evaluation of groundwater quality
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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
2.1.2.1 General Description 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
2.1.3.1 General Description 19
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
4.4.1 Castro County 164
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
5.1.1 Castro County 194
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
4.100 Nitrate Levels >44.3 mg/L with Depth from 50 to 100 ft, Terry.County 186
4.101 Nitrate Levels >44.3 mg/L with Depth from 100 to 150 ft, Terry.County 187
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
N
-¥ U.S. Highway
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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2000
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ál 2002
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
0 2.5 5 Miles
State Highway
U.S. Highway
• Main Cities
City Limits
Figure 2.5. Main Cities in Dawson County,
15
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/- Irrigated; NJ- Nonirrigated Figure 2.6. Crop Acreages, Dawson Coxmty.
17
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18
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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100 98 •
99
29
•
30
31
34 35 T03 •
3 6 0 ^ 10437
33
57
III6O 63 59 ^ •
61
Miles
N Wells
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
Range of Values, mg/L
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
Range of Values, mg/L
• ^ 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
Rangeof Values, mg/L
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
Range of Values, mg/L
^ 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
Range of Values, mg/L
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
Range of Values, mg/L
80 - 200
] 200 - 400
400 - 600
F ^ 600 - 800
>800
• Wells
Sodium Contour
84
0 2.5 5 10 15 20 Miles
Surface created using only 1996 data (TWDB, 2002)
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
Surface created using only 1996 data (TWDB, 2002)
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
Range of Values, mg/L
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
Range of Values, mg/L
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
Range of Values, mg/L
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
Surface created using only 1996 data (TWDB, 2002)
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
Surface created using only 1996 data (TWDB, 2002)
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
Surface created using only 1996 data (TWDB, 2002)
^ 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
Surface created using only 1996 data (TWDB, 2002)
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
Surface created using only 1996 data (TWDB, 2002)
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
Surface created using only 1996 data (TWDB, 2002)
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
Surface created using only 1996 data (TWDB, 2002)
Figure 4.43. Magnesium levels, Terry County.
* Main Cities
U.S. Highway
State Highway
Wells
Range of Values, mg/L
^ , 0 - 5 0
50 - 100
[ _ I 100-150
150-200
]>200
- Magnesium Contour
110
0 2.5 5 10 15 20 Miles
Surface created using only 1996 data (TWDB, 2002)
Figure 4.44. Potassium levels, Terry County.
' ^ Main Cities
U.S. Highway
State Highway
• Wells
Range of Values, mg/L
2-5
] 5 10
] 10- 15
1 15-20
] >20
- Potassium Contour
111
0 2.5 5 10 :Miles
Surface created using only 1996 data (TWDB, 2002)
^ Main Cities
U.S. Highway
State Highway
• Wells
Rangeof Values, mg/L
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
%^|Í\S'' -.. ^ - — v ! i / ^ X :, 1*5 cíh
^ * ^
é/A% '''XSc''/'^'' •'""
, f â"' ^ '
•^^
^ * w
Í R n V ' ^
fc . „ . ' »
^^X^^^X^-""^
^ - < - ••-•'• ' ; - A - - . " ' /
í4 \ • '• f ^í-:'^'
\ V "^-42 \ / ^
* \ /Lamesa
r * J #^0'Donnell
/ ^
/ ^-
/ "^^
56 £
^^"^.•i - > „ /7 - ^ ': /
^,.^^']^'^-''^^'^ Los Ybanez y í RoV' "''''''' ^ ^ ^ V ^ *
Í A ^ ^ * ^
^^ N / V
(349) \
/ k^
/ •?•? \
/ ?'5
/^ ^5/ ^ ' p
>rr; ^ V
' -:'3 >v
Ackerly^
.<?-?:
.6A
0 2.5 10 15 20 iMiles
Total Irrigated Acreage - 33,035 acres
Source: TWDB, 2002 (based on 1994 data)
Key
v"-í-j-'.'--'j Irrigated Land
9 Main Cities
State Highway
U.S. Highway
A Wells under irrigated area
# Wells under nonirrigated area
Figure 4.47. Irrigated acreage, Dawson County.
117
% m
0 2.5 5 Miles
N + Total Irrigated Acreage - 140,350 acres
Key
Source: TWDB, 2002 (based on 1994 data)
:•. •-j Irrigated Land
9 Main Cities
StateHighway
U.S. Highway
A Wells under irrigated area
# Wells under nonirrigated area
Figure 4.48. Irrigated acreage, Terry County.
118
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121
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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d d O fO
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o d
o\
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d
o • ^
o d
o
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VO rs|
r o d
.855
Acc
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d
^ o II fe -2 s?
^ ^ n:
)n (
mg/
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Mea
n M
ax.
Min
. of
sam
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D
evia
tion
A ^ ^ ^ ^
O
5
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o U
•o <p
Rt
I
00 a ts
u ., a I
127
•s at
ed
bU
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irrig
G
1
..'*<
.'ái MÊå' Jk
i"' l Ár ' ^
• t . -L« * . -. Vt'- if. ^ J*
>Jl . . . .;• « j rH
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250
<N o o <N
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0\ o\ o\
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t ^
199
\o o\ 0\ * - . »
cô o 00
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"S ts (50
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128
r—Srøunerfield
0 2.5 10 15
Source: TWDB, 2002 (based on 1994 data)
20 Miles
Total Irrigated Acreage - 261,486 acres
N
— U.S. Highway
State Highway
9 Main Cities
[;•;.:; Irrigated Acres
A Wells under irrigated area
# Wells under nonirrigated area
— Alkalinity Contour
Figure 4.53. Alkalinity levels greater than 300 mg/L, Castro County.
129
^_&ummerfield
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5 10 15 20 Miles
Total Irrigated Acreage - 261,486 acres
N
- t -
U.S. Highway State Highway
9 Main Cities
• ; Irrigated Acres
Fluoride
A Wells under irrigated area
• Wells under nonirrigated area
Figure 4.54. Fluoride levels greater than 4 mg/L, Castro County.
130
mmerfíeld
^? • * l ? ^ . ' • • • • . : - • . • : • • • •
Source: TWDB, 2002 (based on 1994 data)
0 2.5 10 15 20 Miles
Total Irrigated Acreage - 261,486 acres
N
State Highway
U.S. Highway
9 Main Cities
;;.;;;g:/;; Irrigated Acres
RSC
A Wells under irrigated area
# Wells under nonirrigated area
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
<s o o <N
o o fS
o o o <N
OS 0\ o\
998
^
i^
199
NO OS
(/) 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
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5
Total Irrigated Acreage - 33,035 acres
Mues
U.S. Highway
State Highway
9 Main Cities
J^^ Irrigated Acres
Cl>250 mg/L
A Wells under irrigated area
# Wells under nonirrigated area
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 •
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5 10 15 20 Miles
Total Irrigated Acreage - 33,035 acres
N
-¥
U.S. Highway
State Highway
® Main Cities
;'• :í Irrigated Acres
Nitrate > 44.3 mg/L
• Wells under nonirrigated area
A Welis under irrigated area
Figure 4.58. Nitrate levels greater than 44.3 mg/L, Dawson County.
135
0 2.5 5
Source: TWDB, 2002 (based on 1994 data)
Miles
Total Irrigated Acreage - 33,035 acres
N
U.S. Highway
State Highway
• Main Cities
^
•;; ; í Irrigated Acres
# Wells under nonirrigated area
• 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
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5 10 15 20 Miles
Total Irrigated Acreage - 33,035 acres
N
-•-
U.S. Highway
State Highway
9 Main Cíties
•;•;•.:;:] Irrigated Acres
• Wells under nonirrigated area
A Wells under irrigated area
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
• Wells under irrigated area
# Wells under nonirrigated area
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
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5 10 15 20 Miles
Total Irrigated Acreage - 33,035 acres
N
U.S. Highway
State Highway
9 Main Cities
•y::-;f\ Irrigated Acres
SAR> 10
# Wells under nonirrigated area
A Wells under irrigated area
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
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5 10 15
Total hrigated Acreage - 33,035 acres
20 Miles
U.S. Highway State Highway
Main Cities
Irrigated Acres
A Wells under irrigated area
# Wells under nonirrigated area
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
Source: TWDB, 2002 (based on 1994 data)
0 2.5 Miles
Total Irrigated Acreage - 33,035 acres U.S. Highway
State Highway
• Main Cities
^ Irrigated Acres
Calcium> 200 mg/L
A Wells under irrigated area
# Wells under nonirrigated area
Fieure 4.64. Calcium levels greater than 200 mgA., Dawson County. g
144
400 ^ 600
;^0'Donnell 5
0 2.5 5
Total Irrigated Acreage - 33,035 acres
N
Source: TWDB, 2002 (based on 1994 data)
Miles
U.S. Highway
State Highway
® Main Cities
W:--':'\ Irrigated Acres
• Wells under irrigated area
• Wells under nonirrigated area
Sulfate>250 mg/L
Figure 4.65. Sulfate levels greater than 250 mg/L, Dawson County.
145
0 2.5 5 10 15
Total Irrigated Acreage - 33,035 acres
N
- f -
Source: TWDB, 2002 (based on 1994 data)
20 Miles
U.S. Highway
State Highway
Main Cities
l^':/:/--^:!^ Irrigated Acres
A Wells under irrigated area
• Wells under nonirrigated area
Iron>0.3 mg/L
Figure 4.66. Iron levels greater than 0.3 mg/L, Dawson County.
146
Source: TWDB, 2002 (based on 1994 data)
0 2.5
Total Irrigated Acreage - 33,035 acres
N
Miles
U.S. Highway
State Highway
9 Main Cities
.:vj Irrigated Acres
# Wells under nonirrigated area
A Wells under irrigated area
Alkalinity>300 mg/L
Figure 4.67. Alkalinity levels greater than 300 mg/L, Dawson County.
147
#0'Donnell
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5
Total Irrigated Acreage - 33,035 acres
Miles
U.S. Highway
State Highway
9 Main Cities
^ ^ 1 Irrigated Acres
Magnesiuni> 150mg/L
# Wells under nonirrigated area
• Wells under irrigated area
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 ^
" • • \ \ - A * -. '•••f
\S^"X' ''
' « "• ,. " 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,
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5 10 15
Total Irrigated Acreage - 33,035 acres
N
^
20 Miles
U.S. Highway
State Highway
9 Main Cities
J^ÚiWi Lrigated Acres
Percent Na> 60%
# Wells under nonirrigated area
A Wells under irrigated area
Figure 4.69. Percent sodium levels greater than 60 %, Dawson County.
149
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
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5 10 15 20 Miles
Total Irrigated Acreage - 140,350 acres
N
U.S. Highway
State Highway
® Main Cities
;.:;'; Irrigated Acres
# Wells under nonirrigated area
A Wells under irrigated area
Cl>250 mg/L
Figure 4.71. Chloride levels greater than 250 mg/L, Terry County.
152
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5 10 15 20 Miles
Total Irrigated Acreage - 140,350 acres
N
-¥
U.S. Highway
State Highway
• Main Cities
•'•::::;{ IrTÍgated Acrcs
A Wells under irrigated area
# Wells under nonirrigated area
TDS>500 mg/L
Figure 4.72. TDS levels greater than 500 mg/L, Terry County,
153
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5
Total Irrigated Acreage - 140,350 acres
U.S. Highway
State Highway
• Main Cities
W:'Mi\ hrigated Acres
# Wells under nonirrigated area
A Wells under irrigated area
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
Range of Values, mg/L
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
Range of Values, mg/L
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
A Wells under irrigated area
# Wells under nonirrigated area
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
Source: TWDB, 2002 (based on 1994 data)
0 2.5 10 15 20 Miles
Total Irrigated Acreage - 140,350 acres
N
-f-
# Main Cities
U.S. Highway
State Highway
/::?••:•] Irr igated Acres
# Wells under nonirrigated area
^ Wells under irrigated area
F>4mg/L
Figure 4.77. Fluoride levels greater than 4 mg/L, Terry County.
160
Source: TWDB, 2002 (based on 1994 data)
0 2.5 5
Total Irrigated Acreage - 140,350 acres
# Main Cities
U.S. Highway
State Highway
N
i;"í':.l- ;-.' Irrigated Acres
A Wells under irrigated area
# Wells under nonirrigated area
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
Source: TWDB, 2002 (based on 1994 data)
Miles
Main Cities
U.S. Highway
State Highway
Irrigated Acres
-I- PercentNa>60%
# Wells under nonirrigated area
A Wells under irrigated area
Figure 4.79. Percent sodium l.vels greater than 60%. Terry County,
162
Total Imgated Acreage - 140,350 acres
N
Source: TWDB, 2002 (based on 1994 data)
Miles
m Main Cities
U.S. Highway
State Highway
f/-''M:'' • Irrigated Acres
A Wells under irrigated area
# Wells under nonirrigated area
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
Percent area of total = 4.93%
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
Percent area of total = 32.2%
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
Percent area of total = 5.58%
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
Percent area greater than 44.3 mg/L = 0.03%
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
Percent area of total - 8.89%
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
Percent area of total - 14.7%
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
Percent area of total = 34.7%
Figure4.97.TDSlevels>1000m g/LwUhdepthfrom50tol00ft,TerryCounty.
183
0 2.5 5 10 15 Miles
Percent area of total = 17.7%
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
Percent area greater than 44.3 mg/L = 1.07%
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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2. Ayres, R.S., and D.W. Westcot. (1976). Water Quality for Agriculture. Irrigation and Drainage Paper No. 29. Food and Agriculture Organization of the United Nations. Rome.
3. Berka, C, D. McCalIum, and B. Wemick. (1995). Land Use Impacts on Water Quality: Case Studies in Three Watersheds. Resource Management and Environmental Studies and Department of Civil Engineering, University of British Columbia, Vancouver, BC, Canada. Presented at: The Lower Fraser Basin in transition: A symposium and workshop. May 4,1995. Kwantlen CoUege, Surrey, BC, Canada.
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5. Coleman, J. (2002). Personal Communication, South Plains Undergroimd Water Conservation District, Brownfield, Texas.
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11. Famham, D.S., R.F. Hasek, and J.L. Paul. (1985). Water Quality. Cooperative Extension, University of Califomia. Berkeley, Califomia. Leaflet 2995.
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19. Hickey, D.D., and J.R. Wright. (1990). Evaluating Groundwater PoUution Using Geographic Information Systems. Irrigation and Drainage, Proceedings of the 1990 National Conference. American Society of Civil Engineers, New York, New York.
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