officially Castro County is a part of this water conservatíon district, all

the data for Castro County for this project was obtained from tíie TWDB. The HPUWCD

coUects samples for use by the TWDB in 15 counties and keeps no separate database at

its office in Lubbock. Water quality sampling events contained in the TWDB dataset

varied from the 1970s to as late as 1996, but with infrequent reportíng in many years in-

between. The primary data for the analysis was based on the year 1996, the year for

which the most recent data were available. There were 18 wells (Table 3.1) with available

data and the position of the wells is shown in Figure 3.1.

Table 3.1. Wells and their data sources Well No.

10_20_304 10_21_802 10_22_502 10_29_305 10_30_502 10_31_502 10_32_401 10_36_302 10_37_602 10_38_202 10_38_604 10_39_202 10_40_101 10_40_701 10_46_102 10_46_405 10_47_302 10 48 303

ID

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Use

Irrigation Irrigation Irrigation Irrigation

Public Supply Irrigation

Public Supply Irrigation Domestic

Public Supply Public Supply

Irrigation Public Supply Public Supply

Stock Use Irrígation Irrigation Irrigation

;, Castro County. Aquifer

Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala Ogallala

35

0 2.5 5 10 15 20 Miles

N

• Wells

U.S. Highway

State Highway

Figure 3 .1 . Location of wells in Castro County. Projection: Lambert Conformal Conic

36

3.1.2.2 Mesa Underground Water Conservation District (MUWCD)

Mr. Harvey Everheart, manager of MUWCD shared his time and some of the

district's files. The period of data coUectíon started in the 1990s. Data used for the

purpose of this thesis was based on 2001, the most recent year. Data for other years were

not used as there was not any significant difference in the water quality values over the

years. The number of wells for which water quality testing was done in the county was

52. There was no information available regarding the position of five wells, so the

number of wells taken into consideration was 47.

Additional data from the TWDB were also used. The sampling events were for 17

wells in the year 1996, the latest year where frequent data were available. The position of

the wells (both MUWCD and TWDB) is shown in Figure 3.2. Table 3.3 shows tiie data

sources for the wells mentioned (Key given in Table 3.2).

Notation

A/D C D F

F/T I

IND M

N/A 0

0/A 0/F

O/F/A P S

SD T Y

Key

Alluvium/Dockum Group Commercial Domestic supply Fredericksburg Group Fredericksburg and Trinity Groups Irrigation Industrial Mesa Underground Water Conservation District No Data Available Ogallala Formation Ogallala Formation and Antlers Sand Ogallala Formation and Fredericksburg Group Ogallala Formation, Fredericksburg Group, and Antlers Sand Public supply Stock use South Plains Underground Water Conservation District Texas Water Development Board Yard watering

37

0 2.5 5 Miles

N • Wells

U.S. Highway

State Highway

Figure 3.2. Location of wells in Dawson County. Projection: Lambert Confonnal Comc

38

Table 3.3. Wells and their data sources, Dawson County.

WellNo.

27_32_59HA 28_01_71DA

28_01_801 28_02_51JA 28_02_603

28_09_950E 28_10_75CA 28_10_98LA 28_17_14DD 28_17_24AB 28_17_27JB 28_17_52BA 28_17_52LC 28_17_61HA 28_17_69BA 28_17_83JA 28_18_101

28_18_240C 28_18_34HA 28_18_64EA 28_18_64JA 28_18_67BA 28_18_75JA 28_18_95KA 28_19_102 27_08_503 27_15_301 27_23_201 27_24_101 27_24_903 27_31_803 28_27_504

28_25_540A 27 16 920A

ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

Use

D D D D D D D D D Y

D/Y D D D D D D D D I D D D I D D D D D D D D P I

Source M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M

Aquifer N/A N/A

0 N/A

O N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

0 N/A N/A N/A N/A N/A N/A N/A A/D

0 0 O 0 0 0 0

N/A N/A

WellNo. 27_16_660B 28_25_920B 27_08_85AA 27_07_67KA 27_16_460B 27_23_830A 27_16_29BA 27_16_97JB 27_24_52IB 27_16_76EA 27_07_69HB 28_25_34JA 28_02_87AA 27_08_803 27_08_901 27_16_402 27_16_501 27_32_601 28_02_801 28_09_101 28_09_601 28_10_101 28_10_102 28_17_115 28_17_401 28_17_805 28_18_504 28_19_408 28_25_902 28_27_201

-

ID 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

Use D D D I D D D D D D P D D I I D I

Unused D I

D/S D 1 D I I D

P/Other D S

Source

M M M M M M M M M M M M M T T T T T T T T T T T T T T T T T

Aquifer

N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

O/F/A O/F/A

0 O/F/A

0 O 0 O

O/F/A F 0

O/F/A 0 0 0 0 0

39

3.1.2.3 South Plains Underground Water Conservation District (SPUWCD)

Data on water well locations and water quality were obtained from the SPUWCD.

Mr. Jason Coleman, general manager of the district, shared some of the files in electronic

format. Water quality data obtained was for the years 1998 and 2001. For the purpose of

this thesis, the latest data (year 2001) was used, as there did not seem to be significant

differences between the two years.

Well positions based on TWDB data (24 wells) were also used, for the year 1996.

The year was chosen as it was the only year in the 1990s having appreciable information.

The position of the wells in the county is displayed in Figure 3.3. The respective well

numbers along with their sources and well use are given in Table 3.4, with key for the

symbols used displayed in Table 3.2.

3.1,3 Natural Resources Conservation Service fNRCS)

Soil maps and crop pattem information were requested írom the NRCS, formerly

Soil Conservation Service (SCS). Soil Survey Geographic Data (SSURGO) database was

employed for the soil maps of Castro and Terry counties. SSURGO data are available by

county and are an updated digital replacement for the existing paper soil survey books

(USDA, 1995). The SSURGO database reproduces the soil mapping units portrayed at

scales of 1: 15,000 to 1: 20,000 on county soil survey maps and records the attributes by

soil layer or horizon for 1 to 3 soil series m each mapping unit. The database was

originally designed for farm and ranch, landowner/user, township, county, or parish

natural resource planning and management. The database is an excellent source for

40

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U.S. Highway

State Highway [

Figure 3.3. Location of wells in Terry County. Projection: Lambert Conformal Conic

41

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42

determining erodible areas and developing erosion controi practices, making land use

assessments and chemical fate assessments, and identifying potential wetlands and sand

and gravel aquifer areas. SSURGO map data are available in Arclnfo coverages and Arc

interchange file formats.

The State Soil Greographic (STATSGO) database was used for creating soil maps

in Dawson County because no data on this county were available from SSURGO. This

data set is a digital soil map prepared by the National Cooperative Soil Survey and

distributed for usage by the U.S. Department of Agriculture. National coverage is based

on a scale of 1:250,000, except for Alaska, which is set at 1:2,000,000 (USDA, 1994).

Due to these large scales, the STATSGO database is not very detailed for interpretations

at county or smalier scales. The database was designed primarily for regional, multistate,

river basin, state, and muUicoxmty resource planning, management, and monitoring. The

map data are in the form of Arclnfo coverage formats, with descriptive attributes for map

features in dBASE tables,

3.2 Data Compilation

3.2.1 GIS Applications

A geographic information system (GIS) was used to spatially locate and map the

well locations, soil distribution, and analyses of the water quality parameters in the

counties chosen. ArcGIS Desktop was the software employed for this purpose. ArcGIS

Desktop is a software package deveioped by Environmental Systems Research Institute,

Inc. (ESRI) (Ormsby et ai., 2001). This version consists of ArcView, ArcEditor and

43

Arclnfo. The three software products differ only in the number of fimctions each can

perform. ArcGIS Desktop includes three applications: ArcMap, ArcCatalog, and

ArcToolbox. ArcMap can be used to make maps from layers of spatial data, choose

colors and symbols, query attributes, analyze spatial relationships, and design map

layouts. Spatial data can be browsed on the computer's hard drive, network, or on the

Intemet using ArcCatalog. Spatial data can then be previewed and added in the ArcMap.

Conversion of spatial data from one format to another can be obtained using ArcToolbox.

Once the data were converted to be usable in GIS format, well locations were

displayed, water quality parameters mapped, and contours were generated. ArcGIS

Spatial Analyst tool was employed to create continuous surfaces and contours. Spatial

Analyst works with raster-based GIS data to perform integrated raster-vector theme

analysis, and also to overlay, query, and display multiple raster themes. Raster is a fonnat

for the storage, processing and display of spatial data. In this method, the selected area is

divided into rows and colimins to form a rectangular grid structure. Thus, each feature is

represented by a matrix of cells in continuous space. Continuous numeric values, such as

elevation, and continuous categories, such as vegetation types can be represented by the

raster model.

3.2.2 Other Software Used

Water quality data for Castro County was downloaded fi"om the TWDB website

as a portable document format (.pdf) file. The Adobe file extension was first converted as

an Excel worksheet and then saved as a database extension (.dbf extension), which was

44

asier to incorporate into GIS. Weil locations for the chosen counties were obtained from

the water level reports of each county. Point layers were generated in GIS after the

database file was added to the Arclnfo interface. Generation of points is explained in

detail in the following sections.

3.3 Map Generation

3.3.1 Well Location Displav

Information containing the position of the well points (latitude and longitude) was

obtained from the record of wells report in each county published by TWDB. The

information was in the form of Adobe fíle format (.pdf extension). The coordinate system

used was North American Datum 1983 (NAD 1983). State plane Texas North Central

(FIPS 4202) was the projection used. The foUowing steps were utilized in creating points

representing the well location (Ormsby et al., 2001).

• To allow for easy workability in generating point coverage, the file was converted

to a Microsoft Excel spreadsheet.

• Arclnfo requires mcoming point data as having a point-ID, X-coordinate, and Y-

coordinate before one can generate a point coverage. Point-ID in this case was the

well ID assigned by the state. X-coordinate and Y-coordinate were also entered.

• The spreadsheet file was then saved as a .dbf extension to facilitate import into a

GIS.

• In the Add Data dialog, the address of the .dbf file was navigated to and Add was

clicked.

45

• The added file was displayed in the Table of Contents sectíon at the left-most

comer of the map. By right-clickmg the file, the attribute table was accessed by

pressing Open. The table was found to contain the latitude and longitude

information along v^th the well ID.

• To display X- and Y-coordinates, the selected file was right-clicked and Display

XY data was chosen. The X-coordinate was the Longitude field and the Y-

coordinate the Latitude field. To fix the coordinate system Edit was selected.

Then Select was clicked. The foUowing steps were carried out to add the required

coordinate system: Projected Coordinate System\State Plane\NAD 1983 State

Plane Texas North Central FIPS 4202(feet).prj.

• The OK button in the Display XY Data dialog was clicked to display the points

on the map. A new layer with an extension called events (a default name assigned

by the software) was added to the Table of Contents.

• The new layer should be exported as a shapefile. Right-clicking on the layer, Data

was chosen and then Export Data was pressed. After making sure to keep the

coordinate system same as the data source, the output file was named accordingly

and Yes was clicked. A new layer of points was displayed in the Table of

Contents and the points now visible on the map. This layer differs from the events

layer in that it is a permanent shapefile that can be accessed anytime.

The map displays all the well points in the county. Using the Identify tool, each point can

be clicked and its details viewed.

46

3.3.2 Surface Interpolation

ArcGIS Spatíal Analyst introduces a set of spatíal interpolatíon functíons,

allowing the user to generate results for areas of missing data. The interpolatíon methods

available are Spline, Inverse Distance Weighted, and Basic Krigmg. Surface interpolation

for this project was achieved using the Inverse Distance Weighted method. In this

method, cell values are estimated by averaging the values of sample data points in the

vicinity of each cell. An assumption in this method is that the variable being mapped

decreases in influence with distance from its sampled location. The following procedure

was employed in generating surfaces.

• ArcMap was first opened by clicking the option to use an existing map.

• The Tools menu was clicked and Extensions was chosen. The Spatial Analyst box

was checked and the dialog box closed.

• The Spatial Analyst toolbar was loaded by clicking the View menu, descending

dovra to Toolbars and then choosing Spatial Analyst.

• In the Spatial Analyst menu, Interpolate to Raster/Inverse Distance Weighted was

selected. A window opens asking to specify the parameters to be used for the

interpolation.

• The Input Points Field was specified as the shapefile that contains the well

locations. The Z Value Field was the particular water quality parameter raster

needed to be mapped.

47

• Selecting Variable in tiie Search Radius Type puU down menu, tiie output raster

grid was named and stored. A grid was created in tiie layout tiiat contains nine

colors (defauh value assigned by tiie software) in the legend.

• The number of classes was set to five by opening the Layer Properties dialog,

going to Symbology and then pressing Apply.

• The classification type was specified as Natural Breaks, To achieve this, the

Classify button was pressed and Natural Breaks chosen. Apply tab was then

pressed.

• A surface was created that shows the values of the particular parameter in five

classes represented by different colors. The coloring can be changed by going to

Symbology and choosing any other color type.

The same procedure was employed for mapping all the other water quality parameters.

3.3.3 Generation of Contours

Contovirs can be created with the help of ArcGIS Spatial Analyst. These

topographic surfaces enable the user to relate real world elevations and analyze how these

siufaces might affect the data. The contour feature produces an output polyline data set.

A useful advantage of creating contours is the user can visualize flat and steep areas for

analyzing distance between contours. The main emphasis for this project was to contour

the different water quality parameters on a map and observe spatial variations. The

constituents were pH, TDS, chlorides, fluorides, nitrates, and sulfates, sodixmi absorption

ratio (SAR), residual sodium carbonate (RSC), hardness, specific conductivity and

48

Llkalinity. The procedure employed in generating the contours was the same in all the

îases and is explained below.

• ArcMap was first opened by clicking the option to use an existing map.

• The Tools menu was clicked and Extensions was chosen, The Spatial Analyst box

was checked and the dialog box closed.

• The Spatial Analyst toolbar was loaded by clicking the View menu, descending

down to Toolbars and then choosing Spatial Analyst.

• On the Spatial Analysis toolbar, Spatial Analyst/Surface Analysis/Contour was

selected.

• The input surface selected was the water quality parameter for which the contour

was to be generated, such as chlorides, for example.

• Next, the contour interval was specified accordingly and the output feature was

named and stored as a shapefile.

• Clicking OK made the corresponding contours appear on the map. The line

thickness and colors can be modified by clicking the Symbology tab.

The same procedure was repeated every time a different parameter was contoured,

However, a main limitation in dealing with contour lines in the soflware was the lack of

flexible regulation in smoothing. Jagged contour lines can be smoothed using the Smooth

feature in ArcGIS, but the resulting contours are not consistent with their corresponding

values and tend to overiap. Therefore, no smootiiing was performed on tiie uneven lines.

49

CHAPTERIV

RESULTS

4.1 Water Oualitv Parameters

The constituents for which water qxiality data were obtained are listed in Tables

kl, 4.2, and 4.3, along with their respective well numbers. The data were collected from

he TWDB database for 1996 for Castro County. Eighteen wells were taken mto

;onsideration in the county. For Dawson County, data for 1996 were from the TWDB

iatabase (17 wells), while MUWCD provided data for 2001 for 47 wells in the county.

WatQT quality information was available for a total of 121 wells in Terry County, of

A hich 100 are for irrigation purposes. The data were collected in 2001 by SPUWCD and

in 1996 by the TWDB. These data are discussed in the following sections.

4.1,1 Phvsico-Chemical Parameters

The typical measured physico-chemical parameters included pH, TDS, specific

conductivity and/or electrical conductivity of irrigation water (ECjw). The ECjw for

Dawson County were converted to TDS by multiplying by 640 (Fipps, 2003).

4.1.2 Chemical Parameters

Data were available on both anions and cations. Anions tested include Cl', SO4 ",

F', NOs" and HCOs'. Ca^ , Mg^ , Na^, and K* were the cations measured. Iron was

reported only in Dawson County. The other factors measured included alkalinity,

50

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58

hardness (as CaCOa), SAR and RSC.

SAR is a general term to indicate the sodium hazard on the soil (Fipps, 2003). It is

calculated from sodium, calcium and magnesium molar concentrations, given by

Equation4.1:

SAR= [ a l _ . (4.1) |([Ca^-] + [Mg^^

Water with high SAR levels can breakdown the physical structure of the soil. The soil

becomes hard and impervious to water. This condition is manifest mainly in fme textured

soils, such as those high in clay. To coimter the effects of excess sodium, calciimi and

magnesium should be present in adequate quantities to maintain desirable soil tilth, which

is the physical condition of the soil for cultivation, and permeability. If the SAR is above

18, the water is generally unsuitable for use (Table 4.4).

Table 4.4. Sodium Hazard of Water Based on SAR Values

SAR Hazard Comments

1-10 Low Use on sodium-sensitive crops such as avocados must be cautioned.

10-18 Medium Amendments (such as gypsum) and leaching needed.

18-26 High Generally imsuitable for continuous use. > 26 Very High Generally unsuitable for use.

Adapted from Fipps (2003).

RSC is the sum of the bicarbonate and carbonate ion concentrations minus the

simi of calciimi and magnesium ion concentrations (in meq/L), as expressed in Equation

4.2 (Ayers and Westcot, 1976):

RSC = (C03'") + (HC03")-(Ca'^)-(Mg'^)- (4.2)

59

A positive RSC means the soil has capacity for sodixmi build-up. A negative RSC

indicates that there can be no such sodium build-up as the calcium and magnesium ions

are in excess of what can be precipitated as carbonates. RSC above 2.5 is considered a

high water quality hazard for irrigation water (Table 4.5).

Table 4.5. RSC Values in Irrigation Water

RSC Hazard

<0 None. Low, with some removal of calciimi and

magnesium from irrigation water. 0-1.25

1 9S 9 so Medium, with appreciable removal of calcium and magnesium from irrigation water.

>2.50 High, with most calcium and magnesium removed

leaving sodium

Adapted from Ayers and Westcot (1976).

4.2 Water Oualitv Analvsis

The general groimdwater quality indicated by the concentrations of the

constituents is represented in the water quality tables shown in each county. The quality

of water supplied can have adverse effects on plants (used for irrigation), humans (when

used as drinking water source), and soils. To test the suitability of using such water, this

thesis concentrated on comparing established recommended values for each parameter,

for safe use.

The quality of irrigation water is determined by the amount and types of salts in

the water (McFarland et al. 2002). The main problems associated with salty irrigation

water for crop production are salinity and sodium hazards. To test for the hazards, the

60

water should be analyzed for electrical conductivity or TDS, SAR, and the presence of

any toxic ions (such as chloride, boron, sulfates, and sodium). Different crops vary in

their ability to tolerate saits. Certain critical levels have been established for each crop.

Irrigation water near the critical salt value is called "marginal" quality water. This

marginal quality water can cause some yield reduction. Crops continue to grow in the

presence of low salts, but their yields will never be fiiUy realized. Based on the work of

McFarland et al. (2002) and Fipps (2003), critical values for each crop for each parameter

were obtained (Table 4.6). Critical values for SAR and chlorides were not available for

soybeans, wheat and alfalfa. Table 4.4 earlier displayed the limits of SAR in irrigation

water for all crops. Alkalinity measures the combined amount of carbonates, bicarbonates

and hydroxyl ions. Though there are no established toxic levels for alkalinity in irrigation

water, a concentration of 50 mg/L CaCOs has been said to raise the media pH over time

(Will and Faust, 1999). A high alkalinity in irrigation water increases the sodium hazard

of the water greater than that indicated by the SAR. In that case, an adjusted SAR

(SARadj) may be calculated. The SARadj along with knowledge of soil properties helps m

better management practices when using highly alkaline water.

The water quality values could also be compared to federal and state drinkmg

water guidelines. EPA maximum contaminant levels (MCLs) were obtained and used for

comparison. Also, to represent water conditions in Texas, drinkmg water standards for

the state of Texas issued by the Texas Commission on Environmental Quality (TCEQ),

were utilized. Levels for alkalinity, calcium, magnesium and sodium are recommended

limits and no established limits are yet set for these. Table 4.7 gives information related

61

to these standards, along with general limits for irrigation water adapted from Famham et

al(1985).

Table 4.6. Critical Values for Crops in Irrigation Water

Crop mmhos/cm

TDS mg/L

SAR'

1-Adapted from Ayers and Westcot (1976). 2-Adapted from McFarland et al. (2002). 1 mmho/cm = 640 mg/L approx., adapted from Fipps (2003). NA-Not avaiiable.

Chloride^ mg/L

Sodium^ mg/L

Com Cotton Peanut

Sorghum Soybean Wheat Alfalfa

Ll 5.1 2.1 1.7 3.3 4

1.3

704 3264 1344 1088 2112 2560 832

10 10 10 10

NA NA NA

533 710

400-500 710 NA NA NA

533 710

400-500 710 NA NA NA

Table 4.7. Drinking Water and Irrigation Water Limits (mg/L except pH and % Na)

Constítuent

Alkalinity Bicarbonate Calcium Chloride Fluoride'* Hardness Magnesium Iron Nitrate^ as N Nitrate'* as NO3 Potassium

PH Sodium Sodium % Sulfate TDS

Texas standards*

60-300

75-200 300 4

400 50-150

0.3 10

44.3 100

>7.0 100

300 1000

EPA standards^

60-300

75-200 250

4 400

50-150 0.3 10

44.3 100

6.5-8.5 100

250 500

Maximum levels in irrigation water^

520

350 -

30 133

8.4 210 60

1400

1-Texas Secondary Drinking Water Standards, adapted from TCEQ (2002). 2-EPA Secondary Drinking Water Standards, adapted from EPA (2001). 3-Adapted from Famham et al. (1985). 4-Primary Drinking Water Standards, adapted from EPA (2001).

62

4.2.1 Domestic and Irrigation Applications

4.2.1.1 CastroCounty

Based on the data presented in Table 4.1, contours and surfaces were generated

using ArcMap for all the parameters (Figures 4.1-4.15). The Inverse Distance Weighted

method in Spatial Interpolation estimates values by averaging the values of sample data

points in the vicinity of each cell (ESRI, 2001). Hence, the surface created as a raster grid

slightly exceeded the boundary of the county, and was then clipped to fit the coxmty,

While examining the various water quality parameters, it was found that the water

quality samples were within prescribed limits for drinking water and irrigation water for

all constituents except bicarbonate and fluoride. High bicarbonate levels in irrigation

water tend to precipitate calcium carbonate (CaCOa) and magnesium carbonate (MgCOa),

which increases the relative proportion of sodium ions (Ayers and Westcot, 1976). This

condition can produce a sodium hazard level greater than indicated by the SAR value.

The bicarbonate levels varied from 268 to 348 mg/L in the wells, wdth a mean value of

302 mg/L, where water was sampled (Figure 4.1). Nitrate concentrations ranged from 0.8

to 14.1 mg/L, which are well below the EPA limit of 44.3 mg/L (Figure 4.2). Fluoride

levels ranged from 1.2 to 4.3 mg/L, with one well (10_31_502), shown in Figure 4.3 in

between Dimmitt and Nazareth close to State Highway 86, in excess of 4 mg/L, the

recommended primary drinking water limit. Chloride levels varied from 8 to 41 mg/L

(Figure 4.4). Such chloride concentrations can be classified as high quality water, as the

values are well under established standards for drinking and irrigation purposes. TDS

levels ranged from 324 to 454 mg/L, which were within prescribed limits for drinkmg

63

Summerfield

0 2.5 5 Miles

Figure 4.1. Alkalinity levels, Castro County.

• Wells

U.S. Highway

State Highway

^ Main Cities

Range of Values, mg/L

^ 260-280

Q28O-3OO

Q3OO-32O

11320-340

[]]>340 — Alkalinity Contour

64

Summerfield

N

Miles

Figure 4.2. Nitrate levels, Castro County.

Wells

- State Highway

- U.S. Highway

^ Main Cities


0-2.5

• 2.5-5 • 5-7.5 ^ H l 7.5-10 •>,«

Nitrate Contour

65

lummerfield

N

15 ., IDMiles

Figure 4.3. Fluoride levels, Castro County.

•

• Range

H 1 1 • •

Wells

Siate Highway

U.S. Highway

Main Cities

ofValues, mg/L

1.2- 1.8

1.8-2.4

2.4-3

3-3.6

>3.6

- Fluoride Contour

66

Summerfield

0 2.5 5

N

10 15 Miles

• Wells

^ Main Cities

U.S. Highway

StateHighway


• ^ 0-I I 10-20

[ I 20 - 30

m i 30 -• >40

Chloride Contour

Figure 4.4. Chloride levels, Castro County.

67

Miles

• Wells

- ^ Main Cities

U.S. Highway

State Highway

RangeofValues, mg/L

1 £ 3 300 - 330

330-360

[ ] 360 - 390

390 - 420

• >420

TDS Contour

Figure 4.5. TDS levels, Castro County.

68

Summerfield

0 2.5 5 10 15 Miles

N

•

*

Range

! •

1 • • "-.

- U.S. Highway

Wells

- State Highway

Main Cities

of Values, meq/L

0-0.4

0.4-0.8

0.8- 1.2

1.2- 1.6

>1.6

RSC Contour

Figure 4.6. RSC levels, Castro County.

69

[éoj Summerfield

0 2.5 Miles

Figure 4.7. pH levels, Castro County.

U.S. Highway

• Wells

State Highway

^ Main Cities

Rangeof Values

E ~ ^ 7-7.1

[ZZ\ ' • -' • • 7.2-7.3

• 7.3-7.4 pH Contour

70

]Miles

Figure 4.8. Calcium levels, Castro County.

• Wells

State Highway

U.S. Highway

^ Main Cities

RangeofValues, mg/L

Q ^ 30 - 40

r ^ 40 - 50

EZD ' ^ í \ >60

Calcium Contour

71

Summerfield

0 2.5 5 10 15 ., ] Miles

Figure 4.9. Magnesium levels, Castro County,

• Wells

StateHighway

^ Main Cities

U.S. Highway

RangeofValues, mg/L

' ^ 20-25 \r-z 1

[3325-30

I 3 30-35

£3~:i 35 - 40 Qf]>40

Magnesium Contour

72

0 2.5 5 10 15 ., I]Miles

Figure 4.10. Potassium levels, Castro County.

• Wells

- ^ Main Cities

StateHighway

U.S. Highway

RangeofValues, mg/L

4 - 5

5-6

L-, I —"

^ 7 - 8

— Potassium Contour

73

N

15 . iMiles

Figure 4.11. Sodium levels, Castro County.

• Wells

^ Main Cities

U.S. Highway

State Highway

Rangeof Values, mg/L

15-25

I I 25-35

35-45

45-55

>55

Sodium Contour

74

ySummerfield

0 2.5 5

N

10 15 ., DMiles

Figure 4.12. Hardness levels, Castro County.

75

• Wells

-^ Main Cities

U.S. Highway

StateHighway

RangeofValues, mg/L

160-200

200 - 240

[ ^ ^ 240 - 280

C; 1 28 - 320

^ > 3 2 0

— Hardness Contour

0 2.5 15 .. IDMiles •

*

Range

1 r~ 1

Wells

Main Cities

State Highway

U.S. Highway

ofValues,%

10-15

15-25

1 25 - 35

1 35 - 45

1 >45

- PercentNa

Figure 4.13. Percent sodium levels, Castro County.

76

Summerfíeld

0 2.5 5 10 15 ]Miles

N

•

*

Range

1

1

Wells

Main Cities

• U.S. Highway

• State Highway

of Values

0.4 - 0.8

0.8- 1.2

1.2- 1.6

1.6-2

>2

SAR Conlour

Figure 4.14. SAR levels, Castro County.

77

Summerfield

0 2.5 5 10 15 ., 3 Miles

N

Figure 4.15. Sulfate levels, Castro County,

State Highway

• Wells

U.S. Highway

^ Main Cities


r \ 10-20

20-30

30-40

Sulfate Contour

78

and irrigation purposes (Figure 4.5). Residual sodium carbonate (RSC) values varied

fi-om 0-1.7 meq/L. One well, 10_29_305 in the northwest part of the county (Figure 4.6),

reportedly has RSC value of 1.7, placing it as medium hazard with respect to its

application to irrigation. On the whole, the RSC is within recommended limits for use in

irrigation. AU the other parameters were within both the drinking water and irrigation

limits. However, as the number of wells taken into account was small, spatial analysis

based on concentration only may not be usefixl. Spatial distributíons provided limited

information as the number of wells was small.

4.2.1.2 Dawson County

Contour and surface maps were created for each water quality constituent in

ArcMap software, using data displayed earlier in Table 4.2. These maps are portrayed in

Figures 4.16 to 4.30. The extent of the surface was different based on whether the data

were based on the year 1996 or 2001. The TWDB data (year 1996) had a smaller extent,

as information was available only for a limited number (17) of wells. ArcGIS creates

spatial interpolation (as raster) only based on the positions of the feature using which

surfacing is done.

The TDS values firom the wells indicated that the water was typically salty

(Figure 4.16). Total dissolved solids had a mean value of 1352 mg/L fi'om 64 samples

values (range fi-oml28 to 3,365 mg/L), with all wells except two (27_23_201in the far

west portion near Gaines County and 28__17_24AB in the middle of the county)

exceeding the secondary EPA limit of 500 mg/L. Water with TDS greater than 2,000

79

0 2.5 Miles

U.S. Highway

• Wells

State Highway

N

^ Main Cities


^ S Í 100-500

I I 500-1,000

I I UOOO- 1,500

'r^] 1,500-2,000

I >2,000

TDS Contour

Figure 4.16. TDS levels, Dawson County.

80

0 2.5 5 10 15 20 Miles

N

U.S. Highway

• Wells

State Highway

y^ Main Cities


0- 10

10-20

20-30

30-40

>40

Nitrate Contour

Figure 4.17. Nitrate levels, Dawson County.

81

Miles

Figure 4.18. Fluoride levels, Dawson County.

U.S. Highway

• Wells

State Highway

^ Main Cities

RangeofValues, mg/L

0-2

2-4

>4

Fluoride Conlour

82

0 2.5 5 Miles

Fiaure 4.19. Chloride levels, Dawson County

U.S. Highway

Wells

State Highway

^ Main Cities

RangeofValues, mg/L

K H 50 - 250

[ ] 250 - 500

Q ^ 500-750

[ ~ ^ 750-1,000

IZr^ > 1,000 Chloride Contour

83

0 2.5 Miles

Surface created using only 1996 data (TWDB, 2002)

N

Figure 4.20. Sodium levels, Dawson County.

^ Main Cities

U.S. Highway

State Highway


80 - 200

] 200 - 400

400 - 600

F ^ 600 - 800

>800

• Wells

Sodium Contour

84

0 2.5 5 10 15 20 Miles


N

*

•

Range

m i i ^ * " ' •• í ^ i

Main Cities

U.S. Highway

State Highway

Wells

ofValues, %

24-36

36-48

48-60

i 60 - 72

] 72 - 84

- Percent Na Contour

Fieure 4.21. Percent sodium levels, Dawson County. g

85

0 2.5 5 10 15 20 Miles


N

-¥

yf Main Cities

U.S. Highway

State Higliway

Range of Values

M2-4 4 - 6

6 -8

IHs-io >10

• Wells

Í5AK coniour

Figure 4.22. SAR levels, Dawson County,

86

0 2.5 5 10 15 20 Miles

Figure 4.23. Alkalinity levels, Dawson County.

U.S. Highway

• Wells

State Highway

•^ Main Cities


50 - 200

]] 200 - 250

250 - 300

300 - 350

^ 350 - 400

— Alkalinity Contour

87

0 2.5 5 Miles

U.S. Highway

Wells

State Highway

^ MainCities

RangeofValues, mg/L

i m 200 - 600

I I 6 0 0 - 1,000

I ] KOOO- 1,400

400-1,800

I ] >1,800

Hardness Contour

Fieure 4.24. Hardness levels, Dawson County.

88

0 2.5 5 10 15 20 Miles

Wells

U.S. Highway

State Highway

N

^ Main Cities

RangeofValues, mgA.

0-0.2

0.2- 0.4

0.4-0.6

[;: I 0.6-0.8

>0.8

Iron Contour

Figure 4.25. Iron levels, Dawson County.

89

* rM1 ^ O'Donnell 5

0 2.5 5 Miles

Figure 4.26. Sulfate levels, Dawson County.

U.S. Highway

• Wells

State Highway

^ Main Cities


50-250

250 - 500

500 - 750

E Z I l 750-1,000

I ] > 1,000

Sulfate Contour

90

0 2.5 5 10 15 20 Miles

N

•

*

Range

mÊBáá

IHI

- U.S. Highway

Wells

• State Highway

Main Cities

of Values, mg/L

7-7.2

7.2 - 7.4

7.4 - 7.6

7.6-7.8

>7.8

pH Conîour

Figure 4.27. pH levels, Dawson County.

91

0 2.5 5 10 15 20 Miles

Surface createdusing only 1996 data (TWDB, 2002) N

-f-

yi^ Main Cities

U.S. Highway

State Highway


30-75

75-150

150-225

225 - 300

J >300

• Wells

Calciuni Contour

Figure 4.28. Calcium levels, Dawson County.

92

Miles ^ Main Cities

—— U.S. Highway

State Highway


N

Range of Values mg/L

^ H 30 - 50 I ] 50-100 • 100-150

• >200 Magnesium Contour

• Wells

Figure 4.29. Magnesium levels, Dawson County.

93

0 2.5 5 10 15 20 Miles


Figure 4.30. Potassium levels, Dawson County.

*

•

Rangc

'i i

Main Cities

U.S. Highway

- State Highway

Wells

; of Values, mg/L

10- 14

14- 18

18-22

22-26

26-30

Potassium Contour

94

mg/L are considered too salty to drink (EPA, 2001). It was noticed that all TDS values

above 2000 mg/L were in wells in the north-west part of the county. Forty of the 64 wells

exceeded a value of 1000 mg/L. Such water would not be appropriate for peanuts and

sorghum (Table 4.6), two of the important crops in the county. McFarland and Lemon

(2002) showed that peanut yield was significantly reduced with increasing TDS levels.

The growth of peanuts has increased since the drought of 1998 in the Southem High

Plains (McFarland and Lemon, 2002). Cotton, however, has a high saU tolerance level

(3,264 mg/L) and it is the main crop cultivated in the county,

Nitrate levels varied from 1.5 to 50.9 mg/L (Figiu"e 4.17). Five wells in the

westem part of the county exceeded the MCL of 44.3 mg/L. The eastem half of the

county is characterized by better qxxality water, when compared with its westem

counterpart. Nitrate contamination is a cause of concem as excessive levels may hnpair

fetal development and cause methemoglobinemia, known commonly as "blue baby

syndrome" (EPA, 2001). However, the water is suitable for irrigation purposes as any

effect on susceptible crops would not occur until NO3-N level is as high as 30 mg/L,

which is equivalent to approximately 133 mg/L nitrate (Ayers and Westcot, 1976).

The fluoride levels ranged from 1.8 to 7.5 mg/L, with a mean value of 4.4 mg/L

(Figure 4.18). Waters with fluoride levels more than the EPA limit of 4 mg/L may be

harmfiil to teeth leading to discoloration, and may also cause bone disease (EPA, 2001).

Thirty-seven wells out of 64 (58%) in the county had levels greater than 4 mg/L.

The water samples coUected had chloride levels in the range from 48 to as high as

1,144 mgA., with an average of 422 mgfL (Figure 4.19). Sixty-nine percent (44 out of 64)

95

of the wells exceeded the EPA recommended value of 250 mg/L for drinking water

(EPA, 2001). Barring the south-west portion of the county, all the other areas had

primarily values greater than 250 mg/L. The chloride concentration increased from east

to west. A salty taste may exist if water is above the secondary MCL. Higher values of

chloride, as recommended by McFarland et al. (1998), would be potentially injurious to

peanuts and also cause yield reductions (Table 4.6). The water would also be unsuitable

for use on cotton, sorghum, and com (Table 4.6). Excessive foliar absorption and leaf

bum caused by root uptake may resuh if such water is used (Will and Faust, 1999).

Sodiimi levels ranged from 80 to 1,020 mg/L, shown in Figure 4.20. Though there

is no secondary MCL for sodium as of yet, high levels of salt intake may cause

hypertension in individuals (EPA, 2002). As seen in the areal distribution, the sodixmi

concentration generally increased from north to south. Fifteen of the seventeen wells had

sodium values greater than 100 mg/L. One well (27_32_601) in the southem portion of

the county exceeded the critical value for all crops. Leaf bum and defoliation can occur

when water with high sodium values is used (Ayers and Westcot, 1976). The water from

this well is unsuitable as it also has a high sodium percentage (83%), as displayed in

Figure 4.21 and a SAR value of 21 (Figure 4.22). Levels greater than 60% sodium may

cause breakdown in the physical properties of the soils (Ayers and Westcot, 1976). The

water from this well is unsuitable for continuous use on any crop due to its high SAR

level (Fipps, 2003). The sodium and SAR values are within recommended limits for all

the other wells.

Alkalinity values ranged from 170-400 mg/L (Figure 4.23). Water from the wells

96

in the county was very hard (levels ranged from 276 to 2,056 mg/L). Although the water

is considered hard, crops generally grow well in hard water (Figure 4.24). However,

calcium carbonate deposits can clog frrigation pipes. Also, excessive hardness may cause

foliar deposits of calcium and magnesium carbonate under overhead irrigation (WiU and

Faust, 1999).

Iron values in most wells were below the prescribed limit of 0.3 mg/L, with two

wells (27_08_85AA and 28_01_71DA) in the northem part of the county havmg levels of

0.53 and 4.4 mg/L respectively, and one well (28_19_102) in the east with 0.7 mg/L

(Figure 4.25). A msty color is imparted to the water and it has a metallic taste when iron

values are greater than 0.3 mg/L (EPA, 2001).

Sulfates ranged from 50 to 1,320 mg/L, with a mean of 370 mg/L (Figure 4.26).

Sulfate levels greater than 1,000 mg/L may have a laxative effect and may impart a salty

taste to the water (EPA, 2001).

4.2.1.3 TerryCounty

Based on the values displayed in Table 4.3, ArcMap was used to generate contour

lines and surfaces (Figures 4.31 to 4.45). As there were a small number of wells (24) in

maps dealing with 1996 data, the extent was smaller, thus displaying a smaller

interpolation area.

Groundwater quality in the wells sampled is characterized by high values of TDS.

The mean value from 121 samples was 1347 mg/L, with the highest value of 7,334 mg/L.

AU wells but one (24_53_3171 in the northwest) exceeded the secondary EPA Irniit of

97

0 2.5 5

N

-f-

10 15 20 Miles

Figure 4.31. TDS levels, Terry County.

T T n TT- 1

U.S. Ilighway

• Wells

State Ilighway

^ Main Cities

Range of Values, mg/L | K É | | Í | 200 - 1,000

UOOO-2,000

2,000-3,000

| H [ [ | 3,000 - 4,000

I >4,000

1 Uo Lontour

98

0 2.5 5 10 15

N

20 Miles

• Wells

^ Main Cities

U.S. Highway

State Highway Range of Values, mg/L

< 1,000

1,000 andabove

TDS Contour

Figure 4.32. TDS levels greater than 1000 mg/L, Terry County.

99

0* . 0

^®' '

/ •

'••4'

13 *

/7 • / ..mU

2b\ / • • \ /

0 2.5 5

N

^

Figure 4.33. Nitrate levels, Terry County.

Miles

^ Main Cities

• Wells

U.S. Highway

State Highway RangeofValues, mg/L

10-20

20-30

r ~~ | 30 - 40

[ff]>40 Nitrate Contour

100

0 2.5 5 10 15 20 Miles

Figure 4.34. Chloride levels, Terry County.

*

•

Range

•

Main Cities

Wells

• U.S. Highway

- State Highway

of Values, mg/L

0-250

250 - 500

500-750

750- 1,000

> 1,000

Chloride Contour

101

0 2.5 Miles ^ Main Cities

U.S. Highway

StateHighway

• Wells

RangeofValues, mg/L

<250

250 and above

- Chloride Contour

Figure4.35 Chloride levels greater than 250 mgA., Terry County.

102

0 2.5 5 Miles

Surface created using only 1996 data (WDB, 2002)

N

Figure 4.36. Fluoride levels, Terry County.

103

^ Main Cities

— U.S.Highway

StateHighway

• Wells

• Fluoride Contour

RangeofValues,mg/L

"" 0-2

2 - 4

Miles

N

• Wells

U.S.Highway

StateHighway

^ Main Cities

RangeofValues, mg/L

0-250

250 - 500

500 - 750

"^ 750-1,000

1,000-1,250

Sulfate Contour

Figure 4.37. Sulfate levels, Terry County.

104

0 2.5 5 Miles


^ Main Cities

U.S. Highway

• StateHighway

• Wells

RangeofValues, mg/L

B 0-100

i 100-200

N

-I-] 200 - 300

300 - 400

400 - 500

Sodium Contour

Figure 4.38. Sodium levels, Terry County.

105

0 2.5 5 10 15 20 Miles

Surface createdusing only 1996 data (TWDB, 2002)

N +

*

•

Range

iHi

L^

Main Cities

U.S. Highway

State Highway

Wells of Values, mg/L

200 - 250

250 - 300

300-350

350 -400

>400

Alkalinity Contour

Figure 4.39. Alkalinity levels, Terry County.

106

0 2.5 5 10 15 Miles


N

^ Main Cities

U.S. Highway

State Highway

• Wells RangeofValues, mg/L

[^^ 20 - 40

^ 3 1 60 - 80 ~ j 80 - 100

Percent Na Contour

Figure 4.40. Percent sodium levels, Terry County.

107

109

0 2.5 5 10 15 20 Miles


N

Figure 4.41. SAR levels, Terry County.

-^ Main Cities

U.S. Highway

State Highway

• Wells

Range of Values

I I 1 0 - 1 8

18 26

>26

SAR Contour

108

0 2.5 5 Miles


Figure 4.42. Calcium levels, Terry County,

109

-^ Main Cities

U.S. Highway

StateHighway

• Wells

Calcium Contour

RangeofValues, mg/L

^ ^ 0 - 5 0

I I ] 50-100 [ j n 100-150

! i 150-200

I 200-250

Tokio

0 2.5 5 10 15 Miles


Figure 4.43. Magnesium levels, Terry County.

* Main Cities

U.S. Highway

State Highway

Wells


^ , 0 - 5 0

50 - 100

[ _ I 100-150

150-200

]>200

- Magnesium Contour

110

0 2.5 5 10 15 20 Miles


Figure 4.44. Potassium levels, Terry County.

' ^ Main Cities

U.S. Highway

State Highway

• Wells


2-5

] 5 10

] 10- 15

1 15-20

] >20

- Potassium Contour

111

0 2.5 5 10 :Miles


^ Main Cities

U.S. Highway

State Highway

• Wells


0-200

200 - 400

400 - 600

600 - 800

• >800

Hardness Contour

Fieure 4.45. Hardness levels, Terry County.

112

500 mg/L (Figure 4.31). TDS increased generally from the westem part of the county to

the eastem part. There were 89 wells (73%) above 1000 mg/L (Figure 4.32). Levels

above 1000 mg/L give the water a distinctive salty taste (EPA, 2001). Most of the 32

wells with values below 1000 mg/L were in the northem and north-central part of the

county. The use of the water for certain crops may be prohibitive (SPUWCD, 2003). The

high TDS may be harmful to the growth of cotton, sorghum, and peanuts (Table 4.6).

Peanuts are a major concem as the production of the crop has increased since 1993, with

a peak of approximately 40,000 acres irrigated in 1998 (SPUWCD, 2003).

Nitrate levels exceeded the EPA limit of 44.3 mg/L in fifteen v^ells (12%) in the

county. Eight of these wells were grouped in the south-east portion of the county (Figure

4.33). One well (2455202) had a high value of 95.8 mg/L. Nitrate values increased as

one moved from north-west to south-east. The nitrate levels, nevertheless, would not be

unsuited to irrigating crops in the coimty.

Chloride levels ranged from a minimum of 32 mg/L to a maximum of 1688

mg/L., with an average concentration of 294 mg/L (Figure 4.34). Chloride concentrations

in 56 (46%) wells were above the secondary drinking water limit of 250 mg/L. Thirteen

wells, most of them in the south-eastem part of the county had values in excess of 500

mg/L, the critical value for peanuts (Figure 4.35). Chloride concentration above 500

mg/L may lead to foliar injury in peanuts (McFarland et al. 1998). The corresponding

critical values for chloride for cotton and sorghum are, respectively, 710 mg/L and 533

mg/L.

Seventeen wells (71% out of 24 wells) exceeded the EPA limit of 4 mg/L for

113

fluoride (Figure 4.36). Sulfate levels had a mean concentration of 374 mg/L with 91 wells

(73% out of 121 wells) with values higher than the MCL of 250 mg/L (Figure 4.37).

Two-thirds of the 24 wells had sodium values in excess of 100 mg/L. Sodium

concentrations were lower than the critical values for all crops (Figure 4.38). Alkalinity

levels varied from 210 to 415 mg/L (Figure 4.39). Two wells in the westemmost part of

the county (24_44_701 and 24_44_803 m Figure 4.40) had extremely high percent

sodium value of 98%. SAR values for the two wells were in the 40s (Figure 4.41). A high

SAR indicates that the water from both wells is unsuitable for irrigation, as the sodium

levels have negative effects on soil permeability and infiltration resulting in breakdown

of the soil structure (Fipps, 2003).

4.3 Land Use

Land use generally refers to the human purposes associated with the land cover,

such as raising cattle, urban living, or industrial purposes, and relates to the human

activities on the land (Meyer and Tumer, 1994). Groundwater quality can be affected by

land management. The way in which growth takes place in a community has a bearing on

the water quality of the groundwater. Contamination of groundwater can take place

through the improper use of fertilizers, pesticides, human waste, underground storage

tanks, and hazardous wastes. Water from such a contaminated source may pose a hazard

to the public health, and also not be suitable for irrigation of crops. Agriculture is the

main water user in the three study counties, and this thesis mainly focused on the

suitability of the groundwater for irrigation purposes. Figures 4.46,4.47, and 4.48 depict

114

the irrigated acreage maps for each county showing wells under irrigated and nonirrigated

using data from TWDB for the year 1994 (TWDB, 2002).

One type of index is the percentage of land use type (e.g., agricultural,

residential/urban, forested) by area (Berka et al, 1995). Groundwater quality can be

compared under irrigated and nonirrigated agriculture, sewered and nonsewered

residential developments, industrial, and nondeveloped land uses (Trojan et al., 2002).

Information regarding the specific type of land use m the 1994 TWDB data set included

only the irrigated acreage, which can be taken as one agricultural land use type. The most

recent specific land use type data is based on the year 1980, issued by the U.S.

Geological Survey (USGS, 1990). Figures 4.49,4.50, and 4.51 display the land use

pattems based on the USGS data. Population distribution is also another factor that can

affect the availability of groimdwater and also its quality (Trojan et al., 2002). Population

changes correspond with land use change, expansion of urban areas, and changes in

agricultural practices.

For the purpose of this thesis, water quality parameters were compared between

irrigated and nonirrigated locations in the study counties, based on the TWDB data. One

assxmiption made was that any increase in irrigated acreage after 1994 was to have taken

place near the existing irrigated areas. The reason for this assumption was the fact that the

water quality data obtained was for the years 1996 and 2001, giving room for possible

change in irrigated acres in the years in-between. Accordingly, wells were grouped under

irrigated and nonirrigated conditions, and their parameters then compared.

For each well, one sample was taken mto consideration. Therefore, the number of

115

L ,/

' , f

í

Easter L. t . y ^

Dimmitt

í_r"i

M 5 6 > - A Nazareth

0 2.5 10 15 20 iMiles

N

-¥

Total Irrigated Acreage - 261,486 acres

Key

9 Main Cities

U.S Highway

State Highway

i: :•: Irrigated Land

# Wells under nonirrigated area

A Wells under irrigated area

Source: TWDB, 2002 (based on 1994 data)

Figure 4.46. Irrigated acreage, Castro County.

116

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Key

v"-í-j-'.'--'j Irrigated Land

9 Main Cities

State Highway

U.S. Highway



Figure 4.47. Irrigated acreage, Dawson County.

117

% m

0 2.5 5 Miles

N + Total Irrigated Acreage - 140,350 acres

Key


:•. •-j Irrigated Land

9 Main Cities

StateHighway

U.S. Highway



Figure 4.48. Irrigated acreage, Terry County.

118

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samples would be synonymous with the number of wells, hereafter in this document,

Table 4.8 shows the water quality constituents under irrigated and nonirrigated conditions

for the three study counties. The table shows the mean, maximum, minimum, and

standard deviation of the values obtained. A student's t-test, which is a statistical

hypotheses test, was performed to compare the means from the two land use types. The

type of test used was an independent t-test assuming unequal variances. A null hypothesis

was proposed that the difference in means between the two conditions was zero, at 95 %

confidence level. The statistical results are discussed by county in the foUowing section.

4.3.1 Land Use-Water Oualitv Correlation

4.3.1.1 Castro County

Wells were grouped under irrigated and nonirrigated conditions and studied

whether there were any major differences in water quality. Irrigated acreage increased

steadily since 1990 with a peak of 239,600 acres in 1996 (Figure 4.52). The number of

wells under irrigated and nonirrigated areas were 15 and 3, respectively. Figures 4.53 to

4.55 show contours that have values larger than permissible limits for drinking water,

overlaid on the county's irrigated acreage. AU three wells under nonirrigated area had

alkalinity levels more than 300 mg/L, while seven of the fífteen samples in irrigated areas

exceeded 300 mg/L (Figure 4.53). Fluoride concentration was more than 4 mg/L, the

primary drinking water limit, in only one well (10_31_502) in the whole county, under an

irrigated area (Figure 4.54). RSC was 1.7 for a well (10_29_305) under irrigated area

(Figure 4.55). No statistical differences were observed when comparing the major ions

122

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r—Srøunerfield

0 2.5 10 15


20 Miles


N

— U.S. Highway

State Highway

9 Main Cities

[;•;.:; Irrigated Acres



— Alkalinity Contour

Figure 4.53. Alkalinity levels greater than 300 mg/L, Castro County.

129

^_&ummerfield


0 2.5 5 10 15 20 Miles


N

- t -

U.S. Highway State Highway

9 Main Cities

• ; Irrigated Acres

Fluoride


• Wells under nonirrigated area

Figure 4.54. Fluoride levels greater than 4 mg/L, Castro County.

130

mmerfíeld

^? • * l ? ^ . ' • • • • . : - • . • : • • • •


0 2.5 10 15 20 Miles


N

State Highway

U.S. Highway

9 Main Cities

;;.;;;g:/;; Irrigated Acres

RSC



Figure 4.55. RSC levels greater than 1.25, Castro County.

131

under irrigated and nonirrigated conditions. The relatively small number of samples for

comparison made the correlation look less certain. StiU, the general high quality of the

groundwater persisted under both types of land use.

4.3.1.2 Dawson County

Irrigated acreage increased in Dav^son County fi-om 26,700 acres in 1990 to

68,200 acres in 2000, an increase of 155% (Figure 4.56). The main crops that contributed

to that increase were cotton and peanuts. There was also a positive population growth in

the county between 1990 and 2000 (U.S. Census Bureau, 2001). These factors indicated

that there was much development in the county in the last decade. Samples were

compared for water quality constituents under irrigated and nonirrigated areas. There

were 38 wells ín irrigated areas, and 26 in nonirrigated areas.

The mean chloride concentration of samples from irrigated area was 429 mg/L, as

compared to the mean level of 411 mg/L from nonirrigated area, and was statistically

similar. Out of the 38 samples taken, 30 exceeded the secondary EPA drinking water

standards of 250 mg/L in case of irrigated area, whereas 14 out of 26 samples were over

the limit for the nonirrigated case (Figure 4.57). Four wells out of a total of 38 exceeded

the EPA MCL for nitrate of 44.3 mg/L in irrigated areas, while only one well had a value

more than the EPA limit (out of 26 samples) in nonirrigated conditions (Figure 4.58). All

thirty-eight wells had TDS values more than 500 mg/L for irrigated areas, while 24 out of

26 samples exceeded the same level in nonirrigated case (Figure 4.59).

Higher concentrations of chloride and nitrate in Dawson County could likely be

132

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199

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(/) O ^ eS o t-t u

<: 1? o . ed 00

rri

'S o Z

vs.

-2 +J ed U) .

1995

Yea

unty

. Tf 0\ ON

fO o\ Os • '

1992

« 0\ o\ ""

o 0\ Ov

O U c

wso

cd Q • VO

«o •

gure

• ^

s9Joy

133


0 2.5 5


Mues

U.S. Highway

State Highway

9 Main Cities

J^^ Irrigated Acres

Cl>250 mg/L



Figure 4.57. Chloride levels greater than 250 mgÆ, Dawson County.

134

- ^ W : r ^ = ^ — - \ 1. ••-••.' T-T^

" • • • • • . • : • \ ' - • • • • • • : • ' ' • • • • • " - • - • l A

'"'34\:f''-^ 44 ^ \

28 A \ >

/ t 8 o r j 7 1 ^ ^ • • ^ 1 , ^ • • \

\i:B c # i 40 ?/C^-

* / 50 ^ / • (^49)

/ ^í

/—N ^ - i - T j i ^ . - - ' C i i ^ > ^ - ^

•

p^ ,-..-/^'.^^r-.^

7 'M / •

Lamesa

v4 x'''^

\46

^36

^

/ ^U'Uom e î r

/ 5 / •

/ 53 / #^7

A-:':'\

/ 7 . .-. ^ 8

" . - ^ . . ^ ^

Ny 24

[ST]

'- -1 _, N y

Ackerly-

25 A

6Í

•

Î2 •


0 2.5 5 10 15 20 Miles


N

-¥

U.S. Highway

State Highway

® Main Cities

;'• :í Irrigated Acres

Nitrate > 44.3 mg/L


A Welis under irrigated area

Figure 4.58. Nitrate levels greater than 44.3 mg/L, Dawson County.

135

0 2.5 5


Miles


N

U.S. Highway

State Highway

• Main Cities

^

•;; ; í Irrigated Acres


• Wells under irrigated area

TDS>500 mg/L

Figure 4.59. TDS levels greater than 500 mg/L, Dawson County.

136

due to increased application of fertilizers and pesticides (Trojan et al, 2002). Chloride is

also considered to be a good mdicator of human impacts in the environment, being

associated with several anthropogenic sources such as road salt, animal and human waste,

fertilizers, and industrial products. In their research on the effect of land use on

groundwater quality near St. Cloud area m Minnesota, Trojan et al. (2002) showed that

v^thin irrigated areas, the concentrations of bicarbonates, calciimi, chlorides, nitrates,

sulfates, sodium and TDS were higher than nonirrigated areas. High chloride levels might

also have been due to the salinization attributed to the irrigation cycling. In the initial

periods, the farmers might have chosen to not irrigate in areas with high chloride

concentrations. Reeves and Miller (1978), in their study of the groundwater quality in the

Southem High Plains, stated that high nitrate values were the resuU of the application of

high nitrate fertilizers in the region. Also, nitrate values in Ogallala increased in areas

xmder sandy soils, when compared to other areas which had fine-textured soils. Dawson

Coxmty came under this type, along with many other counties in the southem part of the

Southem High Plains. However, there were no statistical differences in means between

irrigated and nonirrigated cases for all the ions. Therefore, it cannot be said with 95%

confidence that there are any significant variations in the groundwater fi'om the two land

use types.

The poor water quality can not necessarily be attributed exclusively to irrigation

activities. There are 40 to 50 large lake basins in the Southem High Plains (Wood et al.,

1992). Dawson County does not contain a large lake basin (area larger than 15 km^), but

has close to 700 playas accounting to 1.29% of the total area in the county (Fish, 2004).

137

Wood and Sanford (1995) showed that approximately 90% of groundwater recharge to

the High Plains Aquifer was through the playa floors. Water quality can be afifected also

by evaporation where the water table is shallow (TWDB, 1993). Dawson County

possesses a relatively shallow water table. The efifect of the water tables on the chemical

quality wiU be discussed in the next section.

Fluoride levels exceeded the EPA primary drinking water standard of 4 mg/L in

22 of the 38 (58%) wells in irrigated areas, and 15 of the 26 (58%) wells in nonirrigated

areas (Figure 4.60). The student's t-test indicated that the difiference in means was

statistically zero between water quality from irrigated and nonirrigated areas. Excessive

fluoride concentrations may be attributed to the presence of fluoride minerals in the sand

and gravel that comprises most of the aquifer (Gutentag et al., 1984). Also, higher

fluoride levels could be derived from volcanic-ash deposits or when it is xmderlain by

Cretaceous rocks.

The sodium concentration exceeded 100 mg/L in 15 of the 17 wells (Figure 4.61).

Eleven samples were over the limit from irrigated area (out of 12 samples). Four of five

samples were above 100 mg/L from nonirrigated case. Saline lakes and playas can

contribute to high sodiimi concentrations in the aquifer (Reeves and MiUer, 1978). In

some areas, large sodium concentrations have been due to disposal of oil field brines. At

least two known land-based oil and chemical spiUs have been reported in Dawson County

according to Texas Water Commission (1989).

One well (27_32_601, No. 52 in Figure 4.62) had a high SAR value of 21.1, and

was under nonirrigated area. Hardness exceeded the recommended limit of 400 mg/L in

138


0 2.5 5 10 15 20 Miles


N

-•-

U.S. Highway

State Highway

9 Main Cíties

•;•;•.:;:] Irrigated Acres



Fluoride>4 mg/L

Figure 4.60. Fluoride levels greater than 4 mg/L, Dawson County.

139

^•^O'Donnell

Source: TWDB, 2002 O ased on 1994 data)

Total hrigated Acreage - 33,035 acres

Miles

U.S. Highway

State Highway

Main Cities

§W^ Irrigated Acres

Sodium> 100 mg/L



Figure 4.61. Sodium levels greater than 100 mgÆ, Dawson County.

140

— ^ • ' • 3 ^ — ' \ 1

ra - X L>-i->-^ j " • - \ 8 3 y 1 ^

\v :2-rX


0 2.5 5 10 15 20 Miles


N

U.S. Highway

State Highway

9 Main Cities

•y::-;f\ Irrigated Acres

SAR> 10



Figure 4.62. SAR levels greater than 10, Dawson County,

141

95 % of the samples in irrigated case, and 58% in nonirrigated areas (Figure 4.63).

Calcium levels were more than the recommended 200 mg/L in 4 wells, 3 of them under

irrigated area (Figure 4.64). Twenty-four wells out of thirty-eight (63%) had values in

excess of 250 mg/L sulfate in irrigated areas, while the percentage was 54% for

nonirrigated condition (Figure 4.65).

Iron values exceeded the recommended 0.3 mg/L in 3 of the 26 samples under

irrigated areas (Figure 4.66). No instance of wells with values more than 0.3 mg/L were

reported for nonirrigated areas. Alkalinity values were more than 300 mg/L in 26% of the

wells under irrigated area, compared to 23% for nonirrigated case (Figure 4.67). Only one

well was above the percent sodium recommended limit of 60%, and was under

nonirrigated area (Figure 4.69),

Looking at the maps generated, it was evident that there was no conclusive proof

of clusters of high concentrations attributable to one type of land use, such as irrigated

land. The theory that agricultural chemicals (fertilizers and pesticides) could have an

impact on the groundwater quality could not be established substantially.

4.3.1.3 Terry County

The increase in irrigated acreage was 29.3% between 1990 and 2000 for Terry

County (Figure 4.70). Most of the increase has been attributed to the growth of peanuts in

the county. The numbers of samples from irrigated and nonirrigated cases were 89 and 32

respectively, for sulfates, nitrates, chlorides, and TDS. In the case of other constituents

(TWDB data source for 1996), there were 17 and 7 samples for irrigated and nonirrigated

142

1000


0 2.5 5 10 15

Total hrigated Acreage - 33,035 acres

20 Miles

U.S. Highway State Highway

Main Cities

Irrigated Acres



Hardness>400 mg/L

Figure 4.63. Hardness levels greater than 400 mg/L, Dawson County.

143

m A5 ^rim

J7 # 2

#0'Donnell

5

300

fÍííU .-> '"*"""

'-

^ - < \' ^ v -:n

" 34 \ \\ ,r 44 ^ \ •-'''

# í "- \ ^ 42 \ /

\ y •

/ ^ \

'• "••*'--•' r T " ' - v - . - i ^ * * ^

7 •i-:'//::-:^^A / ^;ií=--n::'::-A--'

A

25 17

iLamesa

45 'Lfii

180'

í ^ 40

i / 115

15 ^' ^k'' 60 .16

30 24

349

46

33

63 \36 Ackerb

25

62


0 2.5 Miles

Total Irrigated Acreage - 33,035 acres U.S. Highway

State Highway

• Main Cities

^ Irrigated Acres

Calcium> 200 mg/L



Fieure 4.64. Calcium levels greater than 200 mgA., Dawson County. g

144

400 ^ 600

;^0'Donnell 5

0 2.5 5


N


Miles

U.S. Highway

State Highway

® Main Cities

W:--':'\ Irrigated Acres



Sulfate>250 mg/L

Figure 4.65. Sulfate levels greater than 250 mg/L, Dawson County.

145

0 2.5 5 10 15


N

- f -


20 Miles

U.S. Highway

State Highway

Main Cities

l^':/:/--^:!^ Irrigated Acres



Iron>0.3 mg/L

Figure 4.66. Iron levels greater than 0.3 mg/L, Dawson County.

146


0 2.5


N

Miles

U.S. Highway

State Highway

9 Main Cities

.:vj Irrigated Acres



Alkalinity>300 mg/L

Figure 4.67. Alkalinity levels greater than 300 mg/L, Dawson County.

147

#0'Donnell


0 2.5 5


Miles

U.S. Highway

State Highway

9 Main Cities

^ ^ 1 Irrigated Acres

Magnesiuni> 150mg/L



Figure 4.68. Magnesium levels greater than 150 mgA., Dawson County.

148

l-í--1 nJW

' ^W^ o

28 •

•

j f l l í

• ^ ? • • . • • " • : - " ^

^"-••^':^" ^ • ^ ' " 4 9 ^

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' « "• ,. " V A . •--

A " \

9 ^

•

Sk./

ií? / ~ A # ( 3 4 9 ^

^"-vr^-^^

1 l'^f * l 1 1 \ 1 1 l'^L-^ / ) 1 1

5 •

/55

.5-2

•^O'Donnell

55 #47

-A ^

/ "^L^"^'^^-^

/ ^ /7

#Laniesa

%/6

\^^

33 \

\36

15

J5f

6/ #

[gf

^

#

*3

: AA

i 8 o y ^

^ ^ .22 ^20^

24 #

Acker?

25

m,


0 2.5 5 10 15


N

^

20 Miles

U.S. Highway

State Highway

9 Main Cities

J^ÚiWi Lrigated Acres

Percent Na> 60%



Figure 4.69. Percent sodium levels greater than 60 %, Dawson County.

149

o o <N

0\ o\ o\ '' '

00 o\ 0\

co V &û ed 0) ^ 4

P <

1 ? +-» ctf CtO

•r* H

I >

o U

H o

• |

ssjjy

150

areas respectively.

Table 4.8 shows the resuhs from the t-test analysis. Except sodium, all the other

ions did not statistically have any difference in means for water from irrigated and

nonirrigated areas. Consequently, at 95% confidence level, there was no persuasive

evidence in the data to suggest any difference in the two set conditions.

The percentage of wells where the chloride values were over the limit of 250

mg/L was higher in nonirrigated areas (53%), than irrigated areas (44%), as shown in

Figure 4,71. AU wells (32) exceeded the TDS water quality limit of 500 mg/L in

nonirrigated condition, while 88 out of 89 wells had values greater than the same Umit in

irrigated case (Figure 4.72). Seventy-three percent of the samples exceeded the

recommended sulfate limit of 250 mg/L in irrigated scenario, compared to 81% in

nonirrigated case (Figure 4.73).

High TDS values could occur due to the seepage from these large saUne basins or

from playa lake basins (Reeves and MiUer, 1978). The mineraUzed water in the

underlying Lower Cretaceous rocks may have moved into the OgaUala thus accounting

for the excessive dissolved solids concentrations and large proportions of magnesium,

sodium, chloride, and sulfate (Gutentag et al, 1984). Reeves and MiUer (1978) accounted

the high chloride values to the presence of saline playa lakes. Wood (2001) claimed that

eolian transport from saline lakes may have a major impact on groundwater quaUty. In

Terry County, there are three large saline basins—Rich, Mound, and Brownfield lakes

(Wood et al., 1992) in addition to its 297 playas (Fish, 2004). Areas of very high TDS

and chloride levels occurred in regions where these saUne lakes were present (Figures

151


0 2.5 5 10 15 20 Miles


N

U.S. Highway

State Highway

® Main Cities

;.:;'; Irrigated Acres



Cl>250 mg/L

Figure 4.71. Chloride levels greater than 250 mg/L, Terry County.

152


0 2.5 5 10 15 20 Miles


N

-¥

U.S. Highway

State Highway

• Main Cities

•'•::::;{ IrTÍgated Acrcs



TDS>500 mg/L

Figure 4.72. TDS levels greater than 500 mg/L, Terry County,

153


0 2.5 5


U.S. Highway

State Highway

• Main Cities

W:'Mi\ hrigated Acres



Sulfate>250 mg/L

Rgure 4.73. Sulfate levds greater than 250 mg/L, Terry County.

154

4.74 and 4.75). Areas surrounding Brownfield Lake were subject to abnormal TDS and

chloride levels (above 750 mg/L for chlorides and over 3000 mg/L for TDS). The effect

of Mound Lake on the water quality, however, remains unclear as there was no weU

downstream of the lake. Another observation was the presence of a water table much in

the range less than 50 feet in areas where the lakes were located. The discussion on water

table depths is provided in the next section. The high sodium levels can also be due to the

seepage of saUne lakes or playa lakes (Gutentag et al, 1984).

Oil field brines can also contaminate the groundwater quality (TWDB, 1993). The

other areas of poor water quaUty in the county could be due to these brines. Brine was

considered to one of the chief poUutants of the Texas' aquifers as reported by the Texas

Water Commission (1989). The report also pointed that there were three land-based oil

spiUs in Terry County as of 1987. High chloride concentrations in the state were

coincident with areas of heavy oil and gas production in some north-central Pecos

Counties. As mentioned before, the county has a history of continued oil production

which was vital to its economic growth. LocaUzed high chloride concentrations may be

due to these oii field brines. However, this thesis did not investigate on the extent of those

plumes and there was lack of information on the locations of oil and gas production

facilities in the county.

Thirteen wells out of 89 (14.6%) in irrigated area exceeded the MCL of 44.3

mg/L for nitrate. The number was 9 out of 32 weUs in nonirrigated case (28.1%). The

nitrate contours along with the irrigated acreage in the county are depicted in Figure 4.76.

Fluoride values exceeded the EPA primary limit of 4 mg/L in 13 of the 17 wells (76%) in

155

N

15 , 3 Miles

• Wells

• Saline Lakes


0-250

250 - 500

500 - 750

750-1,000

> 1,000

Figure 4.74. Saline lakes in Terry County, and high chloride levels.

156

0 2.5

N

10 15 ] Miles

• Saline Lakes

• Wells


IJ im 200- 1,000

•1 1,000-2,000

2,000-3,000

3,000-4,000

>4,000

Figure 4.75. Saline lakes in Terry County, and high TDS levels,

157

0 2.5 Miles

Total Irrigated Acreage - 140,350 acres N

Main Cities

U.S. Highway

State Highway

.-•: •• Í Í Irrigated Land

NitratO 44.3 mg/L



Figure 4 .76. Nitrate levels greater than 44.3 mg/L, Terry County.

158

irrigated case, as compared to 57% fi-om nonirrigated areas (Figure 4.77). The high

fluoride levels can be attributed to the presence of fluoride minerals m the aquifer, and

also to volcanic-ash deposits where the aquifer is underiain by Lower Cretaceous rocks

(Gutentag and others, 1984).

Hardness levels exceeded the secondary limit of 400 mg/L for all the samples

fi-om irrigated areas, while the number was 4 out of 7 samples m nonirrigated areas

(Figure 4.78). Also, high SAR values and percent sodium levels were more in

nonirrigated as to irrigated case.

The t-test yielded results that could not suggest any difference in means xmder

both land-use types. The maps imply that poorer water quality regions are under

nonirrigated areas. The saUne lakes exist in primarily nonirrigated lands, The farmers

might have decided in the past not to irrigate crops in regions where water quality was

low. This choice could explain the anomalous results that pointed to better quaUty water

from irrigated areas.

4.4 EfiFect of Water Table Depths

Water levels declined in the OgaUala after extensive irrigation from groundwater

began in the 1940s (Weeks and others, 1988). In a large area of the Southem High Plains,

water levels rose during the 1970s, possibly due to infiltration of irrigation water,

changes in farming practices, and high precipitation. By 1980, the water levels dropped

by more than 100 feet in parts of the region. This section deals with testing evidence to

connect the water table depths with variations in water quality. The water table depth

159


0 2.5 10 15 20 Miles


N

-f-

# Main Cities

U.S. Highway

State Highway

/::?••:•] Irr igated Acres


^ Wells under irrigated area

F>4mg/L

Figure 4.77. Fluoride levels greater than 4 mg/L, Terry County.

160


0 2.5 5


# Main Cities

U.S. Highway

State Highway

N

i;"í':.l- ;-.' Irrigated Acres



Hardness>400 mg/L

Figure4.78 Hardness levels greater than 400 mg/L, Terry County.

161

0 2.5 5

Total Lrigated Acreage - 140,350 acres

N


Miles

Main Cities

U.S. Highway

State Highway

Irrigated Acres

-I- PercentNa>60%



Figure 4.79. Percent sodium l.vels greater than 60%. Terry County,

162

Total Imgated Acreage - 140,350 acres

N


Miles

m Main Cities

U.S. Highway

State Highway

f/-''M:'' • Irrigated Acres



Alkalinity>300 mg/L

Figure 4.80. Alkalinity levels greater than 300 mgA., Terry County,

163

maps for all the study counties were generated using data fi-om TWDB for the year 2001

(TWDB, 2002). As there was no specific information on the depth to water table for each

individual water quality weU, the TWDB water levels were assumed to represent each

county. Therefore, the depths indicated in the maps are from wells that were different

from the sampied water quality wells. The water table depths for each county are

niustrated in Figures 4.81 to 4.83.

The different ranges of the watertable depths were arbitrarily chosen as 50 feet or

less (shallow), 50 to 100 feet (intermediate), and 100 feet or more (deep). The areal

extent (generated as surface) of individual parameters with limits above EPA

recommended levels, xmder dififerent water table depths are shown in Figures 4.84 to

4.101. Maps were generated for different individual depth ranges and these ranges were

overlaid on the sxirfaces of the parameters. The 3D Analyst tool in ArcGIS was used to

calculate the respective areas.

4.4.1 CastroCountv

As seen in Figure 4.81, Castro County possesses a relatively deep water table,

more than 200 feet from the land surface in most of the wells (16 of the total 18). When

looking at the groundwater quaUty data of the county, it was clear that most of the

constituents were well within established or recommended limits for drinking or

irrigation uses. There were no instances of high values for parameters like TDS,

chlorides, and nitrates. Also, there was a lower variation in the reported values. This

deeper water table could be a factor for the good and relatively uniform water quality

164

Summerfield

0 2.5 5 10 15

N

-h

20 Miles

^ Main Cities

• Wells

— U.S Highway

State Highway

Depth to water, ft

] 50- 100

] 100- 150

150-200

>200

Figure 4.81. Water table depths, Castro County

165

0 2.5

N

Miles

Fieure 4.82. Water table depths, Dawson County.

• Wells ^ Main Cities

U.S. Highway

State Highway

Depth to water, ft

0-50

50- 100

] 100- 150

>150

166

9 8 . ' " " •

l^ .f okio - l ' '

1"í

36 104 37 •-•- lc

so 'JS J'' U

iA 6í>

elíman ^^' • 1.5 .-^^^ i / T " • 75

í)0

120

118 1 P'

\iound

N

Miles

Fieure 4.83. Water table depths, Terry County.

• WeUs

^ Main Cities

— U.S. Highway

State Highway

• Saline Lakes

Depth to water, ft

Í 7~ I • M 0-50

50- 100

100- 150

>I50

167

prevalent in the county, as the effect of evaporation from the surface would be reiatively

lesser when compared to other shallower water tables.

4.4.2 Dawson Countv

Dawson County has a much shaUower water table, with much of the surface being

of depth under 50 feet (42.5%), as shovm in Figure 4.82. For comparison, areas with

chlorides, TDS or nitrates greater than the recommended Umits were correlated with

water table depth (Figures 4.84 to 4.92). Approximately 35% of the total area of the

county had chloride levels greater than 250 mg/L under <50 feet (Figure 4.84). Twenty-

eight percent of the county area accounted for high chloride levels in the range 50 to 100

feet (Figure 4.85). Most of the high TDS concentrations occurred in areas where water

table depth was <50 feet (Figure 4.87). In fact, the depth <100 feet accounted for close to

60% of the total exceedances in the county for TDS (Table 4.9). In the case of nitrates,

however, there were no major differences between different depth ranges (Figures 4.90 to

4.92). Each range contributed to a meager percent of total area of the county (Table 4.9).

A factor to be noted is the geographic pattem of water-level changes in the county

in recent years. McGuire and Sharpe (1997) mapped the water-level changes in the High

Plains Aquifer from 1980 to 1995. The map depicted water-level decUnes and rises over

the fifteen-year period. It was seen that Dawson County had predominantly rises in the

water table in the range of 5 to 40 feet. Only fewer areas had no significant change. This

rising water table increasingly points to the susceptibility of the groundwater in the

county to contamination.

168

0 2.5 5

N

10 15 20 Miles

Percent area of total = 34.8%

Figure 4.84. Chloride levels >250 mg/L and under 50 ft depth, Dawson County.

• WeUs

U.S. Highway

State Highway

® Main Cities

Cl>250 mg/L

250-500

] 500-750

750-1000

>1000

! Depth, I to 50 ft

169

O'Donnell 5

0 2.5 5 10 15 20 Miles

N

-¥ Percent area of total = 27.9%

Figure 4.85. Chloride levels >250 mg/L with depth from 50 to 100 ft, Dawson County.

• Wells T T C T T rtlTTTrm r

U.o. llignway

State Ilighway

® Main Cities

Cl>250 mg/L 250 - 500

500 - 750

^ m 750- 1,000

> 1,000

] 50 to lOOftdepth

170

• O'Donnell 1

0 2.5 5 10 15


N

20 Miles

U.S. Highway

State Highway

• Wells

9 Main Cities

CI>250 mg/L

250-500

500-750

750-1,000

100 to I50ftdepth

Figure 4.86. Chloride levels >250 mg/L and under 100 to 150 ft range, Dawson County.

171

0 2.5 5 10 15 20 Miles


N

— U.S. Highway

• Wells

State Highway

9 Main Cities

I _ ^ Depth, 1 to 50 ft

TDS>1000, mg/L 1000-1500

1500-2000

>2000

Figure 4.87, TDS levels >1000 mg/L and under 50 ft depth, Dawson County.

172

0 2.5 Miles

U.S. Highway

State Highway

N Percent area of total = 26.6%

• Wells

B Main Cities

50 to lOOftdepth

TDS>1,000 mg/L [ I 1,000-1,500

1,500-2,000

>2,000

F,gure 4.88. TDS levels >100O mg/L with depth from 50 .o 100 ft, Dawson County.

173

O'Donnell

0 2.5 5 10 15

N


20 Miles

U.S. Highway

State Highway

• Wells

® Main Cities

TDS>1000mg/L

1,000-1,500

1,500-2,000

>2,000

3 100tol50ftdepth

Figure 4.89. TDS levels >1000 mg/L and under 100 to 150 ft range, Dawson County.

174

0 2.5 5 10 15 20 Miles

N

Percent area greater than 44.3 mg/L = 0.05%

U.S. Highway

State Highway

Main Cities

Wells

Depth, 1 to 50 ft

Values, mg/L 20-30

30-44.3

>44.3

Figure 4.90. Nitrate levels >44.3 mg/L and under 1 to 50 ft depth, Dawson County.

175

0 2.5 5 Miles

Percent area greater than 44.3 mg/L - 0.07%

U.S. Highway

State Highway

Wells

Main Cities

j 50 to lOOftdepth

Values, mg/L

20-30

I H I 30-44.3 H >44.3

F,gure 4.91. Nitrate levels >44.3 mg/L aud under 50 to 100 ft range. Dawson County.

176

0 2.5 5 10 15 20 Miles

N


U.S. Highway

State Highway

• Wells

® Main Cities

Í Z I J ^^^^^ 150ftrange

Values, mg/L 20-30

WÊÊ 30-44.3

• • >44.3

Figure 4.92. Nitrate levels >44.3 mg/L and under 100 to 150 ft range, Dawson County.

177

Table 4.9. Water Quality Values with Table Depths and Areas County Parameter

Dawson TDS

Chlorides

Nitrates

Teny TDS

Chlorides

Nitrates

4.4.3 Terrv Countv

Maximum value (mg/L)

1000

250

44.3

1000

250

44.3

Water table depth, ft

Oto50 51 to 100 101 to 150

0to50 51tol00 101 to 150

0to50 SltolOO 101 to 150

0to50 51 to 100 101 to 150

0to50 51 tolOO 101 to 150

0to50 51tol00 101 to 150

% Area

32.2 26.6 5.58 34.8 27.9 5.93 0.05 0.07 0.03 14.7 34.7 17.7 13.7 21.8 8.89 2.21 1.07 0.09

The generalized map of depth to water in Terry County is given in Figure 4.83.

Much of the water table surface was in the range from 50 to 100 feet (42.9%). Higher

concentrations of the TDS, chlorides, and nitrates with different water table depths are

depicted in Figures 4.93 to 4.101. Thirty-five percent of the total area of the county had

high TDS concentrations in the depth range 50 to 100 feet (Table 4.9). The number was

fifteen percent for TDS concentrations within the less than 50 feet range. However, ali

very high values (3000 mg/L and above) occurred in the <50 feet range. It is found that

the three large saline basins occur in this area (Figures 4.75 and 4.83; characterized by

Portales-Drake series shown in Figure 2.10). A similar situation was observed in the case

of chlorides. Concentrations were very high (750 mg/L or above) in the depth range <50

178

0 2.5 Miles

N Percent area of total - 13.7%

U.S. Highway

State Highway

Wells

Main Cities

1 Depth, 1 to 50 ft

Cl>250 mg/L

rzz] ^ ' ^^ Q 500-750

750-1,000

1,000

Fieure 4.93. Chloride levels >250 mg /L with depth under 50 ft, Terry County.

179

Miles

Percent area of total - 21.8%

Figure 4.94. Chloride levels >250 mg

U.S. Highway

State Highway

• Wells

• Main Cities p ' ^ ^ ; SOtolOOftdepth

Cl>250 mg/L

P ^ 250 - 500

i Q 500-750

750-UOOO

n > 1,000

/L with depth from 50 to 100 ft, Terry County.

180

0 2.5 15 ., iMiles


U.S. Highway

State Highway

• Terry Wells

• Main Cities

r ^ " " ^ 100 to 150 ft depth

Cl>250 mg/L

250 - 500

1 500 - 750

750-1,000

>U000 J Figure4.95.Chloride levels >250 mg/L with depth from 100 to 150 ft, Terry County,

181

0 2.5 15 ., 2 Miles


U.S. Highway

State Highway

• Wells

• Main Cities

p ' """ ! Depth, 1 to 50 ft

TDS>1,000 mg/L

1,000-2,000

l' I 2,000 - 3,000

WÊ/^ 3,000 - 4,000

Figure 4.96. TDS levels >1000 mg/L wUh depth under 50 ft, Terry County.

182

Miles


Figure4.97.TDSlevels>1000m g/LwUhdepthfrom50tol00ft,TerryCounty.

183

0 2.5 5 10 15 Miles


N

•

•

cz TDS>

• U.S. Highway

• State Highway

Wells

Main Cities

; 100 to 150ftdepth

1,000 mg/L

1,000-2,000

2,000 - 3,000

Figure 4.98. TDS levels >1000 mg/L with depth from 100 to 150 ft, Terry County.

184

N

Miles

Percent area greater than 44.3 mgÆ. - 2.2%

U.S. Highway

State Highway

• Wells

• Main Cities

Nitrate>44.3 mg/L

20-30

30 - 44.3

>44.3

Depth, l to 50 ft

Figure 4.99. Nitrate levels >44.3 mgÆ. with depth from 0 to 50 ft, Terry County.

185

/

/ /

2 % ™

%yûr ^2 18 /

0 2.5 5 10 15 Miles


N

U.S. Highway

State Highway

• Wells

• Main Cities

Nitrate>44.3 mg/L

'p 20 - 30

^ H i 30-44.3

1 ^ ^ >44.3

p j 50 to lOOftdepth

Figure 4.100. Nitrate levels >44.3 mg/L with depth from 50 to 100 ft, Terry County.

186

Miles

Percent area greater than 44.3 mgÆ. = 0.08%

U.S. Highway

State Highway

Wells

Main Cities

r*"" ! I00tol50ftdepth

N

Pigure 4.101. Nteate levels >44.3 mg/L w,th depth íron> 100.0 150 ft, T e ^ Counly

187

eet range, near where the lakes are located (Figure 4.74). Two percent of the total area

lad nitrate values more than 44.3 mg/L under <50 feet range, compared to 1 percent in

the 50 to 100 feet range (Figures 4.100 and 4.101).

The presence of a shallow water table beneath the three lakes could be a major

reason for the typically high TDS and chloride levels in the county. Also, according to

McGuire and Sharpe (1997), the county had water-level increases in the range from 5 to

20 feet from 1980 to 1995. These factors indicate that the water from wells in this

particular part of the county would not be best suited for domestic and to a certain extent

irrigation applications.

4.5 Comparison of Groundwater Oualitv across Counties

The major ionic species (Na , K" , Ca , Mg^ , Cl', COa ", HCOa', and SO^ ') in

groundwater are represented by county in the form of a trilinear diagram in Figure 4.102.

The percentage composition of three ions can be displayed in such a trilinear plot (Fetter,

2001). Data for the plots were from the TWDB based on 1996. Concentrations in mg/L

were converted to meq/L, and percentage total of each ion was plotted in the diagram. It

was evident from the diagrams that bicarbonate was the dominant anionic species in

Castro County (79.5%), while Terry and Dawson Counties were dominated by chloride

ions (39% and 46%, respectively). This observation is consistent with the findings of

Chebotarev who concluded that groundwater tends to evolve chemically toward the

composition of seawater during the course of flow (Freeze and Cherry, 1979). This

evolution is generally accompanied by the following regional changes in dominant anion

188

Key

C: Castro Coimty; D: Dawson County; T: Terry County

Figure 4.102. Trilinear diagram for the study counties.

189

species along the travel path (Equation 4.3).

HCO32- HCO3^--HSO42- - S042--hHC032- -•so4^-+cr ->cr+so4^- - • c r . (4.3)

Thus, water progresses from a region of low salinity to one of high salinity along the flow

path. The Chebotarev sequence is valid for the present case, accounting for the change

from bicarbonate-dominant groundwater in Castro County to the chloride-dominant water

in Dawson County.

A t-test was conducted to test water quality samples between the study counties.

A null hypothesis was set such that there was no difference in mean of population

representing each parameter sampled from one county and the other. It was noted that

water quality values for all parameters from Castro County had statistically different

means in comparison with the other two counties. Thus, there was statistical proof to

suggest that the water quality from Castro County was different from those of the other

two. The means from the t-test for select parameters in each coimty are shown in Table

4.10.

Table 4.10 Means from t-test for Select Parameters

Chloride TDS Nitrate Fluoride

Parameter Castro 22.1 379 6.07 1.99

Counties Dawson

422 1352 13.3 4.41

Terry 294 1347 25.5 4.50

When comparing means for the values between Dawson and Terry coimties, it

was noticed that chloride and nitrate values were statistically different. The nuil

hypothesis was however, accepted, in the other two parameters (TDS and fluoride).

190

The average saturated thickness of the aquifer in each of the counties was

estimated using the 1995 saturated thickness data for Southem High Plains from Stovall

(2001). Castro County had an average saturated thickness of 140 ft, much thicker than

Dawson (90 ft) and Terry (60 ft) counties. The thick saturated zone, along with the fact

that the water table in Castro County was relatively deep, could be a reason for the

uniformly high quality water in the region, The larger, thicker water body in Castro

County has more capacity to dilute infiltrating waters than the thinner aquifer zones in

Dawson and Terry coimties.

191

CHAPTER V

CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions

The purpose of this project was to use modem tools to assess the groimdwater

quality variations in Castro, Dawson, and Terry counties. The specific objectives of the

study were (1) collection of existing water quality, land use, and depth to water data from

the three counties, (2) creation of GIS coverages for the data collected, (3) determination

of any difîerences in water quality between land uses and water table depths, and (4)

evaluation of the groundwater for irrigation and domestic usage.

The TWDB was the main source of water quality data. GIS coverages for

irrigated acreages were obtained from TNRIS. The rest of the water quality data were

from the two single-county conservation districts—MUWCD for Dawson County and

SPUWCD for Terry County. The year 1996 was chosen for Castro County, as this was

the latest year where water quality information was available from the TWDB, the data

for which is provided by HPUWCD. For Dawson and Terry counties, 2001 was the year

taken into consideration. AII the major cations (Na , K" , Mg^ , and Ca^^ and anions (CF,

S04^", NO3", and F') were available for each county, with iron reported only m Dawson

County. However, the number of sample locations for each parameter varied within the

counties.

Independent t-tests were performed on the water quality values. The null

hypothesis was that there was no difference between the mean of population representing

192

each constituent sampled from irrigated and nonirrigated areas. The results indicated that

the null hypothesis was accepted, which meant that at 95% confídence level, there were

no statistical differences in chemical water quality between sample sets from the two land

use types for all counties. The only exception was sodium (Na"") in Teny County where

the null hypothesis was rejected and water quality was statistically different between the

land uses.

A t-test was also conducted to test water quality of samples from each county.

There was evidence to point that water quality from Castro County was statistically

different from those of the other two counties for all major ions (CF, SO^ ', F", NOa", and

TDS). Between Dawson and Terry coimties, the water quality values were statistically

similar for some ions (TDS and F'); while for some others (Cl'and NO3') it was not.

Another factor to be considered is the Chebotarev sequence. According to this

sequence, the dominant anion species in groundwater varies from bicarbonate to chloride

ion along the flowpath. Castro County (HCO^": 79.5%, SO^^': 10.3%, CI*: 10.1%)was

dominated by bicarbonate-rich groundwater. Terry County (HCO3': 22.8%, SO^^':

38.1%, CI': 39.1%) was characterized by relatively high chloride and sulfate ions in its

groundwater. Dawson County (HCO^': 16.2%, SO^^": 37.8%, Cl": 46%), which is

farthest in the Ogallala flow path among the three counties, has the highest chloride ion

dominance leading to increased salinity.

The other conclusions are discussed by county in the following sections. Other

conclusions about specifíc well locations within a county were possible.

193

5.1.1 CastroCounty

Castro County was characterized by high quality groundwater. There was little

variation in reported water quality values. TDS values were within the EPA limit of 500

mg/L for all test wells. Chloride levels were well within the EPA standard of 250 mg/L,

with the maximum value being 41 mg/L. Nitrate values ranged from 0.8 to 14.1 mg/L.

AU the other constituents were also within allowable limits. The presence of a relatively

deeper water table (>200 ft in most areas) could be a reason for the uniform and high

water quality in the county, as evaporation effects would be minimal. The thick saturated

zone, capable of holding large volume of water, could explain the lower concentration of

most constituents. The small number of sample locations (18) made the spatial

distributions look less certain in making specific correlations. However, the county has

excellent quality groimdwater to meet its domestic and irrigation applications.

5.1.2 Dawson Countv

The reported water quality values showed a greater degree of variation than for

Castro County. The county had wells with high TDS, chloride, fluoride, and sodixmi

concentrations. TDS for 62 of 64 wells exceeded 500 mg/L. Forty of the wells had values

above 1000 mg/L. It was noticed that wells with high TDS (>2000 mg/L) were in the

northwest part of the county. Water from wells with the high TDS levels is not suitable

for crops like peanuts and sorghum because of its salinity content. Chloride values were

greater than 250 mg/L in 44 of the 64 wells. The concentrations increased from east to

west in the county. High chloride levels are potentially injurious to peanuts, cotton,

194

sorghum, and com. Five wells in the westem part of the county exceeded the EPA limit

of 44.3 mg/L for nitrate. Fluoride values were above 4 mg/L in 37 of the 64 wells. Fifteen

of seventeen wells exceeded 100 mg/L for sodium. High sodium levels can result in

hypertension in humans, and may cause leaf bum in plants.

There were no clusters of high concentrations attributable to one type of land use,

such as irrigated land. This observation, along with the statistical tests, suggests that

agricultural chemicals have not affected water quality at a detectable level. High chloride

levels may be aggravated by salinization from irrigation cycling. The irrigators might

have also chosen, in the initial stages, to not irrigate in areas of high chloride levels. The

high TDS and chloride levels could be due to the relatively shallow water table in parts of

the county. Approximately 43% of the county had water table depths less than 50 ft. It

was shown in the maps that the high values were within the less than 50 ft range as

compared to other depth ranges. The more uniform high fluoride levels may be attributed

to the presence of naturally occurring fluoride minerals such as volcanic-ash deposits in

and near the aquifer. High localized sodium and chloride concentrations could be due to

disposal of oil field brines. There were at least two documented cases of oil spiUs in the

county as of 1987, but the nature of these plumes and their locations were not

investigated in this research. The rising water table in the most parts of the county in the

past two decades is a source of concem, and groundwater quality from the county should

be closely monitored for its various applications.

195

5.1.3 TerrvCountv

Terry County was characterized by wide variations in the reported values of TDS,

chlorides, fluorides, nitrates, and sulfates. TDS values were over the EPA-stipulated limit

of 500 mg/L in samples from 120 of the 121 wells. Wells above 1000 mg/L were located

in the north and north central portion of the county. Such high levels would not be suited

for growth of cotton, sorghum, and peanuts. Peanuts are relatively less tolerant to salinity

and have become a major crop since the 1990s. Nitrate levels were more than 44.3 mg/L

in samples from 15 of the 121 wells. Eight of these wells were in the southeast part of the

coimty. The concentrations increased from northwest to southeast. Forty-six percent of

the wells had chloride values more than 250 mg/L. Thirteen wells out of 121, most of

them in the southeastem part, were above 500 mg/L, the critical value for peanuts. Levels

above this critical limit could result in potential yield reductions. About 73% of the wells

exceeded the secondary limit of 250 mg/L for sulfates. Seventeen of the twenty-four

wells analyzed for fluoride had levels above 4 mg/L.

An interesting observation when viewing the maps was that water quality was

better in areas where irrigation was practiced than other nonirrigated lands. Farmers

might have chosen in the initial years to irrigate in areas of better quality water. One

explanation for the high TDS and chloride levels is the presence of saline basins in the

county. The county has three large basins. Most high TDS and chloride levels were

present in areas down gradient of Brownfield Lake. These lakes interestingly were all

located in lands that were not irrigated. The effect of Mound Lake, however, is

questionable as no wells were located downstream of the lake. Oil fíeld brines have likely

196

had an impact on localized chloride levels but no specific investigations were made in

this study to account for such impacts. The county, though, has had land-based oil spiUs

reported in the past, and also has a history of high oil production. The higher fluoride

concentrations could be due to the presence of fluoride minerals such as volcanic-ash

deposits in and near the aquifer. Though, most of the county has water table depths in the

50 to 100 ft range (43%), most of the abnormally high dissolved solids and chloride

values occurred in depth range less than 50 ft. The three saline lakes occupy most of this

depth range in the coimty. The increase in the water table elevations in most parts of the

county (5 to 20 ft) in the last 15 to 20 years could pose a greater threat to the groundwater

resources of the county in the coming years. The relatively thin saturated zone (60 ft) in

the region could explain the high concentrations of constituents such as TDS and

chlorides.

5.2 Recommendations

The results from this study will be of use to the TWDB and LERWPG in their

roles on groxmdwater planning and management. Farmers and other groundwater users

can identify areas of high chemical concentrations and choose to irrigate in lands that are

suitable, thus establishing the effectiveness of a tool Uke GIS. Results from this research

can be helpfiil to the fiiture positioning and areal placement of wells in the three counties.

Groundwater conservation districts can also benefit from this research as they address

water quality and quantity issues.

There was a relative inconsistency in the reporting of data in case of irrigated

197

acreages. There were relative variations in the data provided by NASS and USDA. It is

recommended that the agencies try to convert the information into a GIS database that

allows for easy workability. A comparison approach with respect to time is suggested to

view any changes in water quality over the years. This approach will identify areas where

concentrations have changed and aid in better management of the water quality. It is

recognized that the FSA and NRCS are presently moving in that direction.

Water quality analysis must in the future include broader parameters such as

pesticide compounds and radioactive elements. Also, importance must be attached to

other trace metals such as arsenic (for its drinking water effects) and boron (for irrigation

effects). The TWDB maintains a database listing analyses of infrequent constituents from

limited number of wells particular to a small area. These are constituents normally not

included in a typical water quality analysis. The practice of testing important trace metals

should be undertaken by the respective boards in the future to better address the

grovmdwater quality of the counties.

198

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