Prepared in cooperation with the Bureau of Reclamation 

and U.S. Department of Energy

Optimization of Water-Level Monitoring Networks in the 

Eastern Snake River Plain Aquifer Using a Kriging-Based 

Genetic Algorithm Method

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2013–5120

DOE/ID-22224
Cover: Diagram of estimated water-table from kriging of water

levels measured in the reduced Federal-State Cooperative water-level

monitoring network (Co-op network), after removing 40 optimally

selected wells, eastern Snake River Plain, Idaho. The black x-mark and

circle symbols correspond to wells removed and retained from the Co-op

network, as of 2008, respectively. The black dashed line corresponds to the 

boundary of the Idaho National Laboratory.
Optimization of Water-Level Monitoring 

Networks in the Eastern Snake River Plain 

Aquifer Using a Kriging-Based Genetic 

Algorithm Method

By Jason C. Fisher

DOE/ID-22224

Prepared in cooperation with the Bureau of Reclamation and

U.S. Department of Energy

Scientific Investigations Report 2013–5120

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director 

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

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Suggested citation:

Fisher, J.C., 2013, Optimization of water-level monitoring networks in the eastern Snake River Plain aquifer using 

a kriging-based genetic algorithm method: U.S. Geological Survey Scientific Investigations Report 2013-5120 

(DOE/ID-22224), 74 p., http://pubs.usgs.gov/sir/2013/5120/.
iii

Contents

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

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

Previous Investigations........................................................................................................................4

Purpose and Scope ..............................................................................................................................4

Description of Study Area ...................................................................................................................5

Water-Level Monitoring Networks ....................................................................................................5

Federal-State Cooperative Water-Level Monitoring Network.............................................5

U.S. Geological Survey-Idaho National Laboratory Water-Level 

Monitoring Network.......................................................................................................6

Methods...........................................................................................................................................................6

Sources and Descriptions of Data.....................................................................................................6

Geographic Coordinates.............................................................................................................6

Water Levels.................................................................................................................................6

Interpolation of the Water Table ........................................................................................................9

Kriging Formulation .....................................................................................................................9

Semivariogram Formulation.....................................................................................................12

Semivariogram Development ..................................................................................................15

Kriging Implementation.............................................................................................................16

Prediction Uncertainty..............................................................................................................16

Cross-Validation.........................................................................................................................16

Optimization of Water-Level Monitoring Networks.......................................................................22

Planning Objective.....................................................................................................................22

Design Criteria............................................................................................................................25

Multi-Objective Problem Formulation ....................................................................................25

Genetic Algorithm......................................................................................................................27

Computer Software ............................................................................................................................28

Computer Hardware...........................................................................................................................28

Results and Discussion...............................................................................................................................28

Model Performance............................................................................................................................28

Number of Sites Removed........................................................................................................33

Kriging Grid Resolution .............................................................................................................33

Population Size...........................................................................................................................34

Elitism Rate..................................................................................................................................34

Crossover Probability................................................................................................................34

Mutation Probability..................................................................................................................34

Weighting Coefficients.......................................................................................................................35

Optimized Monitoring Networks ......................................................................................................36

Summary and Conclusions.........................................................................................................................50

Acknowledgments.......................................................................................................................................50

References Cited..........................................................................................................................................50

Appendix A. R-Package Documentation ................................................................................................73

Appendix B. Information about the R Session .......................................................................................73
iv

Figures

1. Map showing locations of wells in the Federal-State Cooperative water-level

monitoring network, eastern Snake River Plain, Idaho, 2008................................................2

2. Map showing locations of wells in the U.S. Geological Survey-Idaho National

Laboratory water-level monitoring network, Idaho National Laboratory and vicinity,

Idaho, 2008 .....................................................................................................................................3

3. Map showing standard deviation of water-level measurements for the entire

period-of-record, eastern Snake River Plain, Idaho...............................................................7

4. Map showing mean measurement error of water-level elevations, eastern 

Snake River Plain, Idaho, calendar year 2008..........................................................................8

5. Graph showing semivariogram analysis of water-level elevation residuals after

subtraction from trend ...............................................................................................................15

6. Map showing estimated water table from kriging of water levels measured in the

Federal-State Cooperative water-level monitoring network and U.S. Geological

Survey-Idaho National Laboratory water-level monitoring network, eastern 

Snake River Plain, Idaho............................................................................................................17

7. Maps showing estimated water table and uncertainty from kriging of water levels

measured in the Federal-State Cooperative water-level monitoring network,

eastern Snake River Plain, Idaho.............................................................................................18

8. Maps showing estimated water table and uncertainty from kriging of water levels

measured in the U.S. Geological Survey-Idaho National Laboratory water-level

monitoring network, Idaho National Laboratory and vicinity, Idaho..................................20

9. Scatter plots showing measured and estimated water-level elevations, and

estimation error and estimated water-level elevations, from leave-one-out

cross-validation, eastern Snake River Plain, Idaho..............................................................23

10. Map showing estimation errors from leave-one-out cross-validation, eastern

Snake River Plain, Idaho............................................................................................................24

11. Graphs showing sensitivity of the best fitness value and computational time to

changes in the number of sites removed, kriging grid resolution, population size,

elitism rate, crossover probability, and mutation probability, Federal-State

Cooperative water-level monitoring network, eastern Snake River Plain, Idaho............31

12. Graphs showing sensitivity of the best fitness value and computational time to

changes in the number of sites removed, kriging grid resolution, population size,

elitism rate, crossover probability, and mutation probability, U.S. Geological

Survey-Idaho National Laboratory water-level monitoring network, eastern 

Snake River Plain, Idaho............................................................................................................32

13. Graphs showing sensitivity of the best fitness value, root-mean-square deviation,

and percent local error to changes in the number of sites removed from the

existing Federal-State Cooperative and U.S. Geological Survey-Idaho National

Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho............39

14. Maps showing difference between kriged water-table surfaces using the existing

and reduced Federal-State Cooperative water-level monitoring network, after

removing 10, 20, 40, 60, and 80 optimally selected wells, eastern Snake River Plain,

Idaho .............................................................................................................................................40

15. Maps showing difference between kriged water-table surfaces using the existing

and reduced U.S. Geological Survey-Idaho National Laboratory water-level

monitoring network, after removing 10, 20, 40, 60, and 80 optimally selected wells,

Idaho National Laboratory and vicinity, Idaho.......................................................................45
v

Conversion Factors, Datums, and Abbreviations and 

Acronyms

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 0.0002471 acre 

square kilometer (km2) 247.1 acre

square meter (m2) 10.76 square foot (ft2) 

square kilometer (km2) 0.3861 square mile (mi2)

meter (m) 1.094 yard (yd) 

Slope

meter per kilometer (m/km) 5.28 foot per mile (ft/mi) 

Tables

1. Parameter values used for base-case conditions and the final optimizations of 

the water-table monitoring networks of the eastern Snake River Plain aquifer, 

Idaho .............................................................................................................................................29

2. Sensitivity of the genetic algorithm to incremental changes in the control

parameters, eastern Snake River Plain, Idaho ......................................................................30

3. Range of weighted-objective values in solution space (the collection of all 

possible solutions to the optimization problem) for changes in the number of 

sites to remove, eastern Snake River Plain, Idaho ...............................................................36

4. Genetic algorithm searches summarized for optimized water-level monitoring

networks, eastern Snake River Plain, Idaho ..........................................................................37

5. Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho 

National Laboratory water-level monitoring networks, eastern Snake River Plain, 

Idaho, during 2008.......................................................................................................................53

6. Wells identified for removal based on genetic algorithm searches, eastern 

Snake River Plain, Idaho............................................................................................................67
vi

Abbreviations and Acronyms

Abbreviation or

acronym

Definition

cov covariance

c sill of the theoretical semivariogram

e residual component

f individual objective function

g nugget of the theoretical semivariogram

h distance between two measurement points



h lag distance

l length of a square block side in the uniform kriging grid

m trend function

n number of measurement sites in the monitoring network

na maximum number of times a child chromosome can be aborted during crossover

ne number of measurement sites in the existing monitoring network

niter maximum number of iterations in the genetic algorithm

nn number of nodes in the kriging grid

npen number of times the penalty function is invoked

npop number of chromosomes in the population

nr number of observation wells to remove from an existing monitoring network

nrun maximum number of consecutive iterations without any improvement in the best 

fitness value

nv number of points in the empirical semivariogram

p-value probability of an observed result arising by chance

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued

Datums

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

(NAVD 88).

Vertical coordinate information for historical data collected and stored as National Geodetic 

Vertical Datum of 1929 (NGVD 29) has been converted to the commonly used NAVD 88 for this 

publication. Conversion between NAVD 88 and NGVD 29 varies spatially, and is accurate to 

within about plus or minus 2 centimeters (cm). The reader is directed to the National Geodetic 

Survey website for VERTCON at http://www.ngs.noaa.gov/TOOLS/Vertcon/vertcon.html for a detailed 

description of the height conversion methodology.

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

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

Conversion of horizontal coordinate information from North American Datum of 1927 (NAD 27) 

to NAD 83 varies spatially, and typically is accurate to within 12–18 cm. For assistance with 

conversions, the reader is directed to the National Geodetic Survey website for NADCON at 

http://www.ngs.noaa.gov/TOOLS/Nadcon/Nadcon.html.

Maps are based on the Albers Equal Area Conic projection using a central meridian of 113° 

W., standard parallels of 42° 50’ N. and 44° 10’ N., a false easting of 200,000.00 meters, and the 

latitude of the projection’s origin at 41° 30’ N.
vii

Abbreviation or

acronym

Definition

r range of the theoretical semivariogram

s Cartesian coordinate at a geographic location

s’ Cartesian coordinate at a geographic location

s0 estimation point

sn Cartesian coordinate at a node in the kriging grid

var variance

w weighting coefficient

x easterly coordinate

x decision variables

y northerly coordinate

z water-level elevation

zˆ estimated water-level elevation

orig zˆ estimated water-level elevation based on measurements from the original monitoring 

network

z* estimated water-level elevation from leave-one-out cross validation

ACO ant colony optimization

BB branch-and-bound

C penalty coefficient

Ce covariance function of the residual component

Co-op network Federal-State Cooperative water-level monitoring network

CRAN Comprehensive R Archive Network

DOE U.S. Department of Energy

E expected value

ESRP eastern Snake River Plain

F weighted sum objective function

F ' fitness function

GA genetic algorithm

GB gigabytes

GDAL Geospatial Data Abstraction Library

GTS geostatistical temporal/spatial

IDW inverse distance weighting

IDWR Idaho Department of Water Resources

INL Idaho National Laboratory

KED kriging with external drift

MAROS monitoring and remediation optimization system

Nh number of data pairs in a bin

OK ordinary kriging

P penalty function

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued

Abbreviations and Acronyms
viii

Abbreviation or

acronym

Definition

PLE percent local error

PROJ.4 cartographic projections library

R2 coefficient of determination

R set of all real numbers

R2 adjusted coefficient of determination

RAM random-access memory

Reclamation Bureau of Reclamation

RMSD root-mean-square deviation

RMSE root-mean-square error

SA simulated annealing

SVMs support vector machines

UK universal kriging

USGS U.S. Geological Survey

USGS-INL U.S. Geological Survey-Idaho National Laboratory

USGS-INL network U.S. Geological Survey-Idaho National Laboratory water-level monitoring network

Z set of all integers

X random variable

Y random variable

β deterministic trend coefficient

γ e semivariogram of the residual component



γ e empirical semivariogram of the residual component

e γˆ theoretical semivariogram of the residual component

∆h constant bin width

ε z measurement error for the water-level elevation

λ kriging coefficient

μe mean of the residual component

σ e

2 variance of the residual component

σ UK standard error from universal kriging

σ UK

2 estimated variance from universal kriging

σ z standard deviation of water-level measurements

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued

Abbreviations and Acronyms
Optimization of Water-Level Monitoring Networks in the 

Eastern Snake River Plain Aquifer Using a Kriging-Based 

Genetic Algorithm Method

By Jason C. Fisher

Abstract

Long-term groundwater monitoring networks can provide 

essential information for the planning and management 

of water resources. Budget constraints in water resource 

management agencies often mean a reduction in the number 

of observation wells included in a monitoring network. 

A network design tool, distributed as an R package, was 

developed to determine which wells to exclude from a 

monitoring network because they add little or no beneficial 

information. A kriging-based genetic algorithm method was 

used to optimize the monitoring network. The algorithm 

was used to find the set of wells whose removal leads to 

the smallest increase in the weighted sum of the (1) mean 

standard error at all nodes in the kriging grid where the water 

table is estimated, (2) root-mean-squared-error between the 

measured and estimated water-level elevation at the removed 

sites, (3) mean standard deviation of measurements across 

time at the removed sites, and (4) mean measurement error of 

wells in the reduced network. The solution to the optimization 

problem (the best wells to retain in the monitoring network) 

depends on the total number of wells removed; this number is 

a management decision. The network design tool was applied 

to optimize two observation well networks monitoring the 

water table of the eastern Snake River Plain aquifer, Idaho; 

these networks include the 2008 Federal-State Cooperative 

water-level monitoring network (Co-op network) with 

166 observation wells, and the 2008 U.S. Geological SurveyIdaho National Laboratory water-level monitoring network 

(USGS-INL network) with 171 wells. Each water-level 

monitoring network was optimized five times: by removing 

(1) 10, (2) 20, (3) 40, (4) 60, and (5) 80 observation wells 

from the original network. An examination of the trade-offs 

associated with changes in the number of wells to remove 

indicates that 20 wells can be removed from the Co-op 

network with a relatively small degradation of the estimated 

water table map, and 40 wells can be removed from the 

USGS-INL network before the water table map degradation 

accelerates. The optimal network designs indicate the 

robustness of the network design tool. Observation wells were 

removed from high well-density areas of the network while 

retaining the spatial pattern of the existing water-table map.

Introduction

Long-term groundwater monitoring networks have 

provided vital information for sustainable water resources 

management in the eastern Snake River Plain (ESRP) (figs. 1

and 2), Idaho. Data from these networks have been used to 

validate groundwater flow models, to evaluate the response 

of groundwater levels to artificial recharge efforts and 

changing climatic drivers, and to review water rights with 

respect to the long-term sustainability of aquifer resources. 

Given the high costs associated with the maintenance of 

these networks, development of efficient network designs 

is essential. The design of a groundwater-level monitoring 

network is dependent on the spatial and temporal distribution 

of water levels in the aquifer. These distributions are 

extremely complicated in the ESRP given its diverse geology, 

perched alluvial conditions that overlie the regional aquifer, 

and variable fluxes between groundwater and surface water 

(Whitehead, 1992). Care must be taken to include the many 

complex factors involved when describing the groundwater 

system; for example, statistical procedures must be used to 

simulate the water-table surface (that is, the surface where 

the water pressure head is equal to the atmospheric pressure) 

of the aquifer. A more efficient network of monitoring wells 

may be established by evaluating the value of observations 

measured at each well and the degree to which observations 

are redundant, and then removing low-value or redundant 

wells from the observation network. A heuristic optimization 

procedure for identifying these redundant wells is presented 

in this report. Heuristic is a technique for efficiently guiding 

the process of optimization; it does not guarantee that the 

best solution will be found. This study was conducted by 

the U.S. Geological Survey (USGS) in cooperation with 

the U.S. Bureau of Reclamation (Reclamation) and the U.S. 

Department of Energy (DOE).
2 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 01

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

95

96

99

102 101

104

106

107

108 110 109

111 112 113

114 115 117 116 118

119

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166

26

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85 83 81 82 84 86 87

88

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97 98

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105

2 1 3

4 5 6 7 11 12

14

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21 23 25 27 29

31

32

33 37 39 41 35 43 44 46 48 50

56 55

59 60

9 8 10

13 18 19 20

22

24 28 38

40 42 45 47 49 51 53

58

62 64

67 69 77 71

IDAHO

BOISE

EASTERN 

SNAKE RIVER PLAIN

Twin

Falls

Pocatello

Idaho

Falls

IDAHO 

NATIONAL

LABORATORY

40 Miles

0 20

0 20

40 Kilometers

Base map modified from U.S. Geological Survey 1 arc-second National 

Elevation Dataset, 2012 and other digital data, various scales; Albers 

Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; central 

meridian 113°00’W; North American Datum of 1927.

Idaho National Laboratory boundary

EXPLANATION

Eastern Snake River Plain boundary 29 Well in the Federal-State Cooperative water-level 

monitoring network, as of 2008 —Number identifies 

the observation well in tables; 166 wells were 

measured

Irrigated lands, 2008 —from the Idaho Department 

of Water Resources Geographic Information 

Systems Dataset, 2012

Figure 1. Locations of wells in the Federal-State Cooperative water-level monitoring network, eastern Snake 

River Plain, Idaho, 2008.
Introduction 3

tac13-0832_fig 02

Big Lost River

spreading areas

Big Southern

Butte

Middle

Butte

East

Butte

Atomic

City

Howe

Mud

Lake Terreton

Mud Lake

Big

River

Big Lost River

Sinks and 

Playas

Lost River

Lost

Little

113°

43°

30'

113°12' 112°48' 112°36' 112°24'

43°

36'

43°

42'

43°

48'

43°

54'

43°

24'

167

168 169

170 171

173 174 172 175 176

177

178

179

180

181

182

183 184

185

186 187

188

189

190

191

192 193 194

196 197 195 198

199

201 200 203 202 204 205206

207

208 209

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211 212 213

214 215 216 217

222 229 239

218-221

223-228

230-238 251 240-250 252 253 254 255 256 257

258 259

260

261 263 262 264 265 266 268 267 269 270 271

272

273 274 275 277 276 278 279 280 281 282 283 284 285

286 287 288 289

290 291 292

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300 298 299 301 302

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307 308

309 310

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315 316317

318

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321 322 323 324 325 326 327 328

330 329

331 332

333 334

335

104

112

Well in the U.S. Geological Survey-Idaho National 

Laboratory water-level monitoring network, as of 

2008 —Number identifies the observation well in 

tables; 171 wells were measured

10 Miles

0 5

0 5

10 Kilometers

Idaho National Laboratory boundary

EXPLANATION

Eastern Snake River Plain boundary 287

Irrigated lands, 2008 —from the Idaho Department 

of Water Resources Geographic Information 

Systems Dataset, 2012

Base map modified from U.S. Geological Survey 1 arc-second National 

Elevation Dataset, 2012 and other digital data, various scales; Albers 

Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; central 

meridian 113°00’W; North American Datum of 1927.

IDAHO

BOISE

EASTERN 

SNAKE RIVER PLAIN

Twin

Falls

Pocatello

Idaho

Falls

IDAHO 

NATIONAL

LABORATORY

Map

area

Figure 2. Locations of wells in the U.S. Geological Survey-Idaho National Laboratory water-level monitoring 

network, Idaho National Laboratory and vicinity, Idaho, 2008.
4 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

Previous Investigations

Groundwater monitoring networks can be classified into 

two categories: (1) water-quality monitoring networks, and 

(2) water-level monitoring networks. A typical objective for 

long-term monitoring of water quality is the development 

of a cost-effective design that adequately characterizes a 

contaminant plume. Examples of such networks are given 

by Grabow and others (1993), Reed and others (2000), Lin 

and Rouhani (2001), Cameron and Hunter (2002), Aziz 

and others (2003), Passarella and others (2003), Nunes and 

others (2004b), Herrera and Pinder (2005), Yeh and others 

(2006), Li and Hilton (2007), and Dhar and Datta (2010). For 

long-term monitoring of water levels, the typical objective is 

the development of a cost-effective design for water resources 

management that retains adequate overall prediction accuracy. 

Examples of such networks are given by Gangopadhyay and 

others (2001), Asefa and others (2004), Nunes and others 

(2004a), and Khan and others (2008).

Loaiciga and others (1992) classified the general 

approaches to network design into two categories: 

(1) hydrogeologic, when no advanced statistical methods are 

used; and (2) statistical, when advanced statistical methods 

are used. The statistical approach to network design can 

include numerical models of groundwater flow and transport, 

interpolation methods, and (or) statistical analysis. The 

variance-based (also known as variance reduction) statistical 

method uses the variance of the estimation error obtained from 

kriging to assess the suitability of a given network (Nunes 

and others, 2004a, 2004b). A given monitoring network has 

an uncertainty (quantified as the variance of the estimation 

error) that depends on the number and position of observation 

wells in the monitoring network. Adding wells to this network 

usually decreases uncertainty, whereas removing wells 

usually increases it. These methods systematically search for 

a set (that is, number and position) of observation wells that 

minimize the variance of the estimation error.

In previous investigations, heuristic search methods, 

including decision support tools and mathematical 

optimization, were used to identify the best set of observation 

wells in a monitoring network. Examples of decision 

support tools include the monitoring and remediation 

optimization system (MAROS) (Aziz and others, 2003) and 

the geostatistical temporal/spatial (GTS) algorithm (Cameron 

and Hunter, 2002). Decision support tools typically are 

applicable only to a specific class of problems; therefore, 

mathematical optimization techniques that are applicable 

to a variety of problems have been adopted much more 

widely for network design problems. Reducing the number 

of observation wells in an existing monitoring network is a 

non-linear combinatorial problem and, therefore, is well suited 

for heuristic algorithms. Genetic algorithms (GAs), simulated 

annealing (SA), support vector machines (SVMs), and 

colony optimization (ACO), and the branch-and-bound (BB) 

algorithm are search heuristics that have been used to optimize 

long-term-monitoring networks. For example, Reed and 

others (2000) optimized monitoring networks using inverse 

distance weighting (IDW) and ordinary kriging (OK) using 

heuristic GAs and simulation models. Similarly, Nunes and 

others (2004a) used SA with statistical methods to maximize 

spatial accuracy and to minimize temporal redundancy of 

a large groundwater monitoring network. Asefa and others 

(2004) present a methodology based on SVMs for designing 

a water-level monitoring network that identifies observation 

well locations based on their importance in explaining the 

potentiometric surface. Li and Hilton (2007) used an ACO 

algorithm with IDW to derive a reduced configuration of a 

trichloroethylene concentration monitoring well network. Dhar 

and Datta (2010) developed a methodology for designing a 

water-quality monitoring network by formulating the problem 

as a logic-based mixed-integer linear optimization model and 

solving it using the BB algorithm.

Purpose and Scope

The purpose of this report is to present a heuristic 

network design tool for optimizing long-term monitoring 

networks. This tool is applied to determine cost-effective 

designs for two preexisting water-level monitoring networks 

in the ESRP aquifer. Monitoring costs are reduced by 

eliminating data redundancy in the existing networks—that is, 

by removing observation wells that have little-to-no effect on 

the overall water table characterization. A genetic algorithm is 

used to search for the optimal network design using multiple 

objectives to evaluate candidate subsets of observation 

wells within an existing monitoring network. The design 

objectives considered are to: (1) minimize the interpolation 

error in the water-table map, (2) preserve local anomalies 

in the water-table surface, (3) preserve the variability of 

water-level measurements over time, and (4) maintain wells 

with higher measurement accuracy. The individual objectives 

are combined into a single composite objective function. 

The spatial interpolation technique, universal kriging (UK), 

is embedded in the optimization formulation for estimating 

water-level elevations at unmonitored locations. The total 

number of wells to remove from the original network is 

a management decision. Network efficiency is expected 

to change as more data and resources become available; 

therefore, a reexamination of the network design every few 

years may help determine the effectiveness of a groundwater 

monitoring program. The network design tool is applied 

separately to each water-level monitoring network in 

the ESRP.
Introduction 5

The model of the semivariogram, which describes 

the spatial variability of the water table and is used as 

input for UK, is calculated using 2008 median water-level 

measurements in observation wells of both monitoring 

networks. Because of budgetary constraints on the 

groundwater monitoring programs shortly after 2008, this 

period was selected. The optimized monitoring networks 

provided in this report can be used to evaluate observation 

well reductions made since 2008 through a comparison of the 

optimized networks with the networks currently (2013) in use, 

and to facilitate the design of future groundwater monitoring 

networks in the ESRP aquifer.

Description of Study Area

The study area is the ESRP in Idaho, a relatively flat 

topographic depression, about 270 km long and 100 km wide 

(fig. 1). Land surface elevations range from about 700 m 

above the North American Vertical Datum of 1988 (NAVD 88) 

near the Thousand Springs area in the southwest to more than 

2,000 m near the Yellowstone Plateau in the northeastern 

part of the plain. The ESRP crosses the roughly northwest 

fault-bounded mountain ranges of eastern Idaho from 

southwest to northeast (fig. 1). The steep mountain ranges 

bordering the plain are about 2,000–3,500 m in elevation, 

and collect as much as 150 cm per year of snow during the 

winter, which melts in late spring and early summer. The 

ESRP is a semiarid sagebrush steppe with warm summers 

and cold winters, and precipitation averaging 20 cm per year 

(Garabedian, 1992, p. 2).

The ESRP is the track of the time-transgressive 

Yellowstone Hotspot super-caldera eruptions (Pierce and 

Morgan, 1992). Each caldera system began as a high geoid 

anomaly, followed by several super-caldera eruptions of 

mostly rhyolitic material. After each super-caldera eruptions 

ceased, a quiescent period followed, during which basalt 

erupted in response to continued high heat flow. Post 

super-caldera, thermal contraction, and emplacement of a 

mafic sill in the mid-crust caused continuing subsidence 

(Rodgers and others, 2002).

The entire ESRP is subsiding, although subsidence is 

not uniform and localized depocenters collect sediment and 

are thought to have considerable control on groundwater 

movement in the aquifer (Fisher and Twining, 2011, p. 36). 

The Idaho National Laboratory (INL) occupies a prominent 

depocenter, informally named the Big Lost Trough, which is 

bounded on the north, east, and south by volcanic highlands. 

The Big Lost Trough may have as much as 15 percent 

sediment interbedded between basalt flows (Anderson 

and Liszewksi, 1997). Sediment also accumulates along 

the margins of the ESRP and is typically accompanied by 

agriculture in these areas (fig. 1).

The ESRP aquifer generally is considered an unconfined 

aquifer; however, sediment layers near the INL behave 

as confining units (Fisher and Twining, 2011, p. 34). 

Groundwater flows in a regionally southwest direction and 

discharges mainly through large springs and seeps along the 

Snake River in the Thousand Springs area in the southwestern 

part of the plain (fig. 1). Groundwater moves horizontally 

through basalt interflow zones, and vertically through joints 

and interfingering edges of interflow zones. Infiltration of 

surface water, heavy pumpage, geologic conditions, and 

seasonal fluxes of recharge and discharge locally affect the 

movement of groundwater in the aquifer. Recharge to the 

aquifer is from infiltration of precipitation, groundwater inflow 

from tributary drainages, infiltration of surface water diverted 

for irrigation, and stream and canal losses (Garabedian, 1992, 

p. 11). Land irrigated with groundwater on the ESRP is along 

the southeastern and southern margins of the plain, from north 

of Idaho Falls to west of Twin Falls, and in the Mud Lake area 

northeast of the INL (figs. 1 and 2) (Ackerman and others, 

2006, p. 6).

Water-Level Monitoring Networks

Long-term water-level monitoring networks were 

established in the ESRP aquifer to identify changes in storage 

and the general rate and direction of groundwater flow in the 

aquifer. In this study, only water levels representative of the 

water table in the ESRP aquifer were important. For example, 

observation wells screened across or just below the water table 

are excellent indicators of the water-table elevation, whereas, 

wells screened in locations of perched groundwater or deeper 

confined aquifers are poor indicators and were excluded from 

the analysis of the monitoring network.

Federal-State Cooperative Water-Level 

Monitoring Network

The Federal-State Cooperative water-level monitoring 

network (Co-op network) is administered by the USGS, 

Reclamation, and the Idaho Department of Water Resources 

(IDWR). At the end of 2008, water-level elevations in 

166 observation wells were measured annually, semi-annually, 

quarterly, bi-monthly, or monthly by the USGS and 

Reclamation. The spatial distribution of these wells in the 

ESRP is shown in figure 1. Observation wells cover most of 

the plain, except for the INL. Site information (such as, local 

name, map number, and site number) for each well in the 

network is given in table 5 (at back of report).
6 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

U.S. Geological Survey-Idaho National 

Laboratory Water-Level Monitoring Network

The U.S. Geological Survey-Idaho National Laboratory 

(USGS-INL) water-level monitoring network (USGS-INL 

network) is administered by the USGS-INL Project Office in 

cooperation with the DOE. At the end of 2008, water-level 

elevations in 171 observation wells were measured annually, 

semi-annually, quarterly, or monthly by the USGS. The spatial 

distribution of these wells in and near the INL is shown in 

figure 2. The west, east, north, and south bounding coordinates 

of these wells are about 113°17ꞌ, 112°17', 43°57’, and 43°19', 

respectively. USGS-INL network coverage is densest around 

INL facilities. Site information for each well in the network is 

given in table 5.

Methods

Sources and Descriptions of Data

Existing sources of information for the observation 

wells include: geographic coordinates (that is, longitude, 

latitude, and elevation) of the land surface reference point 

for water-level measurements (measurement point); and 

depth-to-water measurements. Depth-to-water measurements 

are easily converted to water-level elevations by subtracting 

depth-to-water measurements from the elevation at 

land surface.

Geographic Coordinates

Methods for determining the spatial location of the 

land surface reference point of a well varied throughout 

the networks. The least accurate geographic coordinates 

were interpolated from USGS topographic maps and were 

accurate to about plus-or-minus (±) 30 m (or 1 arc-second) 

in the horizontal direction and to about ±3.66 m in the 

vertical direction. The most accurate coordinate positions 

were determined by a professional land surveyor licensed 

in the State of Idaho using a Differential Global Positioning 

System. Surveyed positions were accurate to about ±0.3 m 

(0.01 arc-second) in the horizontal direction and ±0.003 m 

in the vertical direction. The horizontal position is expressed 

in latitude and longitude in conformance with the North 

American Datum of 1983 (NAD 83). The vertical position is 

expressed as the elevation above the NAVD 88. Geographic 

coordinates of the land surface reference point at each 

observation well are given in table 5. For wells in both 

monitoring networks (the number of sites [n] = 335), the mean 

reference-point error is 0.36 m, with a standard deviation of 

0.78 m.

Water Levels

Water levels were obtained by subtracting the depth to 

water from the elevation of the land-surface measurement 

point. Water-depth measurements were obtained by the 

USGS using steel or electric measuring tapes. The depth to 

the water level below the land-surface reference point were 

accurate to ±0.01 m (±0.02 ft), although a few measurements 

obtained under less-than-ideal conditions (for example, 

when condensation accumulated in the borehole) had errors 

greater than ±0.01 m. The period of record and frequency 

of monitoring is variable for each observation well, with 

the earliest water-levels recorded in 1922. The historical 

variability of water-level measurements in a well over 

the entire period of record is described with the standard 

deviation (σ z ) (table 5). The standard deviation is a measure 

of the seasonal fluctuations in the water table as well as 

long-term trends. Standard deviations are shown spatially 

and proportionally in figure 3. For wells in both monitoring 

networks, the standard deviation ranged from 0.15 to 10.31 m, 

with a median value of 1.55 m. These standard deviations are 

small relative to the range of measured hydraulic heads across 

the region.

Water-level data obtained during the 2008 calendar 

year were used to estimate the water-table surfaces. In 2008, 

the number of measurements collected in each well ranged 

from 1 to 63, with a mean of 5. The 2008 median water-level 

elevation was determined for each well and expressed as 

an elevation above the NAVD 88 (table 5). Summing the 

measurement errors of measurement point elevation and 

depth-to-water gives the measurement error of the waterlevel elevations. This error estimate assumes that the 

borehole is vertical. The mean measurement error for 2008 

water-level elevations was determined for each observation 

well (table 5, fig. 4) and ranged from 0.01 to 3.66 m, with a 

median value of 0.01 m. Mean measurement errors greater 

than 0.03 m in certain observation wells were because of the 

large measurement error for the elevation of the land surface 

reference point at these wells.
Methods 7

tac13-0832_fig 03

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

40 Miles

0 20

0 20

40 Kilometers

0.15 (minimum)

1.07 (25th percentile)

1.55 (median)

2.10 (75th percentile)

10.31 (maximum)

Standard deviaiton of water-level elevation 

measurements for period-of-record (σz

) —in meters.

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927.

Idaho National Laboratory boundary

EXPLANATION

Eastern Snake River Plain boundary

Figure 3. Standard deviation of water-level measurements for the entire period-of-record (duration varies at 

each well site), eastern Snake River Plain, Idaho.
8 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 04

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

40 Miles

0 20

0 20

40 Kilometers

Mean water-level elevation measurement 

error for 2008 (εz

)— in meters.

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927.

EXPLANATION

0.01 (minimum)

0.01 (25th percentile)

0.01 (median)

0.03 (75th percentile)

3.66 (maximum)

Idaho National Laboratory boundary

Eastern Snake River Plain boundary

Figure 4. Mean measurement error of water-level elevations, eastern Snake River Plain, Idaho, calendar year 

2008.
Methods 9

Interpolation of the Water Table

The geostatistical technique known as kriging is used 

to interpolate and extrapolate the water-level elevation at 

unmeasured locations in the ESRP aquifer. Snyder (2008, 

p. 19) describes kriging as a type of spatial moving average, 

where the value at an unmeasured location is estimated as 

a weighted average of the measured values. The weights 

assigned to the measured values depend on spatial trends 

and possible correlations in the data (Bossong and others, 

1999, p. 4). Correlation between measurements at two sites 

is assumed to depend on the separation distance between 

the two sites. Generally, measurement sites that are close 

together have a smaller difference in measured values 

than those farther apart. The degree of spatial correlation 

is quantified with the experimental semivariogram, which 

measures correlation between measurements as a function of 

distance between the measurement points. Kriging computes 

an estimate best representing the spatial distribution of the 

measured values based on a semivariogram model that is 

fitted to the experimental semivariogram and a minimization 

of the estimation variance (or estimation error) at measured 

locations. Each estimate is accompanied by a corresponding 

standard error.

One of the key assumptions of kriging is that of 

stationarity (Isaaks and Srivastava, 1989, p. 349). Stationarity 

requires that the expected value (that is, the mean) of the data 

being estimated does not change when shifted in space, and 

that the modeled semivariogram is the same everywhere. 

This assumption is violated whenever there is a significant 

spatial trend in the measured values. For example, measured 

water-level elevations in the ESRP aquifer show a consistent, 

decreasing trend in the southwest direction (Lindholm and 

others, 1988). In such cases, nonstationarity can be accounted 

for in the data by use of a trend model. The trend model used 

for this report is a simple polynomial function (planar) fit 

to the data using linear regression1. Residuals are obtained 

by subtracting the trend from the measured data. Because 

the residuals should be stationary, kriging is applied to the 

residuals and the resulting estimate is added back to the trend 

to compute an estimate of the measured values. This method 

of kriging with a trend model is known in geostatistics as 

universal kriging (UK) (Pebesma, 2004).

Kriging Formulation

A UK model was used for point estimates of water-level 

elevations in the ESRP aquifer. A general description of 

the kriging formulation is provided in many geostatistical 

texts such as Isaaks and Srivastava (1989, p. 278–322) and 

Kitanidis (1997, p. 125–127). A UK model is formulated here 

to describe water-level estimates in this study.

The UK model represents the water-level elevations 

as wavering about a deterministic function (or trend), and 

information about the scale and intensity of fluctuations 

about this trend is provided by a random function with zero 

mean and a correlation structure (Kitanidis, 1997, p. 120). In 

mathematical terms, this is expressed as:

z m ( )s s = ( ) + e( )s (1)

where

s is a pair of Cartesian coordinates describing 

the geographic location (point);

z(s) is the median water-level elevation for the 

2008 measurements at point s, in meters 

above NAVD 88;

m(s) is the deterministic part of z at point s, in 

meters above NAVD 88; and

e(s) is the stochastic part of z at point s, in meters.

A boldface algebraic symbol (such as, s in equation 1) is used 

to denote a vector quantity. The deterministic function m(s), is 

called the trend and is defined as the expected value (E) of the 

water-level elevations, denoted by:

E z m ( )s s  

 = ( ). (2)

Trend is modeled as a linear polynomial function and is 

defined as the least-squares fit of a planar surface to the 

measured data, described by:

m x ( )s s = + β β ( ) + β y ( )s 0 1 2 (3)

where

x(s) is the x coordinate (or easterly value) at 

point s, in meters;

y(s) is the y coordinate (or northerly value) at 

point s, in meters;

β0 is a deterministic unknown trend coefficient, 

in meters above NAVD 88; and

β1 , β2 are deterministic unknown trend coefficients.

1

 An external drift function using topographic elevation from a digital 

elevation model was also considered for this analysis. Kriging with an external 

drift (KED) assumes that the water table is a subdued replica of the topography. 

The relatively large permeability and low areal recharge rate of the ESRP 

aquifer indicate that the water table is essentially unrelated to the topography 

(Haitjema and Mitchell-Bruker, 2005, p. 786); thus, invalidating a key 

assumption of KED.
10 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

To obtain an estimate of z at a point s0 (an estimation point) from water-level elevation 

measurements z(s1), z(s2),..., z(sn), the following are required:

1. The estimate is a linear function of measured data, that is:

0 ii ( ) i 1

ˆ( )

n

z z =s s = ∑λ (4)

where

0 zˆ( ) s is the estimate of z at point s0,

z(si) is the measurement of z at point si,

λi is the coefficient corresponding to well site i, and

n is the total number of measurement sites.

2. The estimate at measured points is unbiased, that is:

E ( ) ( ) 0. [z z ˆ s s 0 0 − =] (5)

3. The estimated variance in square meters at point s0 ( ( σ )) UK

2

0s should be as small as 

possible, where variance is defined as:

( ) ( ) ( ) ( )

2 2

UK 0 0 0 E . z z ˆ = −     σ s ss (6)

The unbiased condition (equation 5), combined with the estimate in equation (4) and the trend 

model in equation (3) becomes:

( ) ( )

( ) ( )

( ) ( )

( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

( ) ( )

ii 0

i 1

ii 0

i 1

ii 0

i 1

i0 1 i 2 i 0 1 0 2 0

i 1

0 i01 i i 10 2 i i 2 0

i 1 i 1 i 1

0 i 1 ii 0

i 1 i 1

E 0

EE 0

0

0

0

1

n

n

n

n

n n n

n n

z z

z z

m m

xy x y

xx y y

x x

=

=

=

=

= = =

= =

  − =    

   − =   

− =

 + + −+ + =       

−+ − + − =

 

−+ −    

∑

∑

∑

∑

∑∑ ∑

∑ ∑

λ

λ

λ

λβ β β β β β

β λβ β λ β β λ β

βλ βλ

s s

s s

s s

ss s s

ss s s

s s 2 ii 0 ( ) ( ) i 1

0.

n

y y =

  

+ −=       β λ ∑ s s

(7)

For this condition (equation 7) to hold for any values of β0, β1, and β2 requires that:

λ λ i λ i

i

i

i i

i

i and = = =

∑ ∑ = ( ) = ( ) ∑ ( ) = ( ) 1 1

0

1

0 1

n n n

, , x x s s y y s s . (8)
Methods 11

The estimated variance (equation 6), combined with the estimate in equation (4), becomes:

σ λ UK i

i

E i

2

0

1

0

2

( ) s s = ( )− ( ) s





 

















 =  ∑

n

z z . (9)

Adding and subtracting the trend (m) from the estimated variance (equation 9) gives

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

( ) ( )

( ) ( )

2

2

UK 0 i i 0 0 0

i 1

2

ii i i 0 0

i 1 i 1

2

ii i 0 0

i 1

2

ii 0

i 1

ii 0 i

i 1 i 1

E

E

E

E

E

n

n n

n

n

n n

zzmm

z mz m

zm z m

e e

ee e

=

= =

=

=

= =

   

= −+ −        

   

= − −+        

    = − −−              

    =   −      

  = −    

∑

∑ ∑

∑

∑

∑ ∑

σ λ

λ λ

λ

λ

λ λ

s ss s s

s ss s

ss s s

s s

ss s( ) ( )

( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

i 0

2

2

i i ii 0 0

i 1 i 1

2

ij i j i i 0 0

i1 j1 i 1

2

ij i j i i 0 0

i1 j1 i 1

E 2

E 2

E 2E E .

n n

n n n

n n n

e

e ee e

ee ee e

e e ee e

= =

= = =

= = =

      −      

   

=−+        

 

= −+    

= −+            

∑ ∑

∑∑ ∑

∑∑ ∑

λ λ

λ λ λ

λ λ λ

s

s ss s

ss ss s

s s ss s

(10)

For random variables X and Y, the covariance (cov) between variables is defined as:

cov E ( ) X Y, . = [ ] XY − E E [ ] X Y[ ] = − E E ( ) X X[ ] ( ) Y Y − E[ ]  

 (11)

Therefore, between any two points, s and s′, the covariance of the residual component in square 

meters is denoted as Ce(s, s′

). Rewriting equation (10) in terms of Ce gives:

σ λ UK iλ λ

j

j e i j

i

i

i

e i e

2

0

1 1 1

0 0 0 ( ) s s = ( ) s s − 2 ( ) s s + s = = =

∑ ∑ ∑

n n n

C C , , C ( ) , . (12)

Where Ce is equal to the variance of e in square meters (σ e

2 ) minus the semivariogram of e in 

square meters ( γ e ), or:

C C e e e e e ( ) s s, , ′ = ( ) s s − ( ) ss ss , , ′ = − ( )′ . 0 0

2 γ σ γ (13)

The estimated variance (equation 12) is then expressed using the semivariogram as:

σ λ λ σ γ λ σ γ UK i

j

j e e i j

i

i

i

e e i

2

0

1

2

1 1

2 ( ) s s = −  ( ) s s 2  

 − − = = =

∑ ∑ ∑

n n n

, ,s0

2  ( )  

 +σ e . (14)
12 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

Coefficients λ λ1 2 , ,,λn are estimated by minimizing the expression of equation (14) subject to 

the linear constraints of equation (8), or:

minimize i

j

j e e i j

i

i

i λ λ λ

λ λ σ γ λ 1 2 1

2

1 1

2 , , ,,  n

nn

∈ = = =

∑∑ − ( ) 

  − R

s s

n

n

n

x x

∑

∑

∑

 − ( )  

 +

=

( ) =

=

=

σ γ σ

λ

λ

e e i e

i

i

i

i

i

subject to:

2

0

2

1

1

1

s s

s

,

s

s s

0

1

0

( )

( ) = ( ) =

∑λi

i

i

n

y y

(15)

where

R is the set of all real numbers.

Once the λ coefficients have been determined, they are substituted back into equations (4) and 

(14) to determine estimates of the water-level elevation and variance, respectively. The 

calculated standard error ( ) σ UK in meters is equal to the square root of the estimated variance. 

Universal kriging requires that the semivariogram of the residual component (γ e ) be known 

beforehand. 

Semivariogram Formulation

The semivariogram is used to characterize the degree of spatial correlation present in 

the data. The semivariogram is developed for e, the residual component of equation (1). The 

residual is obtained by combining equations (1) and (3):

e z ( )ssss = ( ) − m z ( ) = ( ) − − β β 0 1 x y ( )s s − β2 ( ). (16)

Coefficients β0, β1, and β2 are estimated through linear regression analysis by minimizing the 

sum of the squared difference between the measured values (z) and the linear trend model (m), 

or:

minimize i i i

i β β β

β β β 0 1 2

0 1 2

2

1 , ,

. ∈ =

 ( ) − − ( ) − ( )   ∑  R

z x y

n

s s s (17)

The mean is the expected value (E) of the residuals denoted by:

μe ( )s s = E  ( )  

 e . (18)
Methods 13

Kriging assumes that the residuals are mean-centered; therefore, the mean of the residuals is 

denoted by:

E e( )s  

 = 0. (19)

The semivariogram γ e is defined as one-half the variance (var) of the difference between 

residuals at points s and s′:

γ e ( ) s s, ( ′ = − var[ ] s s ) ( ′) . 1

2

e e (20)

For a random variable X with an expected value E[X], the variance of X is defined as:

var E ( ) X X = − ( ) E E [ ] X X E X . 

  = 

  − ( ) [ ] 2 2 2

(21)

Therefore, equation (20) may be expressed as:

γ e ( ) E E

E

s s ss ss

s

, ( ) ( ) ( ) ( )

( )

′ = − ( ) ′  

 { } − − ( ) [ ] ′

= −

1

2

1

2

2 2

ee ee

e e( ) ( ) ( )

( ) ( ) (

( ) ′  

 { } − ( ) [ ] − [ ] ′

= − ( ) ′  

 −

s s s

s s

2 2

1 2

2

0

E E

E

e e

{ } e e −

= − ( ) ′  



0

1

2

2

2

)

E e e ( )s s( ) .

 (22)

To facilitate the semivariogram estimation, it is assumed that the semivariogram depends only 

on the distance between pairs of measurement points, that is:

γ γ e e ( ) s s ( ) h ,

h x x y y

, ′ =

= ( ) ′ − + ( ) ′ − 2 2

(23)

where

h is the distance measured between point pairs s and s′, in meters;

x, y are coordinates where measurements were taken (s points); and

x′, y′ are coordinates where measurements were taken (s′ points).

The semivariogram (equation 22) may then be expressed as:

γ e ( ) s s, ( ′ = − E ( ) s s ) ( + h) . 

 

1

2

2

e e (24)
14 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

The empirical semivariogram (or sample semivariogram) ( 

γ e ), a nonparametric estimate 

of the semivariogram, is computed by grouping γ e ( ) h values, which are in a given h interval 

(or bin). The squared difference in residuals is averaged for point pairs separated by a 

distance that is contained within the same bin (lag distance). Assuming isotropic conditions 

(the orientation of the linear segment that connects two points is neglected), the empirical 

semivariogram is defined as:

     γ e

h

i i

i

( ) ( )for hh

h

s s h h h

N

= − [ ] + − ≤ < + =

∑ 1

2

1

2

1

2

2

N 1

e e ( ) ( ) , ∆h h ∆

∆∆∆∆

h

hhhh

,

let ,,,, h

 = ... 











1

2

3

2

5

2

7

2

(25)

where 

h is the lag distance (or the bin midpoint) at which the empirical semivariogram 

is computed, in meters;   γ e ( ) h is the empirical semivariogram, in square meters;

∆h is a constant bin width, in meters; and

Nh is the number of data pairs in each bin.

The empirical semivariogram is modeled with a continuous function that represents a 

theoretical semivariogram ( e γˆ ). A spherical model was selected for this report to represent the 

theoretical semivariogram, expressed as:

( ) ( )

3

e

, for 

3 1 ˆ , for 0 2 2

0, for 0

c h r

h h h g cg h r r r

h

 >

      = + − −    < ≤          

 =

γ (26)

where

h is the lag distance in meters;

c is the sill (or upper bound of the semivariogram), in square meters;

g is the nugget (or the semivariance of the residual at a lag distance of zero), in 

square meters; and

r is the range (or lag distance at which the semivariogram reaches the sill), in 

meters.

The range indicates the distance over which data are correlated (either positively or 

negatively). Non-linear regression is used to fit the sill and range coefficients in the theoretical 

semivariogram (equation 26); whereas, the nugget is set by visual inspection. The regression is 

stated in the following optimization formulation:

( ) ( ) v 2

h,j e j e j c,r 1

minimize ˆ

n

j

Nh h ∈ =

  − R ∑  

   γ γ (27)

where

nv is the number of points in the empirical semivariogram.
Methods 15

Semivariogram Development

The semivariogram (γ e in equation 14) was developed 

using residual water-level elevations with respect to a regional 

spatial trend (equation 16); in this study, trend is represented 

as a planar function of coordinate variables. Regression 

analysis was used to estimate values for the coefficients of the 

planar trend model from the measured water-level elevations 

(β0 = 951 2. m, β1 = 0 00142 . , β2 = 0 00046 . ) (equation 17). 

An adjusted coefficient of determination [ R2 ] equal to 

0.86 (probability of an observed result arising by chance 

[p-value] = 2.2 × 10-16) indicates that the trend model fits 

the data well and that the assumption of stationarity is valid. 

Wells in both monitoring networks (n = 335) were included in 

the analysis. Residuals calculated as the difference between 

the actual measurements and the trend range from -215.0 to 

236.0 m, with a mean and standard deviation of 0.0 m and 

39.1 m, respectively. 

Type:

Nugget (square meters):

Sill (square meters):

Range (meters):

Theoretical model parameters

Spherical

0

1,949

153,891

Theoretical semivariogram (R2=0.82)

Empirical semivariogram

Sample variance (σe

2

 = 1,531 square meters)

EXPLANATION

Lag distance (h), in meters

0 25,000 50,000 75,000 100,000 125,000 150,000

Semivariance of the residual water-level elevation with respect to a

linear spatial trend [γe (h)], in square meters

0

500

1,000

1,500

2,000

2,500

3,000

3,904

3,752

3,584 4,152

3,946

3,739

3,712

3,720

3,221

3,221

2,580

1,997

1,335

1,565

154

Figure 5. Semivariogram analysis of water-level elevation residuals after 

subtraction from trend. Numbers next to symbols refer to the number of 

sampled data pairs in a lag-distance interval (Nh).

The empirical semivariogram of the residuals based on 

a constant bin width (∆h = 10,000 m) is shown in figure 5. 

The spatial separation distance to which point pairs are 

included in the semivariance estimates is 150 km (about half 

of the maximum separation distance between point pairs). 

There appears to be no nugget, indicating that continuity 

of the water-level elevation is high over short distances. 

Points in the empirical semivariogram are fit with a spherical 

model (theoretical semivariogram) with nugget fixed at 

zero (g = 0 m2, c = 1,949 m2, r = 153,991 m; coefficient of 

determination [R2] = 0.82) (equation 26, fig. 5). Semivariance 

values less than the computed sample variance of the residual 

components (σ e

2

 = 1,531 m2) are positively correlated and 

values greater than the sample variance are negatively 

correlated. 
16 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

Kriging Implementation

Kriging analysis was used to interpolate water levels at 

points in a uniform grid (kriging grid) oriented north-south 

and east-west with square blocks. The spatial resolution of 

a kriging grid is specified using the length of a square block 

side in the grid (l). A spatial resolution of l equal to 500 m 

is used for all map figures in this report. Selection of this 

grid resolution was based on the inherent spatial variability 

of observation wells in the ESRP. Three simulations of the 

water-table surface were run using water-level elevations 

measured from wells in: (1) the Co-op and USGS-INL 

networks (n = 335), (2) the Co-op network (n = 166), and 

(3) the USGS-INL network (n = 171). The area of analysis 

for simulations (1) and (2) is defined by the generalized 

boundary of the ESRP (fig. 1) (number of nodes in the kriging 

grid [nn] = 112,325; area = 28,081 km2). The estimated 

water-table surface (at 50-m contour intervals) based on 

water-level measurements from wells in both networks is 

shown in figure 6. The interpolated water table (ranging from 

872.9 to 1,742.8 m) is consistent with other water-table maps 

constructed for the ESRP aquifer for 1928–30, 1956–58, 

and 1980 (Stearns and others, 1938; Mundorff and others, 

1964; Lindholm and others, 1988). The estimated water-table 

surface (at 50-m contour intervals), based on water-level 

measurements from wells only in the Co-op network, is shown 

in figure 7A. A comparison of this water-table map with the 

map derived from wells in both networks (fig. 6) shows large 

differences in the shape of the water-table contours beneath 

the INL and vicinity. 

For kriging of water-level elevations measured from 

wells in the USGS-INL network (simulation 3), the area of 

analysis is defined by the part of the ESRP beneath the INL 

and vicinity (nn = 18,394; area = 4,599 km2). The interpolated 

water-table surface is described using a 5-m contour interval 

(fig. 8A); the kriging-based water-table map is consistent with 

a March–May 2008 water-table map for this area constructed 

previously using a multilevel B-splines interpolation technique 

(Fisher and Twining, 2011, fig. 4).

Prediction Uncertainty

An advantage of kriging (over other interpolation 

algorithms such as IDW and splines) is that every estimate of 

the water-level elevation is accompanied by a corresponding 

measure of the uncertainty associated with the estimate (that 

is, the standard error, or square root of the estimated variance; 

equation 14). Values of standard error are basically a scaled 

version of the distance to the nearest measurement point; 

for example, standard error is zero at measured points and 

increases as the density of the monitoring network decreases. 

Standard error for kriging based on water levels measured 

from wells in the Co-op network ranged from 1.5 to 31.4 m 

(fig. 7B). The west-central part of the ESRP shows significant 

uncertainty resulting from a scarcity of Co-op network wells 

in this area. Standard error for kriging based on water levels 

measured from wells in the USGS-INL network ranged from 

0.6 to 31.0 m (fig. 8B); uncertainty is greatest in the southeast 

and northeast corners of the kriging grid, areas without 

USGS-INL network wells where extrapolated predictions are 

subject to greater uncertainty.

Cross-Validation

Cross-validation is a specialized resampling procedure, 

used here to (1) indicate if there are significant flaws in the 

kriging model, and (2) identify locations where the water table 

is most dynamic. The resampling procedure (David, 1976; 

Delfiner, 1976), known as leave-one-out cross-validation, 

uses all water-level measurements to estimate the spatial 

trend (equation 3) and the theoretical semivariogram model 

(equation 26). Leave-one-out cross-validation removes one 

site from the data set (well sites in both monitoring networks 

are included in this data set, n = 335) and estimates the 

water-level elevation at that location by kriging with the 

remaining data. The estimation error, that is, the difference 

between the actual (z) and estimated values (z*) at the location 

of the omitted site (z - z*), is then computed (table 5). This 

procedure is repeated for each site in the data set.
Methods 17

tac13-0832_fig 06

Estimated 2008 median water-level elevations from the application of kriging (zˆ)—in meters above North American

Vertical Datum of 1988.

900 1,000 1,100 1,200 1,300 1,400 1,500 1,600 1,700

1,300

1,400

1,500

1,600

1,700

1,200

1,100

1,000

900

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Lake

Walcott

Camas Creek

40 Miles

0 20

0 20

40 Kilometers

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; central 

meridian 113°00’W; North American Datum of 1927. Uniform kriging-grid block size 

(resolution) of 500 meters by 500 meters.

EXPLANATION

Idaho National Laboratory boundary

Eastern Snake River Plain boundary

Water table based on the Co-op and USGS-INL networks

Well in the Federal-State Cooperative water-level

monitoring network (Co-op network)—164 wells

Well in the U.S. Geological Survey-Idaho National

Laboratory water-level monitoring network

(USGS-INL network)—169 wells

Well in both the Co-op and USGS-INL networks

—2 wells

Figure 6. Estimated water table from kriging of water levels measured in the Federal-State Cooperative waterlevel monitoring network and U.S. Geological Survey-Idaho National Laboratory water-level monitoring network, 

eastern Snake River Plain, Idaho.
18 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 07a

Estimated 2008 median water-level elevations from the application of kriging (z)—in meters above North American 

Vertical Datum of 1988.

ˆ

A. Water table based on the Co-op network

900 1,000 1,100 1,200 1,300 1,400 1,500 1,600 1,700

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Lake

Walcott

Camas Creek

40 Miles

0 20

0 20

40 Kilometers

EXPLANATION

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; central meridian 

113°00’W; North American Datum of 1927. Uniform kriging-grid block size (resolution) of 

500 meters by 500 meters.

Idaho National Laboratory boundary

Eastern Snake River Plain boundary Well in the Federal-State Cooperative water-level

monitoring network (Co-op network) — 166 wells

1,300

1,400

1,500

1,600

1,700

1,200

1,100

1,000

900

Figure 7. (A) Estimated water table and (B) uncertainty from kriging of water levels measured in the FederalState Cooperative water-level monitoring network, eastern Snake River Plain, Idaho.
Methods 19

tac13-0832_fig 07b

B. Uncertainty based on the Co-op network

0 5 10 15 20 30

Standard error from the application of kriging (σUK)—in meters.

25

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

EXPLANATION

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; central 

meridian 113°00’W; North American Datum of 1927. Uniform kriging-grid block size 

(resolution) of 500 meters by 500 meters.

Idaho National Laboratory boundary

Eastern Snake River Plain boundary Well in the Federal-State Cooperative water-level

monitoring network (Co-op network)—166 wells

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

40 Miles

0 20

0 20

40 Kilometers

Figure 7.—Continued
20 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

Figure 8. (A) Estimated water table and (B) uncertainty from kriging of water levels measured in the U.S. 

Geological Survey-Idaho National Laboratory water-level monitoring network, Idaho National Laboratory and 

vicinity, Idaho.

tac13-0832_fig 08a

1,330 1,340 1,350 1,360 1,400

Estimated 2008 median water-level elevations from the application of kriging (z)—in meters above North American 

Vertical Datum of 1988.

ˆ

A. Water table based on the USGS-INL network

1,370 1,380 1,390

Big Southern

Butte

Middle

Butte

East

Butte

Big Lost River

spreading areas

Atomic

City

Howe

Mud

Lake Terreton

Mud Lake

Big

River

Big Lost River

Sinks and 

Playas

Lost River

Lost

Little

113°

43°

30'

113°12' 112°48' 112°36' 112°24'

43°

36'

43°

42'

43°

48'

43°

54'

43°

24'

10 Miles

0 5

0 5

10 Kilometers

Idaho National Laboratory boundary

EXPLANATION

Eastern Snake River Plain boundary

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

Well in the U.S. Geological Survey-Idaho National

 Laboratory water-level monitoring network

 (USGS-INL network) — 171 wells

1,360

1,350

1,340

1,370

1,380

1,390
Methods 21

Figure 8.—Continued

tac13-0832_fig 08b

Standard error from the application of kriging (σUK)—in meters.

B. Uncertainty based on the USGS-INL network

0 5 10 15 20 25 30

Big Southern

Butte

Middle

Butte

East

Butte

Big Lost River

spreading areas

Atomic

City

Howe

Mud

Lake Terreton

Mud Lake

Big

River

Big Lost River

Sinks and 

Playas

Lost River

Lost

Little

113°

43°

30'

113°12' 112°48' 112°36' 112°24'

43°

36'

43°

42'

43°

48'

43°

54'

43°

24'

10 Miles

0 5

0 5

10 Kilometers

Idaho National Laboratory boundary

EXPLANATION

Eastern Snake River Plain

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927; Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

Well in the U.S. Geological Survey-Idaho National

 Laboratory water-level monitoring network

 (USGS-INL network) — 171 wells
22 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

A scatter plot of measured values and estimated values 

in the leave-one-out cross-validation analysis is shown in 

figure 9A. The slope of the regressed line (black dashed 

line, slope = 1.1 meters per meter [m/m], R2 = 0.97) is about 

45 degrees (gray solid line), indicating that the estimates 

are not conditionally biased (Philip and Kitanidis, 1989, 

p. 862). Conditional bias occurs when the kriging estimates 

do not have the same variability (standard deviation) as the 

measured values.

Figure 9B shows a scatter plot of the estimation error 

versus the estimated values of water-level elevation at the 

observation wells. Values of estimation error range from 

-144.7 to 267.5 m, with a mean and standard deviation of 

0.7 m and 23.6 m, respectively. The mean estimation error is 

small (ideally zero), indicating an absence of systematic errors 

that could lead to biased estimations from the kriging model. 

Estimation error is equally scattered around a horizontal line 

and independent of the magnitude of the estimated values 

(R2 = -0.003). This indicates stationarity may be assumed 

for the residual values (e). Points outside the 95-percent 

confidence interval (plus or minus twice the standard 

deviation) indicate that either the measurement is incorrect 

or the area where the observation well is located requires 

a denser network of wells (Theodossiou and Latinopoulos, 

2006, p. 997).

An analysis of the spatial distribution of estimation 

error identifies (1) important observations for constructing 

the water-table map (such as isolated measurements that are 

different and distant from surrounding observations), and 

(2) confounding data that is in areas of high data density with 

water levels much different from other local measurements. 

Estimation error is shown in figure 10. Many of the locations 

of larger positive estimation error (green circles) coincide 

with areas of rapid change in the water table elevation. For 

example, the largest green circles at observation wells 2, 98, 

103, 105, and 166 are in areas of steep hydraulic gradients 

along the margin of the ESRP. These wells are all paired 

with a band of nearby wells with relatively large negative 

estimation errors (red circles) that define the locations where 

the hydraulic gradients rapidly flatten. This type of pairing 

is also observed near the 1,440 m water-table contour where 

a band of relatively large green circles is just upgradient of 

a band of relatively large red circles (fig. 10). This area of 

steep hydraulic gradient coincided with changes in aquifer 

transmissivity near Mud Lake (fig. 2) (Lindholm and others, 

1988). Relatively large estimation errors (both positive and 

negative) also can occur at observation wells in sparsely 

populated areas of the monitoring network. For example, 

the large estimation error at well 87 (-44.93 m) probably is 

because of its relative isolation within the network (about 

21 km from the nearest neighboring well) and may indicate a 

need for higher network density in this area.

In areas where the estimation errors are relatively large 

and apparently random (that is, not because of the banding or 

isolation), the data are possibly erroneous. Alternatively, these 

relatively large estimation errors can be explained by extreme 

local abnormalities in the water table resulting from irrigation 

pumping, deep-percolation return flow beneath irrigated fields, 

or both. For example, estimation errors are relatively large and 

random in irrigated areas in the northeast and southwest parts 

of the ESRP (figs. 1 and 10).

Optimization of Water-Level 

Monitoring Networks

Each water-level monitoring network in the ESRP is 

optimized separately. Individually optimizing each network 

enables managers to adapt the results of this study into future 

network designs. However, water-table maps typically are 

interpolated from all available water-level data, with littleto-no distinction made as to which network the observation 

well belongs (other than to select wells from networks that 

maintain a consistent level of data quality). A disadvantage of 

individually optimizing each network is the omission of data 

from other networks; the exclusion of this data can result in 

data redundancy where network coverage overlaps and when 

data from multiple networks are used in the interpolation. To 

avoid this issue, network managers are encouraged to combine 

resources and to optimize a single monitoring network. 

Optimization of combined networks in the ESRP is beyond the 

scope of this report.

Planning Objective

The planning objective for the water-level monitoring 

networks is to reduce total monitoring costs by removing 

wells from the original network because they add little 

or no information characterizing the water table. In this 

study, equal monitoring costs are assumed for each well. 

Although the validity of this assumption is untrue (for 

example, travel time can account for large variability in 

monitoring costs), it permits wells to be evaluated exclusively 

through a geostatistical analysis of the water-level elevation 

measurements. An estimate of the true cost savings for an 

optimized monitoring network is beyond the scope of this 

study; however, decreases in the total number of wells in a 

monitoring network typically will result in a reduction of total 

monitoring costs.
Methods 23

tac13-0832_fig 09

Estimated water-level elevation from cross validation (z*),

in meters above North American Vertical Datum of 1988

Measured water-level elevation (z), in meters above North American Vertical Datum of 1988

800

1,000

1,200

1,400

1,600

1,800

-200

200

400

0

800 1,000 1,200 1,400 1,600

166

100

24

64

103

105

2

11

163

166

163

100

24 64

2 105 103

11

162

162

5

5

98

13

13

40

67

98

98

40

67 13

87

87

28

28

Estimation error (z-z*), in meters 

Measured versus estimated 

Estimation error versus estimated

Linear regression model

95-percent confidence interval

Line at 45-degree slope

EXPLANATION

Mean estimation error:

Correlation of measured and estimated:

Correlation of estimation error and estimated:

Cross-validation statistics

0.72

0.97

-0.01

A. Measured versus estimated

B. Estimation error versus estimated

Figure 9. Scatter plots showing (A) measured and estimated water-level elevations, and (B) estimation error and 

estimated water-level elevations, from leave-one-out cross-validation, eastern Snake River Plain, Idaho. Map numbers 

included for sites located outside the 95-percent confidence interval of estimation error.
24 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

EXPLANATION

-144.73

-1.36

0.04

1.41

267.52

(minimum)

(25th percentile)

(median)

(75th percentile)

(maximum)

Estimation error from leave-one-out crossvalidation (z-z*)—in meters.

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927.

EXPLANATION

Idaho National Laboratory boundary

Eastern Snake River Plain boundary

tac13-0832_fig 10

1,340

1,360

1,260

1,240

1,220

1,380

1,460

1,480

1,740

1,440

1,180

900

1,100

1,300

1,000

1,200

1,400

1,500

1,400

43°

30'

113° 112°

43°

42°

30'

115° 114°

44°

44°

30'

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Lake

Walcott

Camas Creek

40 Miles

0 20

0 20

40 Kilometers

1,300 Water-table contours—Shows elevation of 

median water-levels in 2008. Contour 

intervals 20 meters. Vertical datum is North 

American Verical Datum of 1988 (NAVD 88).

100

24

64

103

105

2

11

163 162

166

5

98

40

67

13

87

28

Figure 10. Estimation errors from leave-one-out cross-validation, eastern Snake River Plain, Idaho. Map 

numbers included for sites located outside the 95-percent confidence interval of estimation error.
Methods 25

Design Criteria

To accomplish the established objective of the monitoring 

networks, water levels should be measured at well sites 

selected to satisfy the following design criteria:

1. The total number of wells in the optimized monitoring 

network is fixed and based on a user-defined number 

of wells to remove from the original network (nr). 

Selecting an appropriate value for nr is a management 

decision and typically requires a cost-benefit analysis. 

To assist decision makers, optimal monitoring networks 

corresponding to a variety of nr values are included in 

this report.

2. Standard error from the application of kriging should 

be as small as possible. For example, removal of an 

observation well from an area of the monitoring network 

where few wells exist typically would result in a large 

increase in the interpolation error (defined as the mean 

standard error for all points [or nodes] in the kriging grid); 

therefore, this well would not likely be removed.

3. The difference between measured and estimated 

water-level elevations indicates the importance of an 

observation well for simulating the water-table surface. 

For example, well sites with smaller differences are 

less important because their exclusion from the existing 

monitoring network would have little-to-no effect on 

the distribution of water levels in the kriged surface. 

In comparison, water-level distribution is much more 

sensitive to observation wells in areas where differences 

between measured and estimated values are large. 

Localized water-table features are preserved by retaining 

wells where the difference between measured and 

estimated values is large.

4. The variability of water-level measurements is preserved 

across time. Observation wells with hydrographs showing 

prominent seasonal fluctuations and long-term trends 

are important for understanding the availability and 

sustainability of groundwater resources. For example, 

observation wells along the margin of the ESRP should 

show a stronger seasonality associated with recharge, 

whereas wells in the center of the plain should have a 

damped response. Near pumping areas, wells may have 

a different type of seasonality, with low water levels 

coinciding with peak demand. Informed decisions 

about the aquifer often require an understanding of 

seasonality. The standard deviation was used to identify 

the temporal variations in water-level measurements. 

Sites with small standard deviations are more likely to be 

excluded from a monitoring network than sites with large 

standard deviations.

5. The measurement error of water-level elevations should 

be as small as possible. Well sites with large measurement 

error are more likely to be excluded from the network. 

Measurement error is estimated as the sum of the 

accuracy to which the measurement point elevation is 

known plus the accuracy of the measurement method.

Each of these criteria (with the exception of design criterion 

(1), the number of sites to remove) was converted to a 

mathematical metric, and the metrics were combined into a 

single multi-objective function that was used to identify a 

water-level monitoring network satisfying the design criteria 

as much as possible.

Multi-Objective Problem Formulation

The multi-objective problem is formulated as a 

single-objective optimization where a weighted combination 

of the design criteria is minimized. In mathematical terms, this 

is expressed as:

minimize

for valid "decision variab

r x x xn

F

F

1 2 , , ,

,

 ∈

′( ) = ( ) Z

x

x les"

P( ) x otherwise







 , (28)

where

Z is the set of all integers;

x is the decision variables;

F′ is the ʺfitnessʺ function, in meters;

F is the weighted-sum objective function, in 

meters; and

P is the ʺpenaltyʺ function, in meters.

The minimum fitness value corresponds to the optimal 

monitoring network. The fitness function is dependent on the 

decision variables, a vector of integer values used to identify 

observation wells in the existing monitoring network that will 

not be included in the reduced network. The purpose of the 

optimization solver is to find values of x that minimize the 

fitness value.

For valid combinations of decision variables, the fitness 

value is calculated using the function F, given by:

F w ( ) x x = f w ( ) + f w ( ) x x + f w ( ) + f ( ) x 1 1 2 2 3 3 4 4 (29)

where

f is the individual objective function, in meters; 

and

w is the weighting coefficient.
26 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

All the design criteria except for the management decision 

of how many wells to remove from the existing monitoring 

network (nr) are quantified by each of the individual objective 

functions: f

1, f2, f3, and f4. The relative influence of each 

criterion may be established by varying the associated 

weights: w1, w2, w3, and w4.

For functions f

1 and f2, kriging estimates are based on 

the reduced monitoring network, which includes all original 

network wells that are not identified by the optimization solver 

for removal. In mathematical terms, measurement points in the 

reduced network are described using set-builder notation as: 

s x i e for all i i ∈ ∈ { } Z |1 ≥ ≤i n and i∉ (30)

where

ne is the number of observation wells in the 

existing monitoring network.

The function f

1 is the metric selected to minimize the 

standard error, and is defined as:

f

n

n

1

2

1

1 = ( ) =

∑

n

UK n,i

i

n

σ s (31)

where 

sn,i is the spatial coordinates corresponding to 

node i in the kriging grid;

nn is the number of nodes in the kriging grid; and

σ UK n,i

2 ( ) s is the estimated variance at point sn,i based 

on the reduced monitoring network 

(equation 14), in square meters.

The summation in f

1 is over all nodes in the kriging grid 

(equation 31). Because standard error in each node depends on 

the proximity of nearby observation wells, removal of wells 

from regions that have sparser data increases standard error 

more than at nodes that are close to other supporting data.

The function f

2 is the metric selected to minimize the 

root-mean-squared-error (RMSE), and is defined as:

( ) ( )

{ }

2

2 i i

i r

e

1 ˆ

for all i i |1 i and i

f zz n

n

=   −  

∈ ∈ ≥≤ ∈

∑

Z

s s

x

(32)

where

si is the spatial coordinates at well site i;

z(si) is the median water-level elevation at point si

for the 2008 measurements, in meters; and

i zˆ( ) s is the estimated water-level elevation at 

point si based on measurements in the 

reduced monitoring network, in meters 

(equation 4).

The summation in f

2 is over well sites selected for removal from 

the existing monitoring network (equation 32). Removal of well 

sites with small differences between measured and estimated 

values decreases the RMSE more than removing wells with 

large differences.

The function f

3 is the metric selected to preserve the 

variability of water-level measurements across time, and is 

defined as

f

n

n 3

1 = ∑ ( ) ∈ ∈ { } 1 ≥ ≤ ∈

r

z i

i

e σ s x for all i i Z | i and i (33)

where

σ z i ( ) s is the standard deviation of all water-level 

elevation measurements collected at point si

across the period of record (duration varies 

at each well site), in meters.

The summation in f

3 is over well sites selected for removal from 

the existing monitoring network (equation 33). Removing wells 

with small standard deviations preserves the variability more 

than removing wells with large standard deviations.

The function f

4 is the metric selected to minimize the mean 

measurement error, and is defined as:

f

n n

n 4

1 = 1 − ∑ ( ) ∈ ∈ { } ≥ ≤ ∉

r

z i

i

e ε s x for all i i Z | i and i (34)

where

ε z i ( ) s is the mean measurement error of z at point si

estimated for the 2008 measurements, in 

meters.

The summation in f

4 is over well sites in the reduced monitoring 

network (equation 34). As expected, removing wells with large 

measurement errors will decrease the mean measurement error 

for the reduced monitoring network.

A penalty function, P, is used to penalize combinations of 

decision variables that are non-unique (that is, when x contains 

duplicate values) by setting the fitness value artificially large 

(equation 28). The penalty function is given by:

P C ( ) x = ndup (35)

where

C is the penalty coefficient, in meters; and

ndup is the number of duplicate wells in x.
Methods 27

The linear dependency of P on ndup indicates that, for 

networks with too few wells, the penalty value is proportional 

to the number of missing wells. For example, a network that 

is missing only 1 well would be penalized less than a network 

that is missing 20 wells. This helps to reduce the number of 

calls to the penalty function. The penalty coefficient, C, is 

selected to be arbitrarily large compared with the maximum 

possible F value. The high computational costs associated 

with kriging an infeasible network are circumvented by calling 

the low-cost penalty function.

Genetic Algorithm

A genetic algorithm (GA) (Holland, 1975) is used to 

find the best fitness value (that is, the minimum F′ value in 

equation 28). GAs are adaptive heuristic search algorithms 

that mimic the mechanics of natural selection and survival 

of the fittest, and are well suited for solving combinatorial 

optimization problems in which there is a large set of 

candidate solutions. Koza (1992, p. 18) provides the following 

definition of a GA:

“The genetic algorithm is a highly parallel 

mathematical algorithm that transforms a set 

(population) of individual mathematical objects 

(typically fixed-length character strings patterned 

after chromosome strings), each with an associated 

fitness value, into a new population (i.e., the next 

generation) using operations patterned after the 

Darwinian principle of reproduction and survival 

of the fittest and after naturally occurring genetic 

operations (notably sexual recombination).”

In GA terminology, the array of decision variables (or 

string of “genes”) in the optimization problem is called a 

“chromosome”, which for the current problem defines the 

set of observation wells being considered for removal from 

the existing monitoring network (x). The integer values used 

to identify wells in the original network (map numbers in 

table 5) are coded in the chromosome as fixed-length binary 

strings using Gray encoding (Gardner, 1986). A chromosome 

represents a unique solution in the solution space, the 

collection of all possible (or “candidate”) solutions to the 

optimization problem. In this study, a chromosome describes a 

single design solution for the reduced water-level monitoring 

network (that is, well sites to exclude from the existing 

monitoring network). Each design solution (a particular set 

of wells uniquely defined in the chromosome) is assigned a

fitness value (F′), which summarizes how well the particular 

set of wells meets the overall design objectives (described in 

the section, Design Criteria). The GA operates on a collection 

of chromosomes called a “population”; where the number of 

chromosomes in a population is expressed as npop.

An implementation of the GA begins with a population 

of random chromosomes (that is, suitable sets of randomly 

selected observation wells). During each “generation” 

(iteration) of the GA, the fitness of every chromosome in the 

population is computed. A subset of chromosomes is selected 

from the population based on their having superior fitness 

values, and is copied to a new population. This ensures that the 

best solutions can survive to the end of the GA run. The small 

part of the population that is guaranteed to survive to the next 

iteration is called “elitism” (re). The rest of the new population 

has many chromosomes modified from the present population.

Operators used to modify chromosomes include 

“crossover” (also called recombination) and “mutation”. 

Crossover is the process of combining part of the data from 

two “parent” chromosomes to produce two new “child” 

chromosomes. Parents are selected from the present population 

using a linear-rank selection method. In linear-rank selection, 

chromosomes are ranked from best fitness value (rank = 1) 

to worst fitness value (rank = ne), and are selected with a 

probability that is linearly proportional to their ranking. Part 

of the data from each parent is combined using single-point 

crossover—one crossover point on both parents’ chromosome 

strings (decoded as integer values) is selected. All data from 

the beginning of the chromosome to the crossover point is 

swapped between the parents’ chromosomes to produce the 

child chromosomes. There is a chance of introducing duplicate 

well sites into the children during crossover. To discourage 

this from happening, a child with duplicates is aborted, a 

new crossover point is randomly selected, and again data are 

swapped between parents. A limit is placed on the number of 

times a child chromosome can be aborted (na) to avoid biasing 

the location of the crossover point. If na is exceeded, the 

infeasible child chromosome containing duplicates survives 

and its associated fitness value is penalized (equation 35). The 

probability that crossover will occur between two parents is 

called the “crossover probability” (pc). If there is no crossover, 

child chromosomes will be an exact copy of their parents. 

After crossover, mutation takes place.

Mutation is the process of altering a gene on the child 

chromosome. This mutation can result in the possible removal 

of a well site, thereby maintaining genetic diversity in the 

population from one iteration to the next. The location of 

the gene (or well site) on the child chromosome that will be 

mutated is randomly selected. The modified value for the 

mutated gene is randomly selected from the set of wells not 

already included in the child chromosome. The probability 

that a mutation will occur on a child chromosome is called the 

“mutation probability” (pm). After crossover and mutation, the 

child chromosomes are copied to the new population.
28 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

New parents are selected for each new pair of children, 

and the process continues until the new population is filled. 

The new population is then used in the next iteration of the 

algorithm, with successive iterations producing smaller values 

of F′. For subsequent iterations, the fitness calculation is only 

needed for chromosomes derived from crossover, mutation 

operations, or both. The algorithm terminates based on the 

following criteria: (1) the maximum number of iterations 

(niter) is reached, or (2) the maximum number of consecutive 

iterations without any improvement in the best fitness value 

(nrun) is exceeded. Global optimality is not guaranteed with 

either of these stopping criteria; however, the probability of 

attaining the global optimum increases with an increase in the 

magnitude of niter or nrun values.

Computer Software

Computer software used to process data, to perform 

kriging analysis, to optimize the water-level monitoring 

networks, and to produce information graphics was written 

in the R programming language (R Development Core Team, 

2013). Functions and data sets specific to this study were 

collected in an R package (a cross-platform extension of the 

R base system) called “ObsNetwork”. Examples of how to 

use these functions and data sets are included in the package 

documentation (appendix A). In addition to the base packages 

included with R, ObsNetwork depends on the following 

contributed packages available on the Comprehensive R 

Archive Network (CRAN):

• sp: provides classes and methods for spatial data 

(Pebesma and Bivand, 2005; Bivand and others, 2008);

• rgdal: provides bindings to the Geospatial Data 

Abstraction Library (GDAL) and access to the 

cartographic projections library (PROJ.4) (Keitt and 

others, 2012);

• raster: performs geographic analysis and modeling 

with raster data (Hijmans and van Etten, 2012);

• gstat: performs spatial geostatistical modeling 

(Pebesma, 2004); and

• GA: implements genetic algorithms using a flexible 

general purpose set of tools (Scrucca, 2013).

ObsNetwork is in the public domain because it contains 

materials that originally came from the USGS. R and other 

package dependencies have more restrictive licenses. The code 

and documentation, including data sets used in this report, are 

available online for downloading from a software repository 

at https://github.com/jfisher-usgs/ObsNetwork. As this code 

is revised or updated, new versions will be made available for 

downloading from this site. Version information about R and 

required packages used in this report are shown in appendix B.

Computer Hardware

Genetic algorithms can be very demanding in terms of 

computer time; therefore, a brief summary of the computer 

hardware used in this study provides context for reported 

computation times (that is, the elapsed time for a GA run). 

The computer used for experimentation was equipped with 

a single IntelTM XeonTM central processing unit (version 

X5687, four cores) at 3.6 gigahertz, and 32 gigabytes (GB) 

of random-access memory (RAM) at 1,333 megahertz. Four 

GB of RAM were allocated for each GA run; failures owing 

to memory restrictions were never a problem. GA runs often 

were made simultaneously (no more than four runs at a time) 

to leverage the multi-core processor; therefore, computation 

times should be viewed as approximate values.

Results and Discussion

The GA-based designs of water-level monitoring 

networks in the ESRP are based on two phases of sensitivity 

analysis. The first phase examines the relationships between 

select control parameters and optimal solutions and identifies 

control parameter values that optimize model performance. 

The second phase analyzes the trade-offs associated with 

changes in the number of wells to remove from the existing 

monitoring network (nr). Parameter values for both phases of 

the sensitivity analysis are shown in table 1.

Model Performance

A series of GA runs were conducted for some of the 

control parameters (number of sites removed, kriging grid 

resolution, population size, elitism rate, crossover probability, 

and mutation probability) to better understand the sensitivity 

of the algorithm to incremental changes in the parameters 

(table 2, figs. 11 and 12), and to find reasonable settings for 

optimizing the existing monitoring networks. For example, a 

solution to the multi-objective function (“best fitness value”) 

was determined for seven different population sizes while 

holding all other control parameters constant (“base-case 

values” in table 1). The functional relationship between best 

fitness value and population size provides a mechanism for 

evaluating the sensitivity of the GA. The analysis, however, 

ignores all interdependencies between the control parameters. 

Although the assumption that there are no interdependencies 

between the control parameters is not strictly valid, the 

analysis is believed to provide an adequate means for 

evaluating algorithm sensitivity. Four types of performance 

measures are considered throughout the analysis:
Results and Discussion 29

Table 1. Parameter values used for base-case conditions and the final optimizations of the water-table monitoring networks of the 

eastern Snake River Plain aquifer, Idaho.

[Control parameter: parameters that control the optimization of the monitoring networks. Base-case value: parameter values specified as base-case conditions 

in the model performance phase of sensitivity. Final value: parameter values used in the final optimization of the monitoring networks]

Control parameter Abbreviation Unit Base-case

value

Final

value

Kriging analysis

Theoretical semivariogram (spherical model)

 Nugget g square meter 0.0 0.0

 Sill c square meter 1,948.5 1,948.5

 Range r meter 159,991.0 159,991.0

Spatial resolution of uniform kriging grid

 Length of square block side l kilometer 2.5 12.5 / 21.5

Multi-objective problem formulation

Number of well sites to remove from the existing monitoring network nr unitless 40 10, 20, 40, 60, 80

Weighting coefficients on individual objective functions

 Weight on the standard error function (f

1) w1 unitless 100 100

 Weight on the root-mean-square error function (f

2) w2 unitless 1 1

 Weight on the mean standard deviation function (f

3) w3 unitless 1 1

 Weight on the mean measurement error function (f

4) w4 unitless 1 1

Penalty function

Penalty coefficient C meter 1,000,000 1,000,000

Genetic algorithm

Population size npop unitless 2,000 2,000

Genetic operations

 Elitism rate re unitless 0.05 (5 percent) 0.05 (5 percent)

 Crossover probability pc unitless 0.80 (80 percent) 0.80 (80 percent)

 Mutation probability pm unitless 0.05 (5 percent) 0.30 (30 percent)

 Maximum number of times a child chromosome can be aborted

 during crossover

na unitless 10 10

Terminating conditions

 Maximum number of iterations niter unitless 100 infinity

 Maximum number of consecutive iterations without any

 improvement in the best fitness value

nrun unitless infinity 50

1

 Specified for the Federal-State Cooperative water-level monitoring network (Co-op network).

2

 Specified for the U.S. Geological Survey-Idaho National Laboratory water-level monitoring network (USGS-INL network).

1. Best fitness value (F′, ideally near the minimum), in 

meters.

2. Computation time (reasonable given computational 

resources and ideally small), in hours.

3. The percentage of chromosomes that invoke the penalty 

function (equation 35), where GA performance decreases 

as the number of calls to the penalty function increases.

4. Number of consecutive iterations without any 

improvement in the best fitness value (nrun). The 

magnitude of value nrun gives some indication as to 

whether the global solution was found. That is, the 

probability of a GA solution being globally optimal 

increases as the number of consecutive iterations without 

improvement increases.
30 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

Table 2. Sensitivity of the genetic algorithm to incremental changes in the control parameters, eastern Snake River Plain, Idaho.

[Control parameter: parameters that control the optimization of the water-level monitoring network. A bolded control parameter value indicates base-case 

conditions (see table 1). Number of sites removed: the number of well sites to remove from the existing monitoring network (nr). Kriging grid resolution: the 

spatial resolution of the uniform kriging grid described using the length of a grid block side (l). Population size: the number of chromosomes in a population 

(npop). Elitism rate: the fraction of the population that is guaranteed to survive to the next iteration (re). Crossover probability: the probability that crossover 

will occur between two parent chromosomes (pc). Mutation probability: the probability that a mutation will occur on a child chromosome (pm). Best fitness 

value: the smallest fitness value (F ′). Computation time: the time required to run the genetic algorithm. Percent penalty: the percentage of chromosomes 

that invoke the penalty function. Number of times best-fitness repeated: the number of consecutive iterations without any improvement in the best-fitness 

value. Abbreviations: Co-op network, Federal-State Cooperative water-level monitoring network; USGS-INL network, U.S. Geological Survey-Idaho National 

Laboratory water-level monitoring network; km, kilometer; m, meter; h, hour]

Control parameter Co-op network USGS-INL network

Name Value

Best fitness

value

(m)

Computation

time

(h)

Percent

penalty

Number of 

times

best fitness

repeated

Best fitness

value

(m)

Computation

time

(h)

Percent

penalty

Number of 

times

best fitness

repeated

Number

of sites

removed

10 1,374.315 14.8 0.0 78 1,174.581 8.8 0.1 67

20 1,378.496 16.1 0.2 30 1,174.913 9.7 0.1 39

40 1,390.061 16.7 0.4 2 1,175.230 11.7 0.5 10

60 1,409.370 15.4 0.8 2 1,175.716 10.9 0.8 1

80 1,443.009 15.5 1.0 4 1,177.684 10.9 1.4 2

Kriging

grid

resolution

(km)

1.5 1,382.731 38.2 0.1 4 1,145.589 24.2 1.5 1

2.0 1,386.504 22.7 0.2 9 1,166.779 15.2 0.3 6

2.5 1,390.061 16.7 0.4 2 1,175.230 11.7 0.5 10

3.0 1,396.676 11.6 0.2 1 1,186.873 8.0 0.3 3

3.5 1,400.436 9.0 0.3 27 1,206.887 6.4 1.1 5

4.0 1,407.150 8.1 0.2 1 1,218.568 5.4 0.2 20

Population

size

500 1,391.565 3.9 0.1 2 1,175.293 2.8 0.1 5

1,000 1,390.643 7.7 0.3 5 1,175.230 5.3 1.2 1

1,500 1,390.008 10.8 0.5 18 1,175.249 7.5 0.5 1

2,000 1,390.061 16.7 0.4 2 1,175.230 11.7 0.5 10

3,000 1,389.979 22.6 0.5 10 1,175.231 15.9 0.3 2

4,000 1,389.848 30.6 0.3 1 1,175.223 20.1 2.3 13

5,000 1,389.848 37.4 0.5 11 1,175.223 25.9 1.1 17

Elitism

rate

0.01 1,390.488 15.0 0.1 1 1,175.247 9.3 0.6 2

0.03 1,390.243 15.4 0.3 4 1,175.234 10.1 1.0 3

0.05 1,390.061 16.7 0.4 2 1,175.230 11.7 0.5 10

0.10 1,389.958 15.0 1.3 2 1,175.239 10.9 0.8 1

0.15 1,390.032 15.4 0.3 5 1,175.250 11.0 0.3 3

0.20 1,390.066 15.1 0.5 5 1,175.234 10.7 2.4 4

0.30 1,390.026 14.3 0.3 4 1,175.229 9.5 0.7 5

0.40 1,389.955 13.6 0.3 2 1,175.242 8.5 1.1 7

0.50 1,390.293 10.3 0.4 3 1,175.252 7.1 1.1 1

Crossover

probability

0.5 1,390.137 10.7 0.1 3 1,175.246 7.6 0.2 3

0.6 1,390.126 12.0 0.2 7 1,175.256 8.2 1.1 1

0.7 1,390.227 14.3 0.8 29 1,175.236 9.4 1.0 1

0.8 1,390.061 16.7 0.4 2 1,175.230 11.7 0.5 10

0.9 1,390.022 18.8 0.1 6 1,175.244 11.6 0.7 1

1.0 1,390.327 20.2 0.4 33 1,175.224 13.5 0.2 12

Mutation

probability

0.005 1,390.957 16.6 0.1 6 1,175.272 11.6 0.1 4

0.020 1,390.747 16.5 0.2 3 1,175.259 9.1 0.2 5

0.050 1,390.061 16.7 0.4 2 1,175.230 11.7 0.5 10

0.100 1,390.041 16.7 0.4 12 1,175.225 11.8 0.5 1

0.200 1,389.848 17.0 1.0 2 1,175.228 12.0 1.3 2

0.300 1,389.870 17.6 2.3 7 1,175.223 10.4 3.9 1

0.400 1,389.848 16.5 2.1 2 1,175.224 11.6 2.2 5

0.500 1,390.026 16.9 3.0 15 1,175.224 11.2 8.1 1
Results and Discussion 31

tac13-0832_fig 11 Best fitness value, in meters

Computation time, in hours

Best fitness value, in meters

Computation time, in hours

10 20 40 60 80

A. Number of sites removed B. Kriging grid resolution

D. Elitism rate

E. Crossover probability F. Mutation probability

10

20

30

40

1,370.0 0

1,390.0

1,410.0

1,430.0

1,450.0

Best fitness value, in meters

Computation time, in hours

Probability that mutation will occur on a child chromosome (pm)

Fraction of the population that is guaranteed to survive to the

next iteration (re

)

Length of a grid block side in the uniform kriging grid, in kilometers (l)

Probability that crossover will occur between two parents (pc

)

Number of sites selected for removal from the existing monitoring

network (nr

)

Number of chromosomes in a population (npop)

0 1,000 2,000 3,000 4,000 5,000

0.5 0.6 0.7 0.8 0.9 1.0 0 0.05 0.1 0.3 0.5

1.5 2.0 2.5 3.0 4.0

1,389.8

1,390.1

1,390.4

1,390.7

1,391.0

30 50 70 3.5

C. Population size

Point at

500, 1,391.6 

(off chart)

0.2 0.4

10

20

30

40

0

10

20

30

40

1,389.8 0

1,390.1

1,390.4

1,390.7

1,391.0

0 0.05 0.1 0.2 0.3 0.4 0.5

Computation time

Best fitness value

EXPLANATION

Figure 11. Sensitivity of the best fitness value and computational time to changes in the (A) number of sites 

removed, (B) kriging grid resolution, (C) population size, (D) elitism rate, (E) crossover probability, and (F)

mutation probability, Federal-State Cooperative water-level monitoring network, eastern Snake River Plain, 

Idaho. A bold control parameter value indicates base-case conditions (see table 1).
32 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 12 Best fitness value, in meters

Computation time, in hours

Best fitness value, in meters

Computation time, in hours

10 20 40 60 80

A. Number of sites removed B. Kriging grid resolution

D. Elitism rate

E. Crossover probability F. Mutation probability

1,140.00

1,160.00

1,180.00

1,200.00

1,220.00

Best fitness value, in meters

Computation time, in hours

Probability that mutation will occur on a child chromosome (pm)

Fraction of the population that is guaranteed to survive

to the next iteration (re

)

Length of a grid block side in the uniform kriging grid, in kilometers (l)

Probability that crossover will occur between two parents (pc

)

Number of sites selected for removal from the existing

monitoring network (nr

)

Number of chromosomes in a population (npop)

0 1,000 2,000 3,000 4,000 5,000

0.5 0.6 0.7 0.8 0.9 1.0 0.05

1.5 2.0 2.5 3.0 4.0

1,175.22

1,175.24

1,175.26

1,175.28

1,175.28

30 50 70 3.5

0 0.1 0.2 0.3 0.4 0.5

C. Population size

10

20

30

40

0

10

20

30

40

0

10

20

30

40

0

0 0.05 0.1 0.2 0.3 0.4 0.5

1,175.22

1,175.24

1,175.26

1,175.28

1,175.28

Computation time

Best fitness value

EXPLANATION

Figure 12. Sensitivity of the best fitness value and computational time to changes in the (A) number of 

sites removed, (B) kriging grid resolution, (C) population size, (D) elitism rate, (E) crossover probability, and 

(F) mutation probability, U.S. Geological Survey-Idaho National Laboratory water-level monitoring network, 

eastern Snake River Plain, Idaho. A bold control parameter value indicates base-case conditions (see 

table 1).
Results and Discussion 33

The GA sensitivity analysis includes runs for the Co-op 

and USGS-INL networks, and examines the four performance 

measures and the tradeoff between these measures as a 

function of the control parameters (table 2). The response 

curves for best fitness and computation time are shown 

for all GA runs in figures 11 (Co-op network runs) and 12

(USGS-INL network runs). Performance is considered best 

when the best fitness value is near the minimum value and 

before computation time becomes too long.

Results of the sensitivity analysis pertaining to each of 

the control parameters are summarized in the six subsections 

that follow:

Number of Sites Removed

The number of sites removed from an existing monitoring 

network (nr) has a significant effect on the best fitness value 

because of the strong dependence of mean standard error (f

1) 

and root-mean-square error (f

2) on nr

. Increasing nr results 

in increased values of f

1 and f2, which, in turn, increases the 

fitness value (weighted sum of objectives, equation 29). A 

rapid worsening of best fitness value starting between 20 and 

40 sites is shown in figure 11A, indicating that the removal of 

less than 20 sites has a relatively small effect on the ability of 

the network to represent the water table. A rapid worsening 

between 60 and 80 sites is shown in figure 12A, indicating 

that the removal of less than 60 sites has a relatively small 

effect on the ability of the network to represent the water table. 

By comparison, these observations imply that 20 sites can 

be removed from the Co-op network with a relatively small 

degradation of the estimated water-table map, and 60 sites 

can be removed from the USGS-INL network before the 

water-table map degradation increases more rapidly. This is 

not unexpected given the high network density around site 

facilities in the USGS-INL network (fig. 2). The possible 

redundancy between water-level measurements in these data 

clusters is quite large; therefore, many of these wells can be 

removed with-little to-no effect on the estimated water-table 

map. Kriging compensates for the effects of data clustering by 

treating clusters more like single points (Isaaks and Srivastava, 

1989, p. 300).

The effect on computation time from incremental 

changes in nr

 was small (less than 2 hours). Peaking at 

40 sites for both monitoring networks is shown in figures 11A

and 12A, indicating a non-linear relationship between nr

and computation time. This nonlinearity is attributed to the 

computational cost of kriging; the search for an optimal 

network requires many simulations of the water-table 

map, making kriging the most expensive operation in the 

optimization problem. Given that water levels are estimated at 

the locations of removed wells (zˆin equation 32), a reduction 

in nr requires fewer estimates, which, in turn, decreases the 

computation time for kriging. The cost of kriging also depends 

on the number of wells in the reduced monitoring network 

(n in equation 4), where an increase in nr results in a smaller 

network size and requires less computation time for kriging 

when compared to the costs associated with kriging a larger 

network. Finally, the nonlinearity in the computation time 

response curves results from the interplay between these two 

opposing forces (the size of the reduced monitoring network 

increases as the number of wells removed from the existing 

network decreases and the reverse), and any cost savings 

associated with a reduced number of estimates or smaller 

network size is cancelled out when nr is equal to 40. The 

number of times the penalty function (equation 35) is invoked 

can also affect the computation time; increasing the number 

of penalty calls results in a decrease in computation time 

because the penalty function does not require kriging. The 

percentage of chromosomes that invoke the penalty function 

was 1.0 percent or less for the Co-op network and 1.4 percent 

or less for the USGS-INL network indicating that computation 

time was unaffected by penalty calls (table 2).

The number of iterations needed to converge on a 

solution increased as the number of removed wells increased, 

as indicated by the increase in the number of consecutive 

iterations without any improvement in the best-fitness when nr

was decreased (table 2). This should be expected given that the 

number of possible combinations increases as nr is increased. 

For example, in the Co-op network there are 3.3×1015

possible network configurations when nr equals 10 and 

5.2×1048 configurations when nr equals 80.

Kriging Grid Resolution

Refining the spatial resolution of the uniform kriging 

grid (measured as the length of a grid block side, l) linearly 

decreased the mean standard error (f

1) (and consequently the 

best fitness value) (figs. 11B and 12B). Because the standard 

error (σ UK) is calculated at the nodes of the kriging grid, a 

finer grid resolution describes in more detail the depressions 

in the standard error surface near the observation wells 

(where σ UK = 0 at a well site). This added detail increases the 

positive skew in the standard error probability distribution, 

which implies that the mean value is decreased. Linear 

regression applied to the best fitness values indicates that the 

USGS-INL network (slope = 28.4 m/km, R2 = 0.987) is much 

more sensitive to changes in grid resolution than the Co-op 

network (slope = 9.7 m/km, R2 = 0.989). This suggests that the 

relatively dense resolution of the USGS-INL network supports 

a finer resolution of the kriging grid. An exponential increase 

in computation time for refinements in the kriging grid 

resolution occurred as accuracy of the mean standard error 

from refinements in grid resolution was improved (figs. 11B

and 12B). An l equal to 2.5 km for the Co-op network and 

1.5 km for the USGS-INL network provides the optimal 

tradeoff between best fitness value and computation times.
34 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

Population Size

Population size is the number of chromosomes in a 

population (npop). If the population size is too small, the 

algorithm may explore too little of the solution space to 

find a suitable solution (Marczyk, 2004). A rapid worsening 

of the best fitness values starting at between 1,000 and 

1,500 chromosomes is shown in figure 11C, indicating that 

a suitable GA solution was not available for population 

sizes of less than 1,500. A rapid decrease in best fitness 

values starting at between 500 and 1,000 chromosomes, 

and again between 1,500 and 2,000 chromosomes, is shown 

in figure 12C, indicating that a population size of less than 

2,000 is susceptible to suboptimal solutions. By comparison, 

this implies that the minimum population size needed to 

optimize the Co-op and USGS-INL networks is 1,500 and 

2,000, respectively. Increasing the population size enlarges 

the search space, which, in turn, increases the computation 

time. The computation time (figs. 11C and 12C) increases 

linearly at a rate of about 3.8 (R2 = 0.997) and 2.6 (R2 = 0.995) 

hours, respectively, for every 500 chromosomes added to the 

population. The fitness response to changes in population 

size for both networks indicates that a population must be 

composed of at least 2,000 chromosomes to find a good 

solution while minimizing computation time.

Elitism Rate

Elitism is the fraction of the population that is guaranteed 

to survive to the next iteration of the GA. This subset of 

chromosomes preserves the best solutions from one iteration 

to the next. Elitism can be an effective method for improving 

the efficiency of the algorithm; however, if set too large, it 

can decrease genetic diversity and potentially result in the 

global solution being overlooked (that is, it converges to a 

local minimum). A rapid worsening of the best fitness values 

starting at between 0.05 (5 percent) and 0.10 (10 percent) 

is shown in figure 11D, indicating a performance loss for 

elitism rates of less than 0.10; a decrease in elitism slows 

convergence. Another rapid worsening of the best fitness 

values was observed between 0.4 and 0.5, indicating 

premature convergence for elitism rates of greater than 0.4. 

A worsening of the best fitness values at the extremes (less 

than 0.05 and greater than 0.3) also is shown in figure 12D, 

however, a peak in the fitness at 0.15 may indicate that 

increasing elitism rates beyond 0.05 may result in a significant 

loss in genetic diversity.

A decrease in the computation time for increased elitism 

rate was observed in both networks for values between 0.2 

and 0.5 (figs. 11D and 12D). Elitist chromosomes survive to 

the next iteration along with their fitness value, and non‐elitist 

chromosomes calculate a fitness value only when they are 

derived from crossover operations (80 percent chance), 

mutation operations (5 percent chance), or both. The fitness 

calculation is computationally expensive (that is, unless 

the penalty function is invoked); therefore, an increase in 

the fraction of the population that does not require a fitness 

calculation results in a shorter computation time. For elitist 

rates of greater than 0.2, this fraction is guaranteed to increase; 

whereas, for elitism rates of less than 0.2, this fraction is a 

function of the elitism rate, the crossover probability, and the 

mutation probability. Computational cost savings associated 

with a large elitism rate are relatively insignificant when 

compared to the potential loss in genetic diversity; therefore, 

an elitism rate of 0.05 (5 percent) is used to facilitate the 

search for a globally optimal solution.

Crossover Probability

The crossover probability controls the rate at which 

solutions are subjected to crossover. The larger the value of 

crossover probability, the quicker potential new solutions are 

introduced into the population. If crossover probability is too 

large, chromosomes with good fitness values are discarded 

faster than selection can exploit them. However, if crossover 

probability is too small, the search may stagnate owing to 

the smaller exploration rate. As expected, the best fitness 

value generally improves for increased values of crossover 

probability, as shown in figure 11E. The exception to this 

downward trend is a rapid worsening of the best fitness value 

between 0.9 (90 percent) and 1.0 (100 percent), indicating 

that crossover probabilities of greater than 0.9 may be too 

large. A general downward trend also is shown in figure 12E; 

however, relatively large best fitness values at 0.6 and 0.9 

indicate a significant amount of uncertainty associated with 

this trend. For both networks, an almost-linear increase in 

computation time was observed for increasing values of 

crossover probability (figs. 11E and 12E). Increasing the 

crossover probability increases the fraction of the population 

requiring fitness calculations, which, in turn, increases the 

computation time. A comparison of the tradeoff between best 

fitness values and computation times indicates that, for both 

networks, selection of a 0.8 (80 percent) crossover probability 

will give relatively accurate estimates of the optimal 

monitoring network.

Mutation Probability

Mutation is used to introduce genetic diversity between 

iterations and to prevent convergence on a local minimum 

(sub-optimal solution). If mutation probability is too large, 
Results and Discussion 35

the GA will have difficulty converging on a suitable solution 

and the search becomes random. However, if the mutation 

probability is too small, the algorithm’s ability to explore 

the solution space will be greatly diminished. That is, the 

population of chromosomes becomes so similar that evolution 

slows or even stops. A worsening of the best fitness value 

between 0.005 (0.5 percent) and 0.2 (20 percent) is shown 

in figure 11F, indicating that mutation probabilities of less 

than 0.2 are too small. The fitness again worsens between 

0.4 and 0.5, indicating that mutation probabilities of greater 

than 0.4 are too large. The worst best fitness values are 

shown in figure 12F between 0.3 and 0.005, indicating that 

mutation probabilities of less than 0.3 are too small. Changes 

in computation time are negligible for both networks (less 

than 1.1 hours for the Co-op network and less than 3.0 hours 

in the USGS-INL network; figs. 11F and 12F). This is not 

unexpected given that the fraction of the population requiring 

fitness calculations is determined primarily by the elitism 

rate (20 percent) and crossover probability (80 percent). A 

mutation probability value of 0.3 (30 percent) provides an 

excellent starting value for further analyses.

Numerical results indicate similar responses between 

networks; however, best fitness values and computation 

times are much larger for GA runs conducted with the Co-op 

network. For example, GA solutions determined using 

base‐case conditions indicate a fitness and computation time 

for the Co-op network that are, respectively, 214.8 m larger 

and 5.0 hours longer than for the USGS-INL network. The 

increased magnitude of best fitness values is attributed to a 

network resolution that is relatively low for the Co-op network 

when compared to the USGS-INL network, where lower 

network resolutions typically increase values of mean standard 

error (f

1) and root-mean-square error (f2), which, in turn 

increase the fitness value. The longer computation times are 

owing to the relatively large number of nodes in the kriging 

grid of the Co-op network (nn = 4,690) when compared to 

the USGS-INL network (nn = 774). Mean standard error 

(equation 31) requires estimates of standard error (σ UK) at 

each node in the kriging grid; therefore, as the total number of 

standard error estimates is increased, so too is the problem size 

(together with the computation time) increased.

The set of control parameter values identified as 

optimizing model performance and used in the final 

optimizations of the existing water-level monitoring networks 

is given in table 1. The most significant changes from 

base-case conditions are (1) a reduction in the kriging grid 

resolution for the USGS-INL network from 2.5 to 1.5 m, 

(2) an increase in mutation probability from 0.05 to 0.30, and 

(3) the implementation of a new stopping criterion. For the 

final optimizations, the GA terminates only after exceeding 

50 consecutive iterations without any improvement in the 

best fitness value, thereby facilitating the search for a globally 

optimal solution.

Weighting Coefficients

For multi-objective problems, identifying a single 

solution that simultaneously minimizes each individual 

objective function (equations 31–34) is almost impossible. 

That is, any single individual objective value often can be 

improved only by degrading at least one of the other objective 

values. Combining the individual objective functions in a 

single weighted-objective function is subjective, requiring 

that a decision maker provide the weights. The weighted 

multi-objective function also is ill-suited for determining 

tradeoffs among individual objective functions; therefore, the 

algorithm’s sensitivity to changes in weights is not examined. 

Because the objective functions are simply weighted and 

added to produce a single fitness value, the function with the 

largest range dominates GA evolution. A poor value for the 

objective function with the larger range degrades the overall 

fitness much more than a poor value for the function with the 

smaller range (Bentley and Wakefield, 1997).

For this study, an emphasis was placed on the estimation 

uncertainty design criterion (f

1) by setting w1 equal to 100, and 

w2, w3, and w4 equal to 1. This assumes that network coverage 

is more important than the other design criteria: preserving 

localized features in the water table (f

2), maintaining temporal 

variations in water-level measurements (f

3), and reducing 

measurement error (f

4). The range of weighted-objective 

values in solution space indicates the relative influence of 

each design criterion in determining the optimal solution. For 

a given GA run and design criterion, a weighted-objective 

value is calculated for every evaluation of the weighted 

sum objective function (F in equation 29); the range of this 

value is defined as the difference between the largest and 

smallest of its individual component values. The range of each 

weighted-objective function is given in table 3 for GA runs 

based on both networks and changing the number of wells 

to remove from the original network (nr). As indicated by 

their ranges, the relative influence of each design criterion on 

the solution is given in decreasing order of importance as f

1, 

f

2, f3, and f4. As intended, design criteria f3 and f4 have little 

control over GA evolution and only after f

1 and f2 have been 

minimized to their fullest possible extent. The criteria f

3 and f4

were assumed much less important than f

1 and f2.
36 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

Table 3. Range of weighted-objective values in solution space (the collection of all possible solutions to the optimization problem) 

for changes in the number of sites to remove, eastern Snake River Plain, Idaho.

[Number of sites removed: well sites to remove from the existing monitoring network (nr). Weighted objective function: the individual objective function 

multiplied by its weighting coefficient. Minimum, Maximum, and Range: the minimum, maximum, and range of all calculated weighted objective values 

during the genetic algorithm search, respectively. Entry in bold indicates the weighted objective function with the largest range. Control parameter values:

a kriging grid resolution of 2.5 kilometers for the Co-op network and 1.5 kilometers for the USGS-INL network, population size of 2,000, elitism rate of 

0.05, crossover probability of 0.80, mutation probability of 0.30, and terminates after 50 consecutive iterations without any improvement in the best fitness 

value. Abbreviations: Co-op network, Federal-State Cooperative water-level monitoring network; USGS-INL network, U.S. Geological Survey-Idaho 

National Laboratory water-level monitoring network; m, meter; w, weighting coefficient; f, individual objective function]

Number

of sites

removed

Weighted

objective

function

Co-op network USGS-INL network

Minimum

(m)

Maximum

(m)

Range

(m)

Minimum

(m)

Maximum

(m)

Range

(m)

10 w1 f1 1,371.00 1,500.18 129.14 1,143.79 1,252.90 109.11

w2 f

2 0.70 105.48 104.73 0.06 4.42 4.36

w3 f3 0.70 4.56 3.90 0.64 2.55 1.90

w4 f4 0.60 0.71 0.11 0.02 0.06 0.05

20 w1 f

1 1,374.13 1,528.82 154.69 1,143.80 1,307.87 164.07

w2 f

2 1.17 93.17 92.00 0.10 3.34 3.24

w3 f

3 0.97 3.77 2.80 1.06 2.12 1.06

w4 f

4 0.56 0.76 0.19 0.01 0.07 0.06

40 w1 f

1 1,383.15 1,620.94 237.79 1,143.86 1,484.57 340.71

w2 f

2 3.15 74.11 70.96 0.14 2.67 2.53

w3 f

3 1.12 3.08 1.95 1.29 1.96 0.67

w4 f

4 0.51 0.83 0.32 0.01 0.08 0.07

60 w1 f

1 1,400.00 1,788.06 388.06 1,144.22 1,606.08 461.85

w2 f

2 5.43 67.97 62.54 0.20 3.03 2.83

w3 f

3 1.40 2.63 1.23 1.38 1.89 0.51

w4 f

4 0.48 0.89 0.42 0.01 0.09 0.08

80 w1 f

1 1,429.98 2,046.18 616.21 1,146.25 1,653.73 507.48

w2 f

2 6.11 77.89 71.78 0.28 2.61 2.33

w3 f

3 1.50 2.63 1.13 1.40 1.81 0.41

w4 f

4 0.43 0.95 0.53 0.01 0.11 0.10

Optimized Monitoring Networks

Each water-level monitoring network was optimized five 

times: removing 10, 20, 40, 60, and 80 observation wells from 

the original network (see “Final values” in table 1 for control 

parameter values). Wells identified for removal are shown in 

table 6 (at back of report). Here, “Times identified” indicates 

the number of times the observation well was identified for 

removal in the five GA runs for each network. For example, 

6 of the 10 wells selected for removal from the Co-op network 

were identified in all five GA runs (wells 8, 80, 120, 124, 140, 

and 164), and 4 of the 10 wells were identified in four of the 

GA runs (wells 34, 84, 91, and 146). By comparison, 8 of the 

10 wells selected for removal from the USGS-INL network 

were identified in all five GA runs (wells 184, 187, 188, 189, 

213, 245, 314, and 321), 1 of the 10 wells was identified in 

four of the GA runs (well 206, in all but the GA run removing 

80 wells), and 1 of the 10 wells was identified only once 

(well 251). Each GA run provides a unique solution that is 

entirely dependent on the number of wells to remove from 

the original network. That is, the solutions are non-sequential; 

wells identified for removal in the GA run removing 10 wells 

are not required to be part of the solution for the GA run 

removing 20 wells. The relatively large values of times 

identified, however, indicate that a consistent group of wells 

provides little-to-no beneficial added information. 
Results and Discussion 37

A summary of the GA runs is shown in table 4. 

Performance measures for each GA run include: best 

fitness value, number of iterations, computation time, rootmean-square deviation (RMSD), and the percent local 

error (PLE). The RMSD (ideally small) is a measure of the 

difference between kriged water-table maps estimated from 

measurements in the original and optimized networks, and 

expressed as:

n 2

orig n,i n,i i 1

n

ˆ ˆ () ()

RMSD

n

z z

n

=   −   = ∑ s s

(36)

where

nn is the number of nodes in the kriging grid; 

sn,i is the spatial coordinate of node i in the 

kriging grid; 

orig zˆ is the estimate of the water-level elevation 

based on the original network, in meters; 

and 

zˆ is the estimate of the water-level elevation 

based on the reduced network, in meters.

The PLE (ideally small) is the maximum error introduced 

by removing nr wells, divided by the relief across the 

kriged water-table map based on the original network, and 

expressed as: 

orig n,i n,i

orig n,i orig n,i

n

100 maximum ( ) ( ) ˆ ˆ PLE

maximum ( ) minimum ( ) ˆ ˆ

for i 1, , .

z z

z z

n

⋅ −     =   −  

= 

s s

s s

(37)

A comparison with the Co-op network GA runs 

implemented using base-case control parameter values 

indicates large gains in model performance using the final 

values optimized for model performance (table 1). Subtracting 

best fitness values calculated using the optimized (final) 

control parameters (table 4) from values calculated using 

base-case control parameters (table 2) gives the change in 

best fitness. Best fitness values decreased by 0.016, 0.191, 

0.407, and 2.644 m for GA runs removing 20, 40, 60, and 

80 wells, respectively. No change was observed for GA runs 

removing 10 wells, indicating this run has a relatively rapid 

rate of convergence on the optimal solution. The solution 

space for a GA run is proportional to the number of wells 

removed; therefore, as nr

 increases, so, too, do the number of 

iterations needed to converge on an optimal solution increase 

(together with computation time). This was the case for all 

runs except the Co-op network GA run removing 60 wells 

(table 4). Random sampling in the GA may explain this 

data abnormality.

Table 4. Genetic algorithm searches summarized for optimized water-level monitoring networks, eastern Snake River Plain, Idaho.

[Number of sites removed: well sites removed from an existing monitoring network (nr). Best fitness value: the smallest fitness value (F ′). Number of 

iterations: the number of completed genetic algorighm (GA) iterations. Computation time: the time required to run the GA. RMSD: root-mean-square 

deviation, a measure of the difference between kriged water-table surfaces estimated using the optimized reduced network and the existing network. Percent 

local error: between water-table maps estimated using the existing and optimized reduced networks. Control parameter values: a kriging grid resolution of 

2.5 kilometers for the Co-op network and 1.5 kilometers for the USGS-INL network, population size of 2,000, elitism rate of 0.05, crossover probability of 

0.80, mutation probability of 0.30, and terminates after 50 consecutive iterations without any improvement in the best fitness. Abbreviations: Co-op network, 

Federal-State Cooperative water-level monitoring network; USGS-INL network, U.S. Geological Survey-Idaho National Laboratory water-level monitoring 

network; m, meter; h, hour]

Number

of sites

removed

Co-op network USGS-INL network

Best fitness

value

(m)

Number of

iterations

Computation

time

(h)

RMSD

(m)

Percent

local

error

Best fitness

value

(m)

Number of

iterations

Computation

time

(h)

RMSD

(m)

Percent

local

error

10 1,374.315 86 11.8 0.061 0.24 1,144.979 81 16.0 0.002 0.23

20 1,378.480 108 17.6 0.143 0.39 1,145.279 131 37.6 0.005 0.30

40 1,389.870 181 28.6 0.598 1.10 1,145.586 187 55.1 0.006 0.30

60 1,408.964 245 39.5 1.276 2.92 1,146.171 203 53.1 0.019 1.02

80 1,440.365 197 30.8 1.736 2.94 1,148.343 246 64.8 0.051 1.51
38 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

The response curves for best-fitness, RMSD, and PLE 

are shown for all GA runs in figure 13. A rapid worsening of 

best fitness value, RMSD, and PLE, respectively, starting at 

between 20 and 40 sites, is shown in figures 13A–C, indicating 

that the removal of less than 20 wells has a relatively small 

effect on the ability of the network to represent the water 

table. A rapid worsening of best fitness value, RMSD, and 

PLE, respectively, starting at between 40 and 60 sites, is 

shown in figures 13D–F, indicating that the removal of less 

than 40 wells has a relatively small effect on the ability of 

the network to represent the water table. By comparison, 

this implies that 20 wells can be removed from the Co-op 

network with a relatively small degradation of the estimated 

water-table map, and 40 wells can be removed from the 

USGS-INL network before the water-table map degradation 

increases rapidly.

The spatial distribution of the difference between the 

two estimated surfaces (that is, the water table estimated from 

data in the original network minus the water table estimated 

from data in the reduced network) is shown for 10, 20, 40, 60, 

and 80 wells removed from the Co-op network (fig. 14) and 

the USGS-INL network (fig. 15). Relatively small differences 

with a spatial extent limited to near the removed well sites 

are shown in figures 14A and 14B. This is not unexpected 

given that the analysis of break-points indicates 20 wells 

can be removed from the Co-op network with relatively 

small degradation of the estimated water-table map. Wells 

primarily were removed from areas of high network density 

located in the northeast and south-central parts of the ESRP. 

A high network density indicates that the redundancy between 

water-level measurements in these areas can be quite large; 

therefore, many of the wells in these areas can be removed 

with little-to no-effect on the estimated water-table map. 

Progressively larger differences with increasing spatial extent 

are shown in figures 14C–E. The areas excluded from well 

removal coincide with areas of rapid change in the water-table 

elevation and areas of the monitoring network that are sparsely 

populated with wells. For example, only a small number of 

wells were removed from areas of steeper hydraulic gradients 

along the margin of the ESRP and near Mud Lake (figs. 2 and 

14E). Wells were never removed from the sparsely populated 

area in the west-central part of the ESRP; removing wells 

in these areas would significantly degrade the estimated 

water-table map.

For 10, 20 and 40 wells removed from the USGS-INL 

network, the magnitude and spatial extent of differences is 

negligible (figs. 15A–C) when compared to the distribution 

of corresponding Co-op network differences (figs. 14A–C). 

This is not unexpected given that the analysis of break-points 

indicates 40 wells can be removed from the USGS-INL 

network with relatively small degradation of the estimated 

water-table map. Wells were removed exclusively from 

areas of high network density near site facilities in the INL 

(fig. 2). Progressively larger differences with increasing 

spatial extent are shown in figures 15D and 15E. Removed 

wells were exclusively from areas of high network density 

near site facilities, indicating widespread data redundancy in 

this network.
Results and Discussion 39

tac13-0832_fig 13 Percent local error (PLE), in meters per meter Best fitness value, in meters Root-mean-square deviation (RMSD), in meters

0.0

1.0

2.0

3.0

10 20 40 60 80

Number of sites selected for removal from the existing

monitoring network (nr

)

30 50 70 10 20 40 60 80

Number of sites selected for removal from the existing

monitoring network (nr)

30 50 70

10 20 40 60 80

Number of sites selected for removal from the existing

monitoring network (nr

)

30 50 70 10 20 40 60 80

Number of sites selected for removal from the existing

monitoring network (nr)

30 50 70

10 20 40 60 80

Number of sites selected for removal from the existing

monitoring network (nr

)

30 50 70 10 20 40 60 80

Number of sites selected for removal from the existing

monitoring network (nr

)

30 50 70 1,370.0

1,390.0

1,410.0

1,430.0

1,450.0

Best fitness value, in meters

1,149.0

1,148.0

1,147.0

1,146.0

1,145.0

1,144.0

0.00

0.50

1.00

1.50

2.00

Root-mean-square deviation (RMSD), in meters

0.00

0.02

0.04

0.06

Percent local error (PLE), in meters per meter

0.0

0.8

1.2

1.6

0.4

A. Co-op network, best fitness value D. USGS-INL network, best fitness value

E. USGS-INL network, RMSD

C. Co-op network, PLE F. USGS-INL network, PLE

B. Co-op network, RMSD

Figure 13. Sensitivity of the best fitness value, root-mean-square deviation, and percent local error to changes in the 

number of sites removed from the existing (A–C) Federal-State Cooperative and (D–F) U.S. Geological Survey-Idaho 

National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho.
40 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 14a

A. 10 wells removed

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

40 Miles

0 20

0 20

40 Kilometers

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

Idaho National Laboratory boundary

Eastern Snake River Plain boundary Well in the optimized Federal-State Cooperative 

water-level monitoring network — 156 wells

Well removed from Federal-State Cooperative 

water-level monitoring network, as of 2008 — 10 wells

EXPLANATION

Difference between the kriged surface using the existing network and reduced network—in meters.

-17.5 -15 -10 0 10 20 25.5

Figure 14. Difference between kriged water-table surfaces using the existing and reduced Federal-State 

Cooperative water-level monitoring network, after removing (A) 10, (B) 20, (C) 40, (D) 60, and (E) 80 optimally 

selected wells, eastern Snake River Plain, Idaho.
Results and Discussion 41

tac13-0832_fig 14b

B. 20 wells removed

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

40 Miles

0 20

0 20

40 Kilometers

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

Idaho National Laboratory boundary

Eastern Snake River Plain boundary Well in the optimized Federal-State Cooperative 

water-level monitoring network — 146 wells

Well removed from Federal-State Cooperative 

water-level monitoring network, as of 2008 — 20 wells

EXPLANATION

Difference between the kriged surface using the existing network and reduced network—in meters.

-17.5 -15 -10 0 10 20 25.5

Figure 14.—Continued
42 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 14c

Difference between the kriged surface using the existing network and reduced network—in meters.

-17.5 -15 -10 0 10 20 25.5

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

40 Miles

0 20

0 20

40 Kilometers

EXPLANATION

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters..

Well in the optimized Federal-State Cooperative 

water-level monitoring network—126 wells

Well removed from Federal-State Cooperative 

water-level monitoring network, as of 2008—40 wells

Idaho National Laboratory boundary

Eastern Snake River Plain boundary

C. 40 wells removed

Figure 14.—Continued
Results and Discussion 43

tac13-0832_fig 14d

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin FallsTwin Falls

Pocatello

Idaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

40 Miles

0 20

0 20

40 Kilometers

EXPLANATION

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

Idaho National Laboratory boundary

Eastern Snake River Plain boundary Well in the optimized Federal-State Cooperative 

water-level monitoring network — 106 wells

Well removed from Federal-State Cooperative 

water-level monitoring network, as of 2008 — 60 wells

Difference between the kriged surface using the existing network and reduced network—in meters.

-17.5 -10 0 10 20 25.5

D. 60 wells removed

-15

Figure 14.—Continued
44 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 14e

113° 112°

43°

43°

30'

42°

30'

115° 114°

44°

44°

30'

Twin FallsTwin Falls

Pocatello

Idaho FallsIdaho Falls

Yellowstone

Plateau

Thousand

Springs

Area

Big

Lost River

Little

Lost

River

Birch

Creek

Snake River

Little

Wood

Wood

Big

Raft

Blackfoot River

Portneuf

Malad

American Falls

Reservoir

River

River

Camas Creek

Lake

Walcott

40 Miles

0 20

0 20

40 Kilometers

EXPLANATION

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

E. 80 wells removed

Difference between the kriged surface using the existing network and reduced network—in meters.

-17.5 -10 0 10 20 25.5

Idaho National Laboratory boundary

Eastern Snake River Plain boundary Well in the optimized Federal-State Cooperative 

water-level monitoring network — 86 wells

Well removed from Federal-State Cooperative 

water-level monitoring network, as of 2008 — 80 wells

-15

Figure 14.—Continued
Results and Discussion 45

tac13-0832_fig 15a

A. 10 wells removed

Big Southern

Butte

Middle

Butte

East

Butte

Big Lost River

spreading areas

Atomic

City

Howe

Mud

Lake Terreton

Mud Lake

Big

River

Big Lost River

Sinks and 

Playas

Lost River

Lost

Little

113°

43°

30'

113°12' 112°48' 112°36' 112°24'

43°

36'

43°

42'

43°

48'

43°

54'

43°

24'

10 Miles

0 5

0 5

10 Kilometers

EXPLANATION

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

Well in the optimized U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network — 161 wells

Well removed from the U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network, as of 2008 — 10 wells

Idaho National Laboratory boundary

Eastern Snake River Plain boundary

Difference between the kriged surface using the existing network and reduced network—in meters.

-1.0 -0.5 0.0 0.3

Figure 15. Difference between kriged water-table surfaces using the existing and reduced U.S. Geological 

Survey-Idaho National Laboratory water-level monitoring network, after removing (A) 10, (B) 20, (C) 40, (D) 60, 

and (E) 80 optimally selected wells, Idaho National Laboratory and vicinity, Idaho.
46 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 15b

Big Southern

Butte

Middle

Butte

East

Butte

Big Lost River

spreading areas

Atomic

City

Howe

Mud

Lake Terreton

Mud Lake

Big

River

Big Lost River

Sinks and 

Playas

Lost River

Lost

Little

113°

43°

30'

113°12' 112°48' 112°36' 112°24'

43°

36'

43°

42'

43°

48'

43°

54'

43°

24'

10 Miles

0 5

0 5

10 Kilometers

EXPLANATION

Well in the optimized U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network — 151 wells

Well removed from the U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network, as of 2008 — 20 wells

Idaho National Laboratory boundary

Eastern Snake River Plain boundary

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

B. 20 wells removed

Difference between the kriged surface using the existing network and reduced network—in meters.

-1.0 -0.5 0.0 0.3

Figure 15.—Continued
Results and Discussion 47

tac13-0832_fig 15c

Big Southern

Butte

Middle

Butte

East

Butte

Big Lost River

spreading areas

Atomic

City

Howe

Mud

Lake Terreton

Mud Lake

Big

River

Big Lost River

Sinks and 

Playas

Lost River

Lost

Little

113°

43°

30'

113°12' 112°48' 112°36' 112°24'

43°

36'

43°

42'

43°

48'

43°

54'

43°

24'

10 Miles

0 5

0 5

10 Kilometers

EXPLANATION

Well in the optimized U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network — 131 wells

Well removed from the U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network, as of 2008 — 40 wells

Idaho National Laboratory boundary

Eastern Snake River Plain boundary

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

C. 40 wells removed

Difference between the kriged surface using the existing network and reduced network—in meters.

-1.0 -0.5 0.0 0.3

Figure 15.—Continued
48 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

tac13-0832_fig 15d

Big Southern

Butte

Middle

Butte

East

Butte

Big Lost River

spreading areas

Atomic

City

Howe

Mud

Lake Terreton

Mud Lake

Big

River

Big Lost River

Sinks and 

Playas

Lost River

Lost

Little

113°

43°

30'

113°12' 112°48' 112°36' 112°24'

43°

36'

43°

42'

43°

48'

43°

54'

43°

24'

D. 60 wells removed

10 Miles

0 5

0 5

10 Kilometers

EXPLANATION

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

Idaho National Laboratory boundary

Eastern Snake River Plain boundary Well in the optimized U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network — 111 wells

Well removed from the U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network, as of 2008 — 60 wells

Difference between the kriged surface using the existing network and reduced network—in meters.

-1.0 -0.5 0.0 0.3

Figure 15.—Continued
Results and Discussion 49

E. 80 wells removed

Big Southern

Butte

Middle

Butte

East

Butte

Big Lost River

spreading areas

Atomic

City

Howe

Mud

Lake Terreton

Mud Lake

Big

River

Big Lost River

Sinks and 

Playas

Lost River

Lost

Little

113°

43°

30'

113°12' 112°48' 112°36' 112°24'

43°

36'

43°

42'

43°

48'

43°

54'

43°

24'

10 Miles

0 5

0 5

10 Kilometers

EXPLANATION

Albers Equal-Area Conic projection, standard parallels 42°50’N, 44°10’N; 

central meridian 113°00’W; North American Datum of 1927. Uniform 

kriging-grid block size (resolution) of 500 meters by 500 meters.

Idaho National Laboratory boundary

Eastern Snake River Plain boundary Well in the optimized U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network — 91 wells

Well removed from the U.S. Geological Survey-Idaho 

National Laboratory water-table monitoring 

network, as of 2008 — 80 wells

Difference between the kriged surface using the existing network and reduced network—in meters.

-1.0 -0.5 0.0 0.3

tac13-0832_fig 15e

Figure 15.—Continued
50 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method

Summary and Conclusions

Budgetary constraints and the high cost of long-term 

groundwater level monitoring in the eastern Snake River 

Plain (ESRP) aquifer have necessitated a reduction in the 

number of observation wells in the existing networks. As 

of 2008, long-term groundwater water-level monitoring 

networks in the ESRP included a Federal-State Cooperative 

water-level monitoring network (Co-op network) with 

166 observation wells, and a U.S. Geological SurveyIdaho National Laboratory water-level monitoring network 

(USGS-INL network) with 171 wells. The spatial distribution 

of observation wells in the Co-op network covers most of 

the ESRP, and USGS-INL network coverage is limited to 

the Idaho National Laboratory and vicinity. The planning 

objective for both networks is to reduce monitoring costs by 

removing observation wells that add little or no information 

characterizing the water table. To accomplish this objective, 

a reduced monitoring network was selected that satisfies the 

following design criteria: (1) interpolation error is minimized, 

(2) local anomalies in the water-table distribution are 

preserved, (3) variability of water-level measurements across 

time is preserved, and (4) measurement error is kept as small 

as possible. The total number of wells to remove from the 

existing network is left as a management decision.

The water-level monitoring networks were optimized 

using a genetic algorithm (GA) with universal kriging and 

statistical analysis. A series of GA runs were conducted 

for some of the control parameters to better understand 

the sensitivity of the algorithm to incremental changes in 

the parameters, and to determine reasonable settings for 

optimizing the existing monitoring networks. The network 

design tool is most sensitive to the number of wells removed 

from the original network and the spatial resolution of the 

kriging grid. As a compromise between solution accuracy 

and computational effort, existing water-level monitoring 

networks were optimized using the following control 

parameter settings: a kriging grid resolution of 2.5 kilometers 

for the Co-op network and 1.5 kilometers for the USGSINL network, population size of 2,000, elitism rate of 0.05 

(5 percent), crossover probability of 0.80 (80 percent), 

mutation probability of 0.30 (30 percent), and terminates 

after 50 consecutive iterations without any improvement in 

the best fitness. Each water-level monitoring network was 

optimized five times: by removing (1) 10, (2) 20, (3) 40, 

(4) 60, and (5) 80 observation wells from the original network. 

An examination of the trade-offs associated with changes in 

the number of wells to remove indicates that 20 wells (12 

percent of the total number of wells in the original network) 

can be removed from the Co-op network with a relatively 

small degradation of the estimated water-table map, and 

40 wells (23 percent) can be removed from the USGS-INL 

network before the water-table map degradation accelerates. 

The optimal network designs indicate the robustness of the 

network design tool. Observation wells were removed from 

high well-density areas of the network while retaining the 

spatial pattern of the existing water-table map.

Acknowledgments

The author would like to thank R.J. Weakland of the 

USGS for her assistance during the early stages of this study.

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Table 5 53

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

12S 22E 35BCC1 1 422013113510501 Co-op 113°51′07′′ 42°20′12′′ 1,338.18 1.524 196 6 1,187.09 0.003 1.53 7.92 -33.17

12S 21E 16DCC1 2 422227113595901 Co-op 114°00′02′′ 42°22′26′′ 1,335.41 0.030 322 6 1,293.11 0.003 0.03 3.87 90.9112S 20E 04DBC1 3 422424114070001 Co-op 114°07′03′′ 42°24′23′′ 1,317.73 0.152 507 10 1,240.17 0.003 0.16 3.32 5.6712S 21E 02DAA1 4 422434113570201 Co-op 113°57′04′′ 42°24′34′′ 1,330.30 0.030 1,830 6 1,184.20 0.003 0.03 8.75 -33.91

11S 23E 34CDC1 5 422458113452701 Co-op 113°45′30′′ 42°24′57′′ 1,302.83 0.030 316 6 1,190.30 0.003 0.03 5.54 -50.10

11S 22E 32CCC1 6 422501113543901 Co-op 113°54′42′′ 42°25′00′′ 1,314.59 0.030 400 6 1,183.58 0.003 0.03 5.32 -0.85

11S 20E 33DAD1 7 422518114062701 Co-op 114°06′30′′ 42°25′19′′ 1,294.87 0.003 694 2 1,221.60 0.003 0.01 10.31 -10.09

11S 18E 25DDC1 8 422555114172101 Co-op 114°17′21′′ 42°25′55′′ 1,263.45 0.762 8 2 1,238.56 0.003 0.77 0.36 0.7511S 17E 25DDD2 9 422600114240901 Co-op 114°24′12′′ 42°25′59′′ 1,262.38 0.003 853 4 1,234.74 0.003 0.01 1.42 24.0911S 19E 30ADD1 10 422621114160501 Co-op 114°16′08′′ 42°26′20′′ 1,267.96 0.003 576 6 1,236.94 0.003 0.01 1.62 5.9311S 23E 14DDD1 11 422739113434001 Co-op 113°43′43′′ 42°27′38′′ 1,290.29 1.524 180 6 1,279.36 0.003 1.53 1.23 74.5911S 24E 14BDB1 12 422810113372001 Co-op 113°37′23′′ 42°28′09′′ 1,329.93 3.048 38 2 1,233.04 0.003 3.05 9.72 -29.08

11S 15E 02BBB1 13 423018114401701 Co-op 114°40′20′′ 42°30′17′′ 1,263.42 1.524 234 4 1,195.03 0.003 1.53 0.91 51.7010S 20E 27BCC1 14 423134114062601 Co-op 114°06′31′′ 42°31′34′′ 1,275.68 0.003 203 2 1,165.95 0.003 0.01 3.70 -11.17

10S 21E 28BCB1 15 423145114003001 Co-op 114°00′33′′ 42°31′44′′ 1,268.09 0.003 409 10 1,165.71 0.003 0.01 3.53 -10.71

10S 21E 26AAA2 16 423159113570302 Co-op 113°57′06′′ 42°31′58′′ 1,268.62 0.762 274 10 1,181.32 0.003 0.77 2.48 4.8910S 22E 20CDC1 17 423206113542301 Co-op 113°54′26′′ 42°32′05′′ 1,265.73 0.003 408 9 1,186.31 0.003 0.01 3.87 -6.19

10S 18E 20DDD1 18 423207114215301 Co-op 114°21′56′′ 42°32′06′′ 1,195.43 1.524 376 2 1,141.39 0.003 1.53 1.20 -20.47

10S 17E 14CCD1 19 423255114260601 Co-op 114°26′09′′ 42°32′54′′ 1,155.48 1.524 226 4 1,135.72 0.003 1.53 4.21 14.1010S 16E 07DAC1 20 423406114370301 Co-op 114°37′07′′ 42°34′06′′ 1,153.04 1.524 220 4 1,143.32 0.003 1.53 1.32 16.7609S 22E 33ADA1 21 423604113522401 Co-op 113°52′27′′ 42°36′03′′ 1,278.80 0.003 211 2 1,202.09 0.003 0.01 2.43 1.9209S 16E 21DCD1 22 423722114345101 Co-op 114°34′54′′ 42°37′21′′ 1,081.39 1.524 204 6 1,076.95 0.003 1.53 0.50 11.5909S 25E 23DBA1 23 423732113295801 Co-op 113°30′01′′ 42°37′31′′ 1,301.54 0.003 2,448 11 1,253.88 0.003 0.01 2.60 4.24

09S 17E 20CAA1 24 423747114293101 Co-op 114°29′32′′ 42°37′46′′ 1,107.91 1.524 218 6 1,013.45 0.003 1.53 1.90 -80.54

09S 26E 13CCC2 25 423802113222701 Co-op 113°22′26′′ 42°38′01′′ 1,305.50 0.762 9 2 1,253.51 0.003 0.77 0.91 -4.34

09S 29E 18CDA1 26 423808113063601 Co-op 113°06′38′′ 42°38′07′′ 1,296.18 0.003 128 3 1,280.78 0.003 0.01 1.60 -2.88

09S 25E 18DDA1 27 423811113341201 Co-op 113°34′15′′ 42°38′10′′ 1,267.71 0.762 434 11 1,253.27 0.003 0.77 2.57 6.0709S 14E 13DDD1 28 423814114450901 Co-op 114°45′12′′ 42°38′13′′ 1,071.94 3.048 216 4 1,062.50 0.003 3.05 7.98 46.3509S 22E 16CDB1 29 423817113530201 Co-op 113°53′05′′ 42°38′16′′ 1,281.41 1.524 172 2 1,198.66 0.003 1.53 3.17 -2.52

09S 28E 18BAD1 30 423837113134301 Co-op 113°13′44′′ 42°38′36′′ 1,286.25 0.003 215 3 1,281.20 0.003 0.01 0.44 3.1409S 26E 10DDD1 31 423855113233901 Co-op 113°23′42′′ 42°38′54′′ 1,286.35 0.030 1,022 6 1,253.89 0.003 0.03 2.91 -5.86

Table 5. Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, during 2008.[Local name: local well identifier used in this study. Map No.: identifier used to locate wells on maps located in figures and as a cross reference with data in other tables. Site No.: unique numerical identifiers used to access well data (http://waterdata.usgs.gov/nwis). Network name: name(s) of long-term water-table monitoring network, as of 2008. Longitude and Latitude: in degrees, minutes, and seconds, and 

are in conformance with the North American Datum of 1983. Reference point elevation: land-surface reference point, in meters above North American Vertical Datum of 1988 (NAVD 88). Reference point location error: accuracy of reference point. Sample size: number of water-level measurements recorded for the period-of-record (POR) and calendar year 2008. 2008 water-level elevation: median waterlevel elevation for calendar year 2008 (z), in meters above the NAVD 88. 2008 measurement method error: accuracy of method used to measure depth to water during calendar year 2008. 2008 measurement 

error: mean accuracy of water-level elevation measurements for calendar year 2008 (εz). Standard deviation: of water-level elevation measurements for the entire period-of-record (σz); duration varies for 

each well site. Estimation error: determined from leave-one-out cross validation (z - z*). Abbreviations: Co-op, Federal-State Cooperative water-level monitoring network; USGS-INL, U.S. Geological Survey-Idaho National Laboratory water-level monitoring network; m, meter]
54 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 5.

Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, 

during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

09S 26E 07AAB1 32 423943113272001 Co-op 113°27′23′′ 42°39′42′′ 1,281.10 0.152 264 6 1,252.98 0.003 0.16 3.08 -3.84

09S 25E 03CAC1 33 424003113313101 Co-op 113°31′34′′ 42°40′02′′ 1,267.92 0.003 741 9 1,248.31 0.003 0.01 1.96 1.1809S 29E 04BCA1 34 424013113043801 Co-op 113°04′44′′ 42°40′13′′ 1,289.27 0.003 770 3 1,287.65 0.003 0.01 0.84 -1.49

08S 27E 31DDA1 35 424042113201101 Co-op 113°20′13′′ 42°40′39′′ 1,281.87 0.003 921 6 1,270.67 0.003 0.01 1.38 1.8508S 29E 34CBC1 36 424052113033901 Co-op 113°03′41′′ 42°40′51′′ 1,338.87 0.003 230 3 1,292.34 0.003 0.01 0.98 1.65

08S 24E 31DAC1 37 424053113412801 Co-op 113°41′31′′ 42°40′52′′ 1,289.20 0.003 2,642 10 1,235.85 0.003 0.01 2.29 -1.41

09S 14E 03BAA1 38 424053114480301 Co-op 114°48′07′′ 42°40′52′′ 978.95 0.030 339 6 955.57 0.003 0.03 0.59 -40.69

08S 25E 36DAA1 39 424102113282101 Co-op 113°28′24′′ 42°41′01′′ 1,283.86 0.003 1,198 9 1,244.72 0.003 0.01 2.75 -12.64

08S 17E 33DAD2 40 424105114274901 Co-op 114°27′50′′ 42°41′06′′ 1,165.84 1.524 190 6 1,086.23 0.003 1.53 1.07 49.7508S 26E 33BCB2 41 424112113255402 Co-op 113°25′57′′ 42°41′11′′ 1,284.99 0.003 607 6 1,277.74 0.003 0.01 0.48 28.2608S 15E 32BBA1 42 424144114434101 Co-op 114°43′43′′ 42°41′44′′ 1,010.96 1.524 8 2 984.82 0.003 1.53 1.89 -18.92

08S 23E 27BDC1 43 424201113452701 Co-op 113°45′30′′ 42°42′00′′ 1,291.63 0.003 196 2 1,229.13 0.003 0.01 2.86 4.8208S 27E 23DDD1 44 424221113152501 Co-op 113°15′28′′ 42°42′22′′ 1,310.52 0.003 130 5 1,285.86 0.003 0.01 0.96 6.9208S 16E 17CCC1 45 424331114365001 Co-op 114°36′54′′ 42°43′30′′ 1,064.61 1.524 132 7 1,012.17 0.003 1.53 1.74 -23.53

08S 25E 16DAC1 46 424334113320201 Co-op 113°32′05′′ 42°43′33′′ 1,294.36 0.003 132 2 1,238.59 0.003 0.01 3.29 -3.68

08S 14E 16CBB1 47 424353114494701 Co-op 114°49′49′′ 42°43′52′′ 968.66 0.003 2,722 6 955.92 0.003 0.01 0.35 2.1408S 27E 07DBC1 48 424419113201801 Co-op 113°20′25′′ 42°44′19′′ 1,319.25 3.048 65 5 1,265.42 0.003 3.05 0.78 4.0108S 14E 12CBC1 49 424439114461201 Co-op 114°46′16′′ 42°44′39′′ 998.15 1.524 206 6 975.97 0.003 1.53 1.00 5.8408S 26E 03DCC1 50 424454113240101 Co-op 113°24′04′′ 42°44′53′′ 1,325.81 0.003 623 9 1,240.68 0.003 0.01 2.34 -16.43

08S 19E 05DAB1 51 424529114150901 Co-op 114°15′12′′ 42°45′28′′ 1,243.15 0.003 999 6 1,152.31 0.003 0.01 1.89 17.7108S 28E 01AAA2 52 424543113071002 Co-op 113°07′13′′ 42°45′43′′ 1,371.09 0.152 114 3 1,299.60 0.003 0.16 1.12 3.6007S 14E 33BBB1 53 424653114494601 Co-op 114°49′49′′ 42°46′51′′ 997.84 1.524 190 6 964.61 0.003 1.53 1.07 10.3907S 30E 24DDC1 54 424730112531701 Co-op 112°53′20′′ 42°47′29′′ 1,340.41 0.003 306 2 1,322.23 0.003 0.01 1.94 6.41

07S 26E 14CCC1 55 424826113233201 Co-op 113°23′35′′ 42°48′25′′ 1,343.06 0.003 677 8 1,241.24 0.003 0.01 2.26 -5.83

07S 25E 19BAA1 56 424828113345201 Co-op 113°34′55′′ 42°48′27′′ 1,317.83 0.003 2,289 10 1,236.17 0.003 0.01 2.25 0.8807S 29E 12CCC2 57 424916113012001 Co-op 113°01′20′′ 42°49′16′′ 1,392.45 1.524 7 3 1,312.98 0.003 1.53 0.92 0.5107S 15E 12CBA3 58 424955114390303 Co-op 114°39′06′′ 42°49′54′′ 1,098.10 0.003 137 6 1,043.17 0.003 0.01 1.97 22.7606S 24E 32DBA1 59 425118113370801 Co-op 113°37′12′′ 42°51′18′′ 1,321.05 0.305 30 2 1,235.00 0.003 0.31 1.41 1.6506S 22E 28CDD1 60 425155113503901 Co-op 113°50′42′′ 42°51′54′′ 1,287.99 0.003 120 5 1,220.44 0.003 0.01 2.57 -3.32

06S 32E 27ADC1 61 425216112414301 Co-op 112°41′45′′ 42°52′15′′ 1,347.25 0.003 2,045 6 1,336.33 0.008 0.01 0.80 1.4106S 19E 19CCD1 62 425250114145101 Co-op 114°14′54′′ 42°52′49′′ 1,232.28 3.048 35 1 1,158.72 0.003 3.05 1.96 -9.99

06S 29E 15BBC1 63 425412113035601 Co-op 113°04′00′′ 42°54′09′′ 1,442.76 3.048 174 3 1,314.94 0.003 3.05 1.03 6.67

06S 13E 18ABC1 64 425421114572901 Co-op 114°57′32′′ 42°54′20′′ 863.39 3.658 526 6 866.33 0.003 3.66 7.61 -88.53

06S 31E 16BAB1 65 425427112503801 Co-op 112°50′39′′ 42°54′26′′ 1,339.77 0.030 446 2 1,333.26 0.003 0.03 1.28 2.61

06S 34E 09BCB1 66 425456112294001 Co-op 112°29′42′′ 42°54′54′′ 1,361.06 1.524 46 4 1,340.65 0.003 1.53 0.39 -7.66

06S 13E 08BDA2 67 425511114562301 Co-op 114°56′26′′ 42°55′10′′ 991.42 3.658 199 6 957.50 0.003 3.66 7.25 71.88

05S 33E 35CDC1 68 425608112340901 Co-op 112°34′11′′ 42°56′07′′ 1,349.64 0.003 1,770 6 1,341.09 0.003 0.01 0.35 1.79

05S 15E 35DBD2 69 425635114382302 Co-op 114°38′26′′ 42°56′34′′ 1,106.46 0.030 434 11 1,060.08 0.003 0.03 3.07 -10.15

05S 28E 26BBD1 70 425746113093901 Co-op 113°09′43′′ 42°57′45′′ 1,507.08 0.305 151 2 1,296.49 0.003 0.31 1.08 7.80
Table 5 55

Table 5. Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

05S 17E 26ACA1 71 425746114240101 Co-op 114°24′07′′ 42°57′45′′ 1,211.72 0.003 2,616 6 1,141.81 0.003 0.01 3.37 -4.02

05S 31E 19DDC2 72 425754112521601 Co-op 112°52′15′′ 42°58′00′′ 1,348.25 3.048 34 2 1,332.45 0.003 3.05 0.77 -2.35

05S 31E 27ABA1 73 425757112485201 Co-op 112°48′57′′ 42°57′56′′ 1,342.10 0.030 2,253 3 1,334.65 0.003 0.03 1.21 -2.58

05S 25E 22DAD1 74 425812113271201 Co-op 113°27′15′′ 42°58′11′′ 1,397.98 0.003 178 4 1,240.29 0.003 0.01 2.51 2.1805S 34E 20CBB2 75 425816112305102 Co-op 112°30′53′′ 42°58′15′′ 1,358.91 0.762 240 3 1,341.26 0.003 0.77 0.70 -1.71

05S 30E 12BBA1 76 430030112541301 Co-op 112°54′15′′ 43°00′32′′ 1,373.10 0.030 263 2 1,337.78 0.003 0.03 1.17 5.2005S 14E 12AAA1 77 430040114435501 Co-op 114°43′58′′ 43°00′39′′ 1,100.90 3.048 187 6 1,061.87 0.003 3.05 2.19 8.9804S 31E 36ABA1 78 430216112464001 Co-op 112°46′43′′ 43°02′18′′ 1,342.69 0.030 236 2 1,340.15 0.003 0.03 0.76 -1.71

04S 33E 20CBB1 79 430333112375801 Co-op 112°38′00′′ 43°03′32′′ 1,347.93 0.305 217 5 1,338.10 0.003 0.31 0.32 -3.87

04S 31E 20BBB1 80 430402112520301 Co-op 112°52′03′′ 43°04′00′′ 1,379.75 0.030 159 2 1,341.24 0.003 0.03 0.87 0.3604S 31E 11ABA1 81 430547112473701 Co-op 112°47′40′′ 43°05′46′′ 1,361.96 1.524 152 3 1,341.97 0.003 1.53 0.73 -1.67

04S 32E 01CBA1 82 430607112400501 Co-op 112°40′07′′ 43°06′06′′ 1,357.38 1.524 225 5 1,343.78 0.003 1.53 0.99 2.4604S 31E 05CBC1 83 430607112515601 Co-op 112°51′58′′ 43°06′06′′ 1,400.98 0.152 33 3 1,342.33 0.003 0.16 1.06 1.6904S 33E 03CBB2 84 430610112353301 Co-op 112°35′36′′ 43°06′10′′ 1,356.75 0.030 1,400 3 1,343.87 0.003 0.03 0.69 1.08

04S 24E 06BBC1 85 430626113391001 Co-op 113°39′13′′ 43°06′25′′ 1,370.55 0.003 1,985 4 1,236.38 0.003 0.01 1.47 -8.28

03S 33E 25CCC1 86 430729112331201 Co-op 112°33′14′′ 43°07′28′′ 1,357.51 0.003 228 5 1,345.00 0.003 0.01 0.61 -0.23

03S 27E 24DDA1 87 430836113143401 Co-op 113°14′37′′ 43°08′35′′ 1,519.60 0.030 174 4 1,253.30 0.003 0.03 2.66 -44.93

03S 34E 22DAB1 88 430843112272701 Co-op 112°27′29′′ 43°08′42′′ 1,354.22 0.003 219 4 1,348.57 0.003 0.01 0.79 0.8603S 31E 16CCB1 89 430930112505701 Co-op 112°51′01′′ 43°09′30′′ 1,415.31 1.524 126 2 1,343.38 0.003 1.53 0.79 2.2703S 33E 17AAD1 90 430955112365001 Co-op 112°36′54′′ 43°09′57′′ 1,376.50 0.030 227 2 1,345.93 0.003 0.03 0.62 -0.25

03S 33E 14BBA1 91 431006112340901 Co-op 112°34′11′′ 43°10′05′′ 1,360.90 0.030 319 2 1,346.90 0.003 0.03 0.66 0.9703S 34E 02BCC3 92 431126112271503 Co-op 112°27′17′′ 43°11′25′′ 1,356.18 0.305 232 5 1,347.77 0.003 0.31 0.87 -1.72

03S 32E 04ACA2 93 431138112425801 Co-op 112°43′00′′ 43°11′37′′ 1,383.29 1.524 5 2 1,346.69 0.003 1.53 1.24 2.37

02S 34E 33BBA1 94 431242112292801 Co-op 112°29′31′′ 43°12′40′′ 1,359.49 0.030 389 3 1,348.25 0.003 0.03 0.88 0.2202S 35E 22DAC1 95 431349112202001 Co-op 112°20′22′′ 43°13′48′′ 1,380.28 0.762 160 3 1,352.95 0.003 0.77 1.40 -11.9302S 35E 11DDD1 96 431517112190101 Co-op 112°19′03′′ 43°15′16′′ 1,378.27 0.003 229 5 1,369.68 0.003 0.01 4.02 10.0302S 33E 16ABB1 97 431520112360901 Co-op 112°36′11′′ 43°15′19′′ 1,389.68 0.152 112 2 1,347.06 0.003 0.16 0.81 -0.68

02S 20E 01ACC2 98 431642114013002 Co-op 114°01′30′′ 43°16′46′′ 1,461.03 0.003 1,176 3 1,415.78 0.003 0.01 1.37 45.8901S 35E 36CDD1 99 431702112182401 Co-op 112°18′24′′ 43°17′01′′ 1,384.86 1.524 14 3 1,365.92 0.003 1.53 0.41 -2.27

01S 23E 26CCC1 100 431810113413601 Co-op 113°41′42′′ 43°18′09′′ 1,534.17 0.305 193 4 1,236.73 0.003 0.31 2.72 -144.73

01S 34E 21DAC1 101 431902112284301 Co-op 112°28′45′′ 43°18′59′′ 1,386.96 1.524 124 3 1,350.81 0.003 1.53 1.04 -1.67

01S 32E 22BDB1 102 431929112421701 Co-op 112°42′20′′ 43°19′28′′ 1,445.77 1.524 125 2 1,346.82 0.003 1.53 0.90 -0.75

01S 22E 18DBD2 103 432007113525401 Co-op 113°52′54′′ 43°20′07′′ 1,468.65 3.048 16 3 1,443.30 0.003 3.05 2.66 95.3102N 31E 35DCC1

USGS 1

104 432700112470801 Co-op,

USGS-INL

112°47′11′′ 43°26′59′′ 1,531.86 0.003 2,033 14 1,350.69 0.003 0.01 1.10 -0.70

02N 26E 22DDA1 105 432854113201001 Co-op 113°20′13′′ 43°28′53′′ 1,635.37 0.003 121 2 1,433.87 0.003 0.01 0.44 97.2202N 38E 16ADD1 106 433029111590201 Co-op 111°59′04′′ 43°30′28′′ 1,445.22 0.762 147 2 1,408.74 0.003 0.77 3.13 -4.95

02N 37E 02ABA1 107 433220112040701 Co-op 112°04′09′′ 43°32′19′′ 1,441.24 0.003 678 9 1,391.24 0.003 0.01 2.46 -7.76

03N 38E 22BAB1 108 433457111583701 Co-op 111°58′40′′ 43°34′57′′ 1,461.07 1.524 196 3 1,421.90 0.003 1.53 4.26 -7.59
56 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 5.

Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, 

during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

03N 37E 12BDB1 109 433625112031801 Co-op 112°03′20′′ 43°36′24′′ 1,449.52 0.003 616 8 1,415.38 0.003 0.01 3.44 4.9703N 37E 02CBD1 110 433656112043901 Co-op 112°04′41′′ 43°36′55′′ 1,458.02 3.048 209 2 1,407.96 0.003 3.05 3.12 -5.84

04N 39E 26DAA1 111 433849111492601 Co-op 111°49′28′′ 43°38′48′′ 1,501.32 0.610 292 6 1,485.97 0.003 0.61 7.06 24.6304N 35E 14AAA1

USBR SITE 15

112 434102112180701 Co-op,

USGS-INL

112°18′09′′ 43°41′01′′ 1,506.59 0.003 922 6 1,376.63 0.003 0.01 1.62 -10.21

04N 38E 12BBB1 113 434153111563201 Co-op 111°56′34′′ 43°41′54′′ 1,473.14 0.003 233 3 1,464.76 0.003 0.01 2.64 3.07

05N 37E 21DBB1 114 434453112063601 Co-op 112°06′38′′ 43°44′52′′ 1,456.36 0.003 197 5 1,454.48 0.003 0.01 0.77 14.6205N 39E 08DAD1 115 434638111530401 Co-op 111°53′06′′ 43°46′38′′ 1,473.39 0.003 327 3 1,471.07 0.003 0.01 0.37 0.0905N 36E 02BDA2 116 434748112113602 Co-op 112°11′38′′ 43°47′47′′ 1,453.00 0.003 285 6 1,448.68 0.003 0.01 2.36 17.3106N 35E 32DDD1 117 434756112212101 Co-op 112°21′23′′ 43°47′55′′ 1,460.78 0.003 199 6 1,381.10 0.003 0.01 2.01 -9.75

06N 39E 35CBB2 118 434816111501302 Co-op 111°50′15′′ 43°48′15′′ 1,476.50 0.003 309 5 1,474.19 0.003 0.01 0.90 2.3206N 35E 27DDA1 119 434857112185801 Co-op 112°19′00′′ 43°48′56′′ 1,463.59 0.003 117 2 1,387.06 0.003 0.01 1.80 -24.97

06N 39E 30ADC1 120 434915111540501 Co-op 111°54′07′′ 43°49′14′′ 1,469.29 0.003 359 6 1,465.19 0.003 0.01 1.15 -1.78

06N 38E 25ACB4 121 434917111553102 Co-op 111°55′33′′ 43°49′16′′ 1,472.28 0.003 424 5 1,462.98 0.003 0.01 1.65 -1.10

06N 37E 29ACA2 122 434922112072202 Co-op 112°07′24′′ 43°49′21′′ 1,471.32 0.003 273 2 1,454.03 0.003 0.01 1.03 -2.98

06N 38E 30BAD2 

USBR SITE 1

123 434924112013801 Co-op 112°01′40′′ 43°49′23′′ 1,486.80 0.003 179 2 1,457.22 0.003 0.01 0.76 -3.89

06N 39E 28BBB1 124 434932111523701 Co-op 111°52′39′′ 43°49′31′′ 1,472.88 0.003 315 5 1,470.78 0.003 0.01 0.66 2.7406N 39E 23AAC2 125 435015111495302 Co-op 111°49′55′′ 43°50′14′′ 1,477.49 0.003 567 3 1,464.39 0.003 0.01 1.29 -11.37

06N 35E 21AAB1 126 435028112202601 Co-op 112°20′28′′ 43°50′27′′ 1,459.41 0.003 372 2 1,427.75 0.003 0.01 1.69 22.0606N 39E 16DAA1 127 435048111512701 Co-op 111°51′29′′ 43°50′47′′ 1,474.75 0.003 318 5 1,472.37 0.003 0.01 1.01 6.9006N 40E 15AAA1 128 435115111430201 Co-op 111°43′04′′ 43°51′15′′ 1,494.60 3.048 105 6 1,488.70 0.003 3.05 1.42 -4.28

06N 39E 13ABA1 129 435118111481601 Co-op 111°48′18′′ 43°51′17′′ 1,483.48 0.003 276 5 1,480.29 0.003 0.01 1.39 10.3706N 36E 11ABA3 130 435208112105103 Co-op 112°10′53′′ 43°52′07′′ 1,469.58 0.003 250 5 1,454.71 0.003 0.01 1.53 3.3606N 39E 10BBB1 131 435209111512101 Co-op 111°51′21′′ 43°52′08′′ 1,474.55 0.003 390 6 1,464.22 0.003 0.01 1.55 -3.66

06N 38E 02DBD1 132 435228111563401 Co-op 111°56′36′′ 43°52′27′′ 1,489.95 0.003 199 5 1,463.07 0.003 0.01 1.68 0.1007N 39E 34CCB1 133 435314111511902 Co-op 111°51′21′′ 43°53′13′′ 1,472.75 0.003 945 5 1,464.12 0.003 0.01 1.33 -4.98

07N 33E 34AAA1 134 435357112332001 Co-op 112°33′22′′ 43°53′56′′ 1,459.24 0.305 59 2 1,454.62 0.003 0.31 1.63 40.3107N 35E 26CDD1 135 435359112182501 Co-op 112°18′27′′ 43°53′58′′ 1,461.08 0.305 154 6 1,453.82 0.003 0.31 1.85 6.3107N 37E 28CCD1 136 435402112065001 Co-op 112°06′52′′ 43°54′01′′ 1,479.04 0.003 198 2 1,455.86 0.003 0.01 1.07 -1.94

07N 35E 20CBD1 137 435504112222301 Co-op 112°22′24′′ 43°55′04′′ 1,469.67 0.003 2,909 6 1,451.60 0.003 0.01 2.38 1.0607N 38E 23DBA2 138 435506111563102 Co-op 111°56′34′′ 43°55′05′′ 1,480.10 0.003 2,586 3 1,465.33 0.003 0.01 0.91 3.3007N 40E 19ADD4 139 435516111464002 Co-op 111°46′42′′ 43°55′15′′ 1,481.46 0.003 763 5 1,480.24 0.003 0.01 0.76 -9.71

07N 36E 22ABD4 140 435528112121201 Co-op 112°12′12′′ 43°55′27′′ 1,461.61 0.003 1,171 6 1,455.53 0.003 0.01 0.85 -0.19

07N 34E 24BBA1 141 435540112243901 Co-op 112°24′41′′ 43°55′39′′ 1,461.09 1.524 127 6 1,450.96 0.003 1.53 3.07 0.95

07N 39E 16DBB3 142 435605111515801 Co-op 111°52′00′′ 43°56′04′′ 1,486.23 0.003 1,063 5 1,480.36 0.003 0.01 2.15 12.14

07N 35E 13AAD1 143 435626112164301 Co-op 112°16′45′′ 43°56′25′′ 1,460.93 0.003 247 5 1,454.90 0.003 0.01 1.69 -1.41

07N 39E 07BDA1 144 435705111542701 Co-op 111°54′29′′ 43°57′04′′ 1,486.83 0.003 590 5 1,463.96 0.003 0.01 1.85 -6.63

07N 36E 11ABB1 145 435723112111101 Co-op 112°11′10′′ 43°57′23′′ 1,496.14 1.524 22 6 1,456.71 0.003 1.53 0.53 0.45
Table 5 57

Table 5. Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

07N 36E 09BBB1 146 435728112141301 Co-op 112°14′15′′ 43°57′27′′ 1,462.60 1.524 133 6 1,456.29 0.003 1.53 1.29 0.3107N 34E 04CDC1 147 435728112281101 Co-op 112°28′11′′ 43°57′27′′ 1,461.62 0.003 1,175 6 1,452.08 0.003 0.01 2.36 0.2907N 40E 05DBC1 148 435736111460201 Co-op 111°46′04′′ 43°57′35′′ 1,500.64 0.003 371 5 1,497.55 0.003 0.01 0.99 20.1308N 41E 33ABB1 149 435904111373101 Co-op 111°37′35′′ 43°59′04′′ 1,528.11 1.524 178 6 1,508.19 0.003 1.53 2.57 0.1308N 34E 27CDD1 150 435912112264801 Co-op 112°26′50′′ 43°59′11′′ 1,465.66 3.048 156 6 1,449.94 0.003 3.05 2.84 -5.01

08N 41E 25CBB2 151 435924111343702 Co-op 111°34′39′′ 43°59′23′′ 1,547.93 1.524 24 6 1,527.00 0.003 1.53 3.04 8.5908N 36E 21DCD1 152 440002112131801 Co-op 112°13′20′′ 44°00′01′′ 1,467.18 0.762 157 6 1,456.59 0.003 0.77 1.39 -0.18

08N 34E 17CCC7 153 440058112293605 Co-op 112°29′38′′ 44°00′57′′ 1,466.85 0.003 230 3 1,456.10 0.003 0.01 1.07 5.8308N 34E 11DCC1 154 440151112252301 Co-op 112°25′25′′ 44°01′50′′ 1,485.48 1.524 158 6 1,455.76 0.003 1.53 1.56 2.9408N 40E 06CCC1 155 440236111474701 Co-op 111°47′49′′ 44°02′35′′ 1,552.50 1.524 133 6 1,464.86 0.003 1.53 1.59 0.3608N 36E 03DCD1 156 440239112121101 Co-op 112°12′13′′ 44°02′38′′ 1,477.85 0.762 106 6 1,458.18 0.003 0.77 1.45 2.5908N 40E 01CAD1 157 440253111412101 Co-op 111°41′23′′ 44°02′52′′ 1,574.15 0.003 207 3 1,467.93 0.003 0.01 2.71 -23.57

09N 36E 33CBB1 158 440353112135701 Co-op 112°13′59′′ 44°03′52′′ 1,483.96 1.524 1,416 6 1,456.08 0.003 1.53 1.34 -1.24

09N 34E 29DAB1 159 440447112284401 Co-op 112°28′47′′ 44°04′46′′ 1,475.72 0.762 64 3 1,452.72 0.003 0.77 2.92 -3.01

09N 36E 15CCC1 160 440608112125001 Co-op 112°12′52′′ 44°06′07′′ 1,510.49 1.524 148 4 1,458.53 0.003 1.53 1.59 3.0909N 34E 11ADD1 161 440725112245301 Co-op 112°24′55′′ 44°07′24′′ 1,508.35 1.524 1,638 6 1,456.59 0.003 1.53 0.98 -3.94

09N 40E 05DDD1 162 440752111452901 Co-op 111°45′31′′ 44°07′51′′ 1,688.31 0.003 185 4 1,468.04 0.003 0.01 2.49 -61.99

09N 38E 05BBA1 163 440839112003101 Co-op 112°00′33′′ 44°08′38′′ 1,676.04 1.524 133 6 1,458.43 0.003 1.53 1.38 -66.97

09N 36E 04BAA1 164 440841112133001 Co-op 112°13′30′′ 44°08′40′′ 1,541.89 1.524 154 6 1,457.48 0.003 1.53 1.73 0.3610N 36E 21CCC1 165 441030112135801 Co-op 112°14′06′′ 44°10′27′′ 1,567.80 1.524 90 6 1,457.33 0.003 1.53 1.44 -19.16

11N 39E 07DBC118 MILE RANCH

166 441740111540201 Co-op 111°54′04′′ 44°17′39′′ 1,904.40 1.524 130 6 1,740.53 0.003 1.53 2.88 267.52

01S 30E 15BCA1 

USGS 14 MV-61

167 432019112563201 USGS-INL 112°56′34′′ 43°20′18′′ 1,565.56 0.003 931 5 1,345.81 0.003 0.01 0.68 3.50

01N 30E 29CCB1 

USGS 124

168 432307112583101 USGS-INL 112°58′31′′ 43°23′06′′ 1,556.25 0.003 42 2 1,346.56 0.003 0.01 0.74 0.08

01N 29E 30BBD1 

USGS 11

169 432336113064201 USGS-INL 113°06′45′′ 43°23′35′′ 1,545.51 0.003 540 5 1,344.92 0.003 0.01 0.86 8.59

01N 29E 08BCD1 

USGS 125

170 432602113052801 USGS-INL 113°05′33′′ 43°25′59′′ 1,540.51 0.003 67 4 1,347.10 0.003 0.01 0.84 1.36

01N 30E 10BBA1 

CERRO GRANDE

171 432618112555501 USGS-INL 112°55′56′′ 43°26′17′′ 1,518.73 0.003 437 4 1,347.85 0.003 0.01 1.21 -0.23

02N 29E 35CCC1 

USGS 108

172 432659112582601 USGS-INL 112°58′29′′ 43°26′58′′ 1,534.61 0.003 67 1 1,348.14 0.003 0.01 1.21 0.09

02N 29E 31CDC1 

USGS 109

173 432701113025601 USGS-INL 113°02′58′′ 43°27′00′′ 1,538.36 0.003 131 4 1,346.92 0.003 0.01 1.12 -0.82

02N 29E 33DCC1 

USGS 105

174 432703113001801 USGS-INL 113°00′20′′ 43°27′03′′ 1,554.05 0.003 69 3 1,348.05 0.003 0.01 1.27 0.29
58 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 5.

Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, 

during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

02N 30E 35DAD2 

USGS 110A

175 432717112501502 USGS-INL 112°50′17′′ 43°27′16′′ 1,524.88 0.003 37 2 1,350.85 0.003 0.01 0.92 0.59

02N 27E 33ACC2 

USGS 13

176 432731113143902 USGS-INL 113°14′41′′ 43°27′30′′ 1,639.25 0.003 376 1 1,337.06 0.003 0.01 0.39 -39.02

02N 28E 35AAC1 

USGS 9

177 432740113044501 USGS-INL 113°04′42′′ 43°27′32′′ 1,534.29 0.003 1,885 12 1,347.24 0.003 0.01 0.99 0.05

02N 29E 19DDA1 

A11A31

178 432853113021701 USGS-INL 113°02′19′′ 43°28′52′′ 1,545.04 0.003 14 2 1,348.00 0.003 0.01 0.91 -0.19

02N 29E 24DAD1 

USGS 104

179 432856112560801 USGS-INL 112°56′11′′ 43°28′55′′ 1,521.28 0.003 123 5 1,349.84 0.003 0.01 1.08 -0.22

02N 29E 19BCB1 

USGS 120

180 432919113031501 USGS-INL 113°03′17′′ 43°29′18′′ 1,537.38 0.003 329 12 1,348.21 0.003 0.01 0.94 -0.30

02N 32E 22ABA1 

COREHOLE 1

181 432927112410101 USGS-INL 112°41′03′′ 43°29′26′′ 1,637.88 0.003 162 4 1,350.69 0.003 0.01 1.18 -6.27

02N 29E 20BBA1 

RWMC M6S

182 432931113015001 USGS-INL 113°01′53′′ 43°29′30′′ 1,545.20 0.003 28 1 1,348.68 0.003 0.01 0.74 0.31

02N 28E 21BBB1 

SGS 86

183 432935113080001 USGS-INL 113°08′04′′ 43°29′34′′ 1,548.50 0.003 340 5 1,348.48 0.003 0.01 1.38 4.35

02N 29E 18CCD2 

RWMC M4D

184 432939113030101 USGS-INL 113°03′04′′ 43°29′38′′ 1,532.05 0.003 29 1 1,348.87 0.003 0.01 0.71 0.11

02N 29E 18CCD1 

USGS 88

185 432940113030201 USGS-INL 113°03′04′′ 43°29′39′′ 1,531.40 0.003 308 5 1,348.78 0.003 0.01 4.67 -0.06

02N 30E 16CCA1 

USGS 107

186 432942112532801 USGS-INL 112°53′30′′ 43°29′41′′ 1,499.90 0.003 115 2 1,351.58 0.003 0.01 1.14 -1.47

02N 29E 18DCB1 

USGS 119

187 432945113023401 USGS-INL 113°02′36′′ 43°29′44′′ 1,534.77 0.003 119 4 1,348.61 0.003 0.01 0.96 -0.17

02N 29E 18DCA1 

USGS 118

188 432947113023001 USGS-INL 113°02′33′′ 43°29′46′′ 1,528.85 0.003 72 4 1,348.79 0.003 0.01 0.84 0.15

02N 29E 18CBD1 

USGS 117

189 432955113025901 USGS-INL 113°03′01′′ 43°29′54′′ 1,528.87 0.003 127 4 1,348.77 0.003 0.01 0.93 0.10

02N 29E 18CBA1 

RWMC M1SA

190 432956113030901 USGS-INL 113°03′11′′ 43°29′55′′ 1,528.52 0.003 40 2 1,348.50 0.003 0.01 0.73 -0.33

02N 29E 15CBA1 

USGS 106

191 432959112593101 USGS-INL 112°59′34′′ 43°29′58′′ 1,529.72 0.003 123 2 1,348.61 0.003 0.01 1.38 -0.60

02N 28E 13ADD1 

USGS 89

192 433005113032801 USGS-INL 113°03′34′′ 43°30′05′′ 1,534.28 0.003 297 4 1,348.98 0.003 0.01 2.27 0.37

02N 29E 18ADB1 

RWMC M3S

193 433008113021801 USGS-INL 113°02′21′′ 43°30′07′′ 1,530.04 0.003 48 2 1,348.75 0.003 0.01 0.90 -0.23
Table 5 59

Table 5. Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

02N 29E 18BDA1 

USGS 87

194 433013113024201 USGS-INL 113°02′46′′ 43°30′12′′ 1,530.50 0.003 303 4 1,349.11 0.003 0.01 1.41 0.33

02N 29E 13AAA1 

USGS 83

195 433023112561501 USGS-INL 112°56′18′′ 43°30′22′′ 1,507.10 0.003 359 4 1,352.95 0.003 0.01 1.04 -0.07

02N 29E 17BBA1 

RWMC M7S

196 433023113014801 USGS-INL 113°01′51′′ 43°30′22′′ 1,526.63 0.003 46 2 1,348.96 0.003 0.01 2.43 0.25

02N 29E 11CCA1 

USGS 131

197 433036112581601 USGS-INL 112°58′19′′ 43°30′35′′ 1,518.14 0.003 107 12 1,351.36 0.003 0.01 0.20 -0.98

02N 29E 09CDA2 

USGS 129

198 433036113002701 USGS-INL 113°00′30′′ 43°30′36′′ 1,533.04 0.003 89 11 1,348.57 0.003 0.01 0.15 -0.91

02N 29E 07DAA1 

RWMC M14S

199 433052113025001 USGS-INL 113°02′52′′ 43°30′51′′ 1,534.96 0.003 27 2 1,348.89 0.003 0.01 1.18 -0.52

02N 29E 11ADD1 

USGS 127

200 433058112572201 USGS-INL 112°57′25′′ 43°30′57′′ 1,511.78 0.003 99 4 1,354.78 0.003 0.01 0.97 1.42

02N 29E 08ADC1 

RWMC M11S

201 433058113010401 USGS-INL 113°01′06′′ 43°30′57′′ 1,523.29 0.003 24 2 1,349.30 0.003 0.01 0.66 -0.05

02N 29E 03CCC1 

WMC M12S

202 433118112593401 USGS-INL 112°59′36′′ 43°31′17′′ 1,517.51 0.003 25 2 1,352.50 0.003 0.01 0.81 1.35

02N 27E 02DDC1 

USGS 8

203 433121113115801 USGS-INL 113°12′00′′ 43°31′20′′ 1,584.48 0.003 519 5 1,348.70 0.003 0.01 1.24 -17.60

02N 30E 04DCC1 

SITE 9

204 433123112530101 USGS-INL 112°53′03′′ 43°31′22′′ 1,502.39 0.003 355 4 1,355.85 0.003 0.01 1.21 0.94

02N 29E 01DCA1 

USGS 130

205 433130112562801 USGS-INL 112°56′31′′ 43°31′30′′ 1,502.97 0.003 113 12 1,355.36 0.003 0.01 0.29 0.15

03N 29E 36DDC1 

ICPP 1798

206 433216112562601 USGS-INL 112°56′46′′ 43°32′26′′ 1,504.13 0.003 56 6 1,356.36 0.003 0.01 0.23 0.35

03N 29E 36DCC2 

CFA LF 2-10

207 433216112563301 USGS-INL 112°56′35′′ 43°32′15′′ 1,504.34 0.003 76 5 1,355.51 0.003 0.01 1.28 -0.72

03N 29E 36CCC1 

CFA LF 3-9

208 433216112571001 USGS-INL 112°57′13′′ 43°32′15′′ 1,507.10 0.003 27 2 1,356.33 0.003 0.01 1.40 0.38

02N 35E 02BBC1 

HIGHWAY 1C

209 433218112191601 USGS-INL 112°19′18′′ 43°32′17′′ 1,552.49 0.003 430 4 1,372.09 0.003 0.01 1.43 3.44

03N 31E 35DCA1 

AREA 2

210 433223112470201 USGS-INL 112°47′05′′ 43°32′22′′ 1,564.27 0.003 75 2 1,357.49 0.003 0.01 1.43 0.63

03N 29E 36DAC1 

CFA LF 2-11

211 433230112561701 USGS-INL 112°56′20′′ 43°32′30′′ 1,503.23 0.003 21 2 1,356.27 0.003 0.01 1.84 0.33

03N 29E 36BCB1 

USGS 85

212 433246112571201 USGS-INL 112°57′14′′ 43°32′45′′ 1,506.47 0.003 362 4 1,356.33 0.003 0.01 1.46 -0.05

03N 29E 36BDB3 

USGS 128

213 433250112565601 USGS-INL 112°56′58′′ 43°32′49′′ 1,505.22 0.003 45 6 1,356.28 0.003 0.01 0.76 -0.12
60 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 5.

Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, 

during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

03N 30E 31AAD1 

USGS 20

214 433253112545901 USGS-INL 112°55′02′′ 43°32′52′′ 1,499.18 0.003 715 4 1,355.91 0.003 0.01 1.12 -0.67

03N 32E 36ADD1 

USGS 101

215 433255112381801 USGS-INL 112°38′22′′ 43°32′55′′ 1,601.64 0.003 180 11 1,363.97 0.003 0.01 1.54 3.98

03N 29E 34ADD1 

ICPP MON A-166

216 433300112583301 USGS-INL 112°58′36′′ 43°32′59′′ 1,511.69 0.003 58 4 1,356.37 0.003 0.01 0.38 0.68

03N 34E 32BBC1 

HIGHWAY 2

217 433307112300001 USGS-INL 112°30′02′′ 43°33′06′′ 1,591.10 0.015 385 4 1,367.32 0.003 0.02 1.34 4.03

03N 29E 25DDB1 

USGS 113

218 433314112561801 USGS-INL 112°56′21′′ 43°33′14′′ 1,502.30 0.003 107 4 1,356.74 0.003 0.01 1.68 0.49

03N 29E 25DCA1 

USGS 112

219 433314112563001 USGS-INL 112°56′33′′ 43°33′14′′ 1,503.06 0.003 114 5 1,356.29 0.003 0.01 1.67 -0.24

03N 30E 30CCB1 

USGS 77

220 433315112560301 USGS-INL 112°56′06′′ 43°33′14′′ 1,501.11 0.003 106 2 1,356.24 0.003 0.01 1.59 -0.24

03N 30E 30CBD1 

USGS 114

221 433318112555001 USGS-INL 112°55′53′′ 43°33′18′′ 1,500.69 0.003 106 4 1,356.34 0.003 0.01 1.70 -0.01

03N 32E 29DDC1 

USGS 2

222 433320112432301 USGS-INL 112°43′24′′ 43°33′19′′ 1,563.25 0.003 685 5 1,359.42 0.003 0.01 1.08 0.23

03N 30E 30CAD1 

USGS 115

223 433320112554101 USGS-INL 112°55′44′′ 43°33′19′′ 1,500.33 0.003 109 3 1,356.47 0.003 0.01 1.63 0.12

03N 29E 25CAD1 

USGS 38

224 433322112564301 USGS-INL 112°56′46′′ 43°33′22′′ 1,503.43 0.003 140 3 1,356.26 0.003 0.01 1.63 -0.02

03N 29E 25CAA1 

USGS 37

225 433326112564801 USGS-INL 112°56′51′′ 43°33′25′′ 1,503.28 0.003 343 2 1,356.27 0.003 0.01 1.24 -0.07

03N 29E 25BDD1 

USGS 36

226 433330112565201 USGS-INL 112°56′54′′ 43°33′29′′ 1,503.37 0.003 174 4 1,356.41 0.003 0.01 1.75 0.07

03N 30E 30ACC1 

USGS 116

227 433331112553201 USGS-INL 112°55′35′′ 43°33′31′′ 1,499.47 0.003 105 4 1,356.49 0.003 0.01 1.68 0.06

03N 30E 30BCC1 

USGS 111

228 433331112560501 USGS-INL 112°56′08′′ 43°33′30′′ 1,500.82 0.003 71 2 1,356.16 0.003 0.01 1.81 -0.29

03N 29E 26CAB1 

ICPP MON A-167

229 433331112580701 USGS-INL 112°58′09′′ 43°33′30′′ 1,508.84 0.003 40 2 1,356.56 0.003 0.01 0.47 0.13

03N 29E 25BDC1 

USGS 34

230 433334112565501 USGS-INL 112°56′57′′ 43°33′34′′ 1,503.28 0.003 166 2 1,356.40 0.003 0.01 1.59 0.07

03N 29E 25BDB1 

USGS 35

231 433339112565801 USGS-INL 112°57′01′′ 43°33′38′′ 1,503.41 0.003 127 2 1,356.24 0.003 0.01 1.67 -0.17

03N 29E 25BBD1 

USGS 39

232 433343112570001 USGS-INL 112°57′04′′ 43°33′42′′ 1,503.89 0.003 177 4 1,356.42 0.003 0.01 1.77 0.16
Table 5 61

Table 5. Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

03N 30E 30BAD1 

USGS 67

233 433344112554101 USGS-INL 112°55′43′′ 43°33′43′′ 1,498.65 0.003 79 2 1,356.41 0.003 0.01 1.84 -0.07

03N 29E 25ABD1 

USGS 57

234 433344112562601 USGS-INL 112°56′28′′ 43°33′43′′ 1,501.36 0.003 138 4 1,356.42 0.003 0.01 1.73 0.15

03N 30E 30BBB1 

USGS 51

235 433350112560601 USGS-INL 112°56′09′′ 43°33′49′′ 1,499.74 0.003 87 2 1,356.59 0.003 0.01 1.81 0.25

03N 29E 25AAA2 

USGS 123

236 433352112561401 USGS-INL 112°56′16′′ 43°33′51′′ 1,500.45 0.003 46 2 1,356.30 0.003 0.01 1.43 -0.17

03N 30E 30BBA2 

USGS 122

237 433353112555201 USGS-INL 112°55′54′′ 43°33′53′′ 1,498.79 0.003 44 2 1,356.46 0.003 0.01 1.46 -0.07

03N 30E 30BAB1 

USGS 59

238 433354112554701 USGS-INL 112°55′51′′ 43°33′53′′ 1,498.51 0.003 81 2 1,356.53 0.003 0.01 1.80 0.07

03N 29E 23DCD1 

USGS 84

239 433356112574201 USGS-INL 112°57′44′′ 43°33′56′′ 1,506.05 0.003 333 4 1,356.46 0.003 0.01 1.53 0.09

03N 30E 19DDC2 

USGS 82

240 433401112551001 USGS-INL 112°55′13′′ 43°34′00′′ 1,496.66 0.003 350 4 1,356.65 0.003 0.01 1.52 -0.22

03N 30E 19CCC1 

USGS 48

241 433401112560301 USGS-INL 112°56′05′′ 43°34′00′′ 1,499.72 0.003 101 2 1,356.52 0.003 0.01 1.95 -0.01

03N 29E 24DDC1 

USGS 45

242 433402112561801 USGS-INL 112°56′20′′ 43°34′02′′ 1,499.97 0.003 102 2 1,356.25 0.003 0.01 1.93 -0.22

03N 29E 24DDA2 

USGS 42

243 433404112561301 USGS-INL 112°56′14′′ 43°34′02′′ 1,499.72 0.003 110 2 1,356.48 0.003 0.01 1.84 0.05

03N 30E 19CCB1 

USGS 47

244 433407112560301 USGS-INL 112°56′06′′ 43°34′07′′ 1,499.35 0.003 114 2 1,356.58 0.003 0.01 1.77 0.05

03N 29E 24DDA3 

SGS 46

245 433407112561501 USGS-INL 112°56′17′′ 43°34′06′′ 1,499.59 0.003 304 2 1,356.56 0.003 0.01 1.36 0.09

03N 29E 24DDA1 

USGS 41

246 433409112561301 USGS-INL 112°56′14′′ 43°34′07′′ 1,499.54 0.003 117 2 1,356.52 0.003 0.01 1.85 -0.05

03N 29E 24DDB1 

USGS 44

247 433409112562101 USGS-INL 112°56′24′′ 43°34′08′′ 1,499.99 0.003 109 2 1,356.50 0.003 0.01 1.86 0.08

03N 29E 24DAD1 

USGS 40

248 433411112561101 USGS-INL 112°56′14′′ 43°34′11′′ 1,499.35 0.003 352 4 1,356.54 0.003 0.01 1.60 0.02

03N 30E 19CAC1 

USGS 52

249 433414112554201 USGS-INL 112°55′47′′ 43°34′14′′ 1,497.46 0.003 83 2 1,356.53 0.003 0.01 1.81 -0.07

03N 29E 24DAD2 

USGS 43

250 433415112561501 USGS-INL 112°56′17′′ 43°34′14′′ 1,499.30 0.003 117 2 1,356.50 0.003 0.01 1.89 -0.01

03N 29E 23CBA1 

MIDDLE 1823

251 433418112581701 USGS-INL 112°58′20′′ 43°34′18′′ 1,506.58 0.003 60 11 1,356.42 0.003 0.01 0.16 0.00
62 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 5.

Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, 

during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

03N 29E 19CBB1 

USGS 22

252 433422113031701 USGS-INL 113°03′24′′ 43°34′21′′ 1,539.78 0.003 593 4 1,351.45 0.003 0.01 1.27 -4.96

03N 29E 23ADC1 

USGS 76

253 433425112573201 USGS-INL 112°57′35′′ 43°34′24′′ 1,503.65 0.003 102 3 1,356.17 0.003 0.01 1.74 -0.70

03N 29E 23ABB1 

USGS 65

254 433447112574501 USGS-INL 112°57′50′′ 43°34′46′′ 1,502.13 0.003 228 4 1,357.49 0.003 0.01 1.46 1.18

03N 30E 16DDD1 

NPR TEST

255 433449112523101 USGS-INL 112°52′34′′ 43°34′49′′ 1,504.68 0.003 125 5 1,360.21 0.003 0.01 1.94 1.47

03N 30E 18CCC1 

USGS 121

256 433450112560301 USGS-INL 112°56′06′′ 43°34′49′′ 1,497.53 0.003 49 3 1,356.31 0.003 0.01 1.42 -1.51

03N 29E 14DDA2 

USGS 58

257 433500112572502 USGS-INL 112°57′28′′ 43°34′59′′ 1,500.11 0.003 415 4 1,356.67 0.003 0.01 1.45 -0.10

03N 32E 14CDD1 

USGS 100

258 433503112400701 USGS-INL 112°40′09′′ 43°35′02′′ 1,573.23 0.003 130 4 1,364.18 0.003 0.01 1.61 0.11

03N 29E 14CBD1 

USGS 79

259 433505112581901 USGS-INL 112°58′22′′ 43°35′05′′ 1,503.89 0.003 288 3 1,356.25 0.003 0.01 1.40 -0.45

03N 29E 14DBD1 

TRA DISP

260 433506112572301 USGS-INL 112°57′39′′ 43°35′05′′ 1,501.49 0.003 63 2 1,356.52 0.003 0.01 1.68 -0.21

03N 32E 13DCA1 

ARBOR TEST

261 433509112384801 USGS-INL 112°38′50′′ 43°35′08′′ 1,575.06 0.003 387 4 1,364.99 0.003 0.01 1.32 -0.24

03N 29E 14ADD1 

MTR TEST

262 433520112572601 USGS-INL 112°57′32′′ 43°35′19′′ 1,499.61 0.003 2,678 12 1,356.67 0.003 0.01 1.25 -0.55

03N 29E 14BCB1 

SITE 19

263 433522112582101 USGS-INL 112°58′24′′ 43°35′21′′ 1,502.50 0.003 87 2 1,356.67 0.003 0.01 1.73 0.13

03N 30E 12CDD1 

USGS 5

264 433543112493801 USGS-INL 112°49′40′′ 43°35′42′′ 1,506.03 0.003 441 4 1,360.29 0.003 0.01 4.44 -0.09

03N 32E 13BBD1 

SITE 16

265 433545112391501 USGS-INL 112°39′18′′ 43°35′44′′ 1,562.04 0.003 106 1 1,365.44 0.003 0.01 1.15 0.04

03N 32E 14AAC1 

ANL OBS A 001

266 433545112394101 USGS-INL 112°39′44′′ 43°35′44′′ 1,561.98 0.003 16 1 1,365.34 0.003 0.01 1.34 0.15

03N 32E 14AAC2 

ANL MW 13

267 433545112394102 USGS-INL 112°39′44′′ 43°35′45′′ 1,561.77 0.003 16 1 1,365.20 0.003 0.01 1.22 -0.14

03N 29E 12DDB1 

FIRE STA 2

268 433548112562301 USGS-INL 112°56′25′′ 43°35′47′′ 1,495.29 0.003 41 5 1,361.21 0.003 0.01 3.00 3.28

03N 29E 01DBB1 

USGS 98

269 433657112563601 USGS-INL 112°56′38′′ 43°36′56′′ 1,489.30 0.003 162 3 1,359.86 0.003 0.01 2.45 -1.18

03N 30E 06ACD1 

USGS 99

270 433705112552101 USGS-INL 112°55′24′′ 43°37′03′′ 1,485.91 0.003 157 4 1,361.49 0.003 0.01 2.31 0.42
Table 5 63

Table 5. Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

03N 29E 01ABC1 

WS INEL 1

271 433716112563601 USGS-INL 112°56′42′′ 43°37′14′′ 1,486.12 0.003 82 3 1,361.10 0.003 0.01 2.38 1.01

04N 35E 31DAA1 272 433759112225401 USGS-INL 112°22′56′′ 43°37′58′′ 1,561.68 1.524 17 1 1,373.38 0.003 1.53 1.44 -0.39

04N 30E 31ABD1 

USGS 97

273 433807112551501 USGS-INL 112°55′19′′ 43°38′06′′ 1,481.93 0.003 446 12 1,361.64 0.003 0.01 2.48 -0.06

04N 30E 26CCA1 

SITE 6

274 433826112510701 USGS-INL 112°51′09′′ 43°38′25′′ 1,475.13 0.003 53 2 1,361.98 0.003 0.01 2.26 0.53

04N 30E 30DAB1 

NRF 9

275 433840112550201 USGS-INL 112°55′03′′ 43°38′35′′ 1,480.40 0.003 53 2 1,361.65 0.003 0.01 2.07 -0.25

04N 30E 29CBB1 

NRF 10

276 433841112545201 USGS-INL 112°54′51′′ 43°38′37′′ 1,480.57 0.003 52 2 1,361.92 0.003 0.01 2.07 0.07

04N 30E 30ADC1 

NRF 8

277 433843112550901 USGS-INL 112°55′14′′ 43°38′41′′ 1,480.33 0.003 52 2 1,361.92 0.003 0.01 2.07 0.18

04N 30E 29BCD1 

NRF 11

278 433847112544201 USGS-INL 112°54′42′′ 43°38′43′′ 1,479.96 3.048 51 2 1,362.03 0.003 3.05 2.11 0.20

04N 30E 30ACA1 

USGS 102

279 433853112551601 USGS-INL 112°55′19′′ 43°38′50′′ 1,479.43 0.003 108 3 1,361.86 0.003 0.01 1.96 0.09

04N 30E 29BAC1 

NRF 12

280 433855112543201 USGS-INL 112°54′30′′ 43°38′53′′ 1,479.60 0.003 52 2 1,361.70 0.003 0.01 2.02 -0.17

04N 30E 19DDD1 

NRF 6

281 433910112550101 USGS-INL 112°55′04′′ 43°39′10′′ 1,478.21 0.003 74 3 1,361.36 0.003 0.01 1.92 -0.42

04N 30E 20CCA1 

NRF 7

282 433920112543601 USGS-INL 112°54′33′′ 43°39′23′′ 1,477.11 0.003 70 2 1,361.72 0.003 0.01 1.79 0.06

04N 30E 19DAD1 

NRF 13

283 433928112545401 USGS-INL 112°54′52′′ 43°39′32′′ 1,476.91 3.048 52 3 1,361.69 0.003 3.05 2.10 -0.11

04N 30E 22BDD1 

USGS 17

284 433937112515401 USGS-INL 112°51′57′′ 43°39′36′′ 1,474.30 0.003 601 6 1,362.18 0.003 0.01 1.71 -0.58

04N 35E 20CAA1 285 433945112221701 USGS-INL 112°22′19′′ 43°39′45′′ 1,534.25 1.524 22 3 1,376.35 0.003 1.53 1.72 3.6904N 29E 14CAA1 

SITE 17

286 434027112575701 USGS-INL 112°57′59′′ 43°40′26′′ 1,488.64 0.003 173 12 1,363.27 0.003 0.01 2.50 0.35

04N 31E 16ADC1 

USGS 6

287 434031112453701 USGS-INL 112°45′39′′ 43°40′30′′ 1,494.15 0.003 472 4 1,364.95 0.003 0.01 1.24 -3.24

04N 29E 09DCD1 

USGS 23

288 434055112595901 USGS-INL 113°00′03′′ 43°40′54′′ 1,489.77 0.003 556 5 1,363.48 0.003 0.01 1.97 -3.30

04N 30E 07ADB1 

USGS 12

289 434126112550701 USGS-INL 112°55′10′′ 43°41′25′′ 1,469.90 0.003 2,207 12 1,365.26 0.003 0.01 2.10 1.44

04N 30E 06ABA1 

USGS 15

290 434234112551701 USGS-INL 112°55′20′′ 43°42′34′′ 1,467.77 0.003 220 4 1,365.88 0.003 0.01 2.47 -2.95
64 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 5.

Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, 

during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

05N 32E 36ADD1 

USGS 21

291 434307112382601 USGS-INL 112°38′28′′ 43°43′07′′ 1,475.92 0.003 3,549 63 1,371.37 0.003 0.01 1.58 -2.53

05N 31E 28CCC1 

SITE 14

292 434334112463101 USGS-INL 112°46′34′′ 43°43′34′′ 1,462.13 0.003 1,097 4 1,375.41 0.003 0.01 1.51 4.23

05N 34E 29DAA1 

USGS 29

293 434407112285101 USGS-INL 112°28′53′′ 43°44′06′′ 1,487.75 0.003 144 2 1,376.08 0.003 0.01 1.70 3.77

05N 29E 23CDD1 

USGS 19

294 434426112575701 USGS-INL 112°57′59′′ 43°44′26′′ 1,464.13 0.003 2,653 11 1,378.15 0.003 0.01 1.32 7.58

05N 33E 23DDA1 

USGS 32

295 434444112322101 USGS-INL 112°32′24′′ 43°44′43′′ 1,467.79 0.003 122 2 1,375.63 0.003 0.01 1.79 0.79

05N 31E 14BCC1 

USGS 18

296 434540112440901 USGS-INL 112°44′12′′ 43°45′40′′ 1,465.39 0.003 528 5 1,378.29 0.003 0.01 1.77 -1.40

05N 30E 15ADC1 

DH 2A

297 434547112512801 USGS-INL 112°51′30′′ 43°45′47′′ 1,462.46 0.003 123 4 1,374.44 0.003 0.01 3.63 0.10

05N 33E 17ADD1 

USGS 28

298 434600112360101 USGS-INL 112°36′03′′ 43°45′59′′ 1,455.42 0.003 145 2 1,380.68 0.003 0.01 1.59 2.58

05N 33E 13BDC1 

USGS 30C

299 434601112315401 USGS-INL 112°31′57′′ 43°46′00′′ 1,462.26 0.003 336 4 1,376.51 0.003 0.01 1.72 -0.68

05N 30E 11CDD1 

DH 1B

300 434611112504301 USGS-INL 112°50′46′′ 43°46′10′′ 1,461.70 0.003 235 5 1,374.80 0.003 0.01 3.32 -1.43

05N 33E 10CDC1 

USGS 31

301 434625112342101 USGS-INL 112°34′23′′ 43°46′25′′ 1,459.79 0.003 130 2 1,378.86 0.003 0.01 1.35 -0.98

05N 34E 09BDA1 

USGS 4

302 434657112282201 USGS-INL 112°28′24′′ 43°46′55′′ 1,461.31 0.003 1,920 4 1,377.98 0.003 0.01 1.00 -5.52

06N 32E 36ADD1 

2ND OWSLEY

303 434819112380501 USGS-INL 112°38′09′′ 43°48′19′′ 1,459.48 0.003 352 2 1,386.32 0.003 0.01 0.97 -0.12

06N 33E 26DDB1 

USGS 27

304 434851112321801 USGS-INL 112°32′21′′ 43°48′50′′ 1,459.21 0.003 1,210 12 1,386.60 0.003 0.01 1.44 -9.79

06N 32E 26CDB1 

ANP 9

305 434856112400001 USGS-INL 112°40′03′′ 43°48′55′′ 1,459.88 0.003 461 4 1,387.33 0.003 0.01 1.53 -0.06

06N 32E 26CAB1 

ANP 10

306 434909112400401 USGS-INL 112°40′06′′ 43°49′08′′ 1,459.85 0.003 53 2 1,387.88 0.003 0.01 1.89 -0.23

06N 31E 27BDD1 

USGS 7

307 434915112443901 USGS-INL 112°44′42′′ 43°49′14′′ 1,460.82 0.003 851 4 1,390.86 0.003 0.01 1.23 1.98

06N 31E 21DCC1 

PSTF TEST

308 434941112454201 USGS-INL 112°45′44′′ 43°49′40′′ 1,459.94 0.003 91 3 1,390.18 0.003 0.01 2.41 0.33

06N 32E 22CCB2 

GIN 3

309 434945112413101 USGS-INL 112°41′33′′ 43°49′44′′ 1,459.90 0.003 24 1 1,391.00 0.003 0.01 2.52 0.21
Table 5 65

Table 5. Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

06N 31E 24DDA1 

GIN 1

310 434947112414301 USGS-INL 112°41′43′′ 43°49′47′′ 1,460.05 0.003 22 1 1,390.82 0.003 0.01 2.39 -0.19

06N 32E 22CCB1 

GIN 2

311 434949112413401 USGS-INL 112°41′36′′ 43°49′48′′ 1,459.90 0.003 41 1 1,391.02 0.003 0.01 2.39 -0.04

06N 32E 22CCB3 

GIN 4

312 434949112413601 USGS-INL 112°41′37′′ 43°49′48′′ 1,459.93 0.003 22 1 1,391.05 0.003 0.01 2.39 0.05

06N 32E 22CBC1 

GIN 5

313 434953112413301 USGS-INL 112°41′34′′ 43°49′53′′ 1,460.02 0.003 23 1 1,391.08 0.003 0.01 2.37 0.03

06N 32E 22BBD6 

TAN 23A

314 435020112412704 USGS-INL 112°41′29′′ 43°50′19′′ 1,460.19 0.003 21 5 1,391.31 0.003 0.01 1.32 -0.05

06N 32E 22BBD1 

TAN 15

315 435021112412701 USGS-INL 112°41′29′′ 43°50′20′′ 1,460.10 0.003 22 5 1,391.37 0.003 0.01 1.58 0.07

06N 31E 13CDD2 

TAN 17

316 435034112421601 USGS-INL 112°42′18′′ 43°50′33′′ 1,460.90 0.003 68 6 1,391.13 0.003 0.01 1.52 -0.31

06N 31E 13CDD1 

TAN 8

317 435034112421701 USGS-INL 112°42′19′′ 43°50′33′′ 1,461.52 0.003 68 6 1,391.44 0.003 0.01 1.44 0.32

06N 31E 16DCA1 

NO NAME 1

318 435038112453401 USGS-INL 112°45′35′′ 43°50′38′′ 1,459.31 0.003 84 4 1,390.88 0.003 0.01 2.81 0.24

06N 31E 13CCA3 

TAN 14

319 435039112423701 USGS-INL 112°42′39′′ 43°50′38′′ 1,458.27 0.030 23 5 1,390.94 0.003 0.03 1.55 -0.08

06N 31E 13CCA2 

TAN 13A

320 435040112423801 USGS-INL 112°42′40′′ 43°50′39′′ 1,458.15 0.030 21 5 1,390.99 0.003 0.03 1.45 0.02

06N 31E 13DBB4 

TAN 18

321 435051112421401 USGS-INL 112°42′17′′ 43°50′50′′ 1,464.99 0.003 22 5 1,391.46 0.003 0.01 1.30 -0.01

06N 31E 13DBB5 

TAN 19

322 435051112421501 USGS-INL 112°42′17′′ 43°50′50′′ 1,465.12 0.003 21 5 1,391.52 0.003 0.01 1.31 0.07

06N 31E 13DBB1 

USGS 24

323 435053112420801 USGS-INL 112°42′15′′ 43°50′50′′ 1,462.60 0.003 1,748 15 1,391.34 0.003 0.01 1.56 -0.10

06N 31E 11CDC1 

FET DISP 3

324 435124112433701 USGS-INL 112°43′40′′ 43°51′23′′ 1,458.73 0.003 39 1 1,391.75 0.003 0.01 2.24 0.56

06N 31E 10ACC1 

ANP 6

325 435152112443101 USGS-INL 112°44′34′′ 43°51′51′′ 1,462.40 0.003 88 2 1,391.16 0.003 0.01 2.44 -0.37

06N 31E 12ACD1 

IET 1 DISP

326 435153112420501 USGS-INL 112°42′08′′ 43°51′53′′ 1,461.05 0.003 60 2 1,391.28 0.003 0.01 2.60 -0.21

06N 32E 11ABA1 

USGS 26

327 435212112394001 USGS-INL 112°39′43′′ 43°52′10′′ 1,460.66 0.003 559 5 1,391.25 0.003 0.01 1.47 -10.83

07N 31E 33DCD1 

ANP 5

328 435308112454101 USGS-INL 112°45′44′′ 43°53′07′′ 1,486.11 0.003 67 1 1,391.80 0.003 0.01 1.92 0.78
66 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 5.

Wells in the Federal-State Cooperative and U.S. Geological Survey-Idaho National Laboratory water-level monitoring networks, eastern Snake River Plain, Idaho, 

during 2008.—Continued

Local name Map No. Site No. Network name Longitude Latitude

Reference 

point

elevation

(m)

Reference 

point

location

error

(m)

Sample size 2008

water-level

elevation

(m)

2008

measurement

method error

(m)

2008

measurement

error

(m)

Standard

deviation

(m)

Estimation

error

(m) POR 2008

07N 31E 34BDD1 

USGS 25

329 435339112444601 USGS-INL 112°44′48′′ 43°53′38′′ 1,478.88 0.003 3,584 56 1,391.61 0.003 0.01 1.69 0.18

07N 31E 28CAC1 

P and W 1

330 435416112460401 USGS-INL 112°46′06′′ 43°54′15′′ 1,493.26 0.003 69 1 1,391.85 0.003 0.01 1.88 0.92

07N 31E 28DAB1 

P and W 2

331 435419112453101 USGS-INL 112°45′33′′ 43°54′18′′ 1,491.80 0.003 98 2 1,391.10 0.003 0.01 2.42 -0.44

07N 31E 26BBC1 

P and W 3

332 435443112435801 USGS-INL 112°44′01′′ 43°54′42′′ 1,490.16 0.003 72 1 1,391.94 0.003 0.01 1.78 -1.32

07N 31E 22BDD1 

ANP 7

333 435522112444201 USGS-INL 112°44′46′′ 43°55′19′′ 1,505.15 0.003 60 2 1,391.81 0.003 0.01 1.91 -1.88

07N 31E 20BDB1 

USGS 126A

334 435529112471301 USGS-INL 112°47′15′′ 43°55′28′′ 1,521.64 0.003 93 4 1,391.54 0.003 0.01 2.04 -0.00

07N 31E 20BDB2 

USGS 126B

335 435529112471401 USGS-INL 112°47′16′′ 43°55′28′′ 1,521.81 0.003 93 4 1,391.52 0.003 0.01 2.03 -0.02
Table 6 67

Table 6. Wells identified for removal based on genetic algorithm searches, eastern Snake River Plain, Idaho.[Number of sites removed: well sites removed from an existing monitoring network (nr). Local name: local well identifier used in this study. Map No.: identifier used to locate wells on map figures and as a 

cross reference with data in other tables. Site No.: unique numerical identifiers used to access well data (http://waterdata.usgs.gov/nwis). Times identified: number of times the observation well was identified 

for removal in each of the networks five genetic algorithm searches. Control parameter values: a kriging grid resolution of 2.5 kilometers for the Co-op network and 1.5 kilometers for the USGS-INL network, population size of 2,000, elitism rate of 0.05, crossover probability of 0.80, mutation probability of 0.30, and terminates after 50 consecutive iterations without any improvement in the best fitness value. Abbreviations: Co-op network, Federal-State Cooperative water-level monitoring network; USGS-INL network, U.S. Geological Survey-Idaho National Laboratory water-level monitoring network]

Number

of sites

removed

Optimized Co-op network Optimized USGS-INL network

Local name Map No. Site No. Times identified Local name Map No. Site No. Times identified

10 11S 18E 25DDC1 8 422555114172101 5 02N 29E 18CCD2 RWMC M4D 184 432939113030101 5

09S 29E 04BCA1 34 424013113043801 4 02N 29E 18DCB1 USGS 119 187 432945113023401 5

04S 31E 20BBB1 80 430402112520301 5 02N 29E 18DCA1 USGS 118 188 432947113023001 5

04S 33E 03CBB2 84 430610112353301 4 02N 29E 18CBD1 USGS 117 189 432955113025901 5

03S 33E 14BBA1 91 431006112340901 4 03N 29E 36DDC1 ICPP 1798 206 433216112562601 4

06N 39E 30ADC1 120 434915111540501 5 03N 29E 36BDB3 USGS 128 213 433250112565601 5

06N 39E 28BBB1 124 434932111523701 5 03N 29E 24DDA3 USGS 46 245 433407112561501 5

07N 36E 22ABD4 140 435528112121201 5 03N 29E 23CBA1 MIDDLE 1823 251 433418112581701 1

07N 36E 09BBB1 146 435728112141301 4 06N 32E 22BBD6 TAN 23A 314 435020112412704 5

09N 36E 04BAA1 164 440841112133001 5 06N 31E 13DBB4 TAN 18 321 435051112421401 5

20 11S 18E 25DDC1 8 422555114172101 5 02N 29E 18CCD2 RWMC M4D 184 432939113030101 5

09S 26E 07AAB1 32 423943113272001 4 02N 29E 18DCB1 USGS 119 187 432945113023401 5

09S 25E 03CAC1 33 424003113313101 4 02N 29E 18DCA1 USGS 118 188 432947113023001 5

09S 29E 04BCA1 34 424013113043801 4 02N 29E 18CBD1 USGS 117 189 432955113025901 5

05S 31E 19DDC2 72 425754112521601 4 02N 29E 18CBA1 RWMC M1SA 190 432956113030901 4

04S 31E 20BBB1 80 430402112520301 5 02N 29E 18BDA1 USGS 87 194 433013113024201 4

04S 33E 03CBB2 84 430610112353301 4 03N 29E 36DDC1 ICPP 1798 206 433216112562601 4

03S 33E 14BBA1 91 431006112340901 4 03N 29E 36BDB3 USGS 128 213 433250112565601 5

03S 34E 02BCC3 92 431126112271503 4 03N 29E 25CAD1 USGS 38 224 433322112564301 4

06N 39E 30ADC1 120 434915111540501 5 03N 29E 25BDC1 USGS 34 230 433334112565501 4

06N 39E 28BBB1 124 434932111523701 5 03N 29E 25AAA2 USGS 123 236 433352112561401 4

06N 39E 10BBB1 131 435209111512101 2 03N 30E 30BBA2 USGS 122 237 433353112555201 4

06N 38E 02DBD1 132 435228111563401 3 03N 29E 24DDA3 USGS 46 245 433407112561501 5

07N 35E 20CBD1 137 435504112222301 4 03N 29E 24DAD1 USGS 40 248 433411112561101 4

07N 36E 22ABD4 140 435528112121201 5 03N 29E 14DDA2 USGS 58 257 433500112572502 4

07N 36E 09BBB1 146 435728112141301 4 03N 32E 14AAC1 ANL OBS A 001 266 433545112394101 4

07N 34E 04CDC1 147 435728112281101 4 06N 32E 22BBD6 TAN 23A 314 435020112412704 5

08N 36E 21DCD1 152 440002112131801 3 06N 31E 13CCA2 TAN 13A 320 435040112423801 4

09N 36E 33CBB1 158 440353112135701 4 06N 31E 13DBB4 TAN 18 321 435051112421401 5

09N 36E 04BAA1 164 440841112133001 5 06N 31E 13DBB5 TAN 19 322 435051112421501 4
68 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 6.

Wells identified for removal based on genetic algorithm searches, eastern Snake River Plain, Idaho.—Continued

Number

of sites

removed

Optimized Co-op network Optimized USGS-INL network

Local name Map No. Site No. Times identified Local name Map No. Site No. Times identified

40 12S 20E 04DBC1 3 422424114070001 3 02N 29E 18CCD2 RWMC M4D 184 432939113030101 5

11S 18E 25DDC1 8 422555114172101 5 02N 29E 18DCB1 USGS 119 187 432945113023401 5

10S 21E 26AAA2 16 423159113570302 3 02N 29E 18DCA1 USGS 118 188 432947113023001 5

09S 26E 10DDD1 31 423855113233901 2 02N 29E 18CBD1 USGS 117 189 432955113025901 5

09S 26E 07AAB1 32 423943113272001 4 02N 29E 18CBA1 RWMC M1SA 190 432956113030901 4

09S 25E 03CAC1 33 424003113313101 4 02N 29E 18BDA1 USGS 87 194 433013113024201 4

09S 29E 04BCA1 34 424013113043801 4 03N 29E 36DDC1 ICPP 1798 206 433216112562601 4

08S 27E 31DDA1 35 424042113201101 3 03N 29E 36BDB3 USGS 128 213 433250112565601 5

08S 14E 16CBB1 47 424353114494701 3 03N 29E 25DDB1 USGS 113 218 433314112561801 3

08S 27E 07DBC1 48 424419113201801 3 03N 29E 25DCA1 USGS 112 219 433314112563001 3

05S 31E 19DDC2 72 425754112521601 4 03N 30E 30CCB1 USGS 77 220 433315112560301 3

04S 31E 20BBB1 80 430402112520301 5 03N 30E 30CBD1 USGS 114 221 433318112555001 3

04S 31E 11ABA1 81 430547112473701 3 03N 29E 25CAD1 USGS 38 224 433322112564301 4

04S 33E 03CBB2 84 430610112353301 4 03N 29E 25BDD1 USGS 36 226 433330112565201 3

03S 33E 14BBA1 91 431006112340901 4 03N 30E 30BCC1 USGS 111 228 433331112560501 3

03S 34E 02BCC3 92 431126112271503 4 03N 29E 25BDC1 USGS 34 230 433334112565501 4

02S 35E 11DDD1 96 431517112190101 3 03N 29E 25BDB1 USGS 35 231 433339112565801 3

03N 37E 12BDB1 109 433625112031801 3 03N 29E 25BBD1 USGS 39 232 433343112570001 3

06N 39E 35CBB2 118 434816111501302 3 03N 30E 30BAD1 USGS 67 233 433344112554101 3

06N 39E 30ADC1 120 434915111540501 5 03N 29E 25ABD1 USGS 57 234 433344112562601 3

06N 38E 25ACB4 121 434917111553102 2 03N 30E 30BBB1 USGS 51 235 433350112560601 3

06N 37E 29ACA2 122 434922112072202 3 03N 29E 25AAA2 USGS 123 236 433352112561401 4

06N 39E 28BBB1 124 434932111523701 5 03N 30E 30BBA2 USGS 122 237 433353112555201 4

06N 39E 23AAC2 125 435015111495302 3 03N 30E 30BAB1 USGS 59 238 433354112554701 3

06N 39E 16DAA1 127 435048111512701 3 03N 30E 19CCC1 USGS 48 241 433401112560301 3

06N 39E 13ABA1 129 435118111481601 3 03N 29E 24DDC1 USGS 45 242 433402112561801 3

07N 39E 34CCB1 133 435314111511902 3 03N 29E 24DDA2 USGS 42 243 433404112561301 3

07N 35E 20CBD1 137 435504112222301 4 03N 30E 19CCB1 USGS 47 244 433407112560301 3

07N 38E 23DBA2 138 435506111563102 3 03N 29E 24DDA3 USGS 46 245 433407112561501 5

07N 40E 19ADD4 139 435516111464002 3 03N 29E 24DDA1 USGS 41 246 433409112561301 3

07N 36E 22ABD4 140 435528112121201 5 03N 29E 24DAD1 USGS 40 248 433411112561101 4

07N 39E 16DBB3 142 435605111515801 3 03N 29E 24DAD2 USGS 43 250 433415112561501 3

07N 35E 13AAD1 143 435626112164301 3 03N 29E 14DDA2 USGS 58 257 433500112572502 4

07N 36E 11ABB1 145 435723112111101 3 03N 32E 14AAC1 ANL OBS A 001 266 433545112394101 4

07N 34E 04CDC1 147 435728112281101 4 04N 30E 29BCD1 NRF 11 278 433847112544201 3

08N 41E 33ABB1 149 435904111373101 3 06N 32E 22BBD6 TAN 23A 314 435020112412704 5

08N 34E 27CDD1 150 435912112264801 3 06N 31E 13CDD1 TAN 8 317 435034112421701 3

08N 36E 21DCD1 152 440002112131801 3 06N 31E 13CCA2 TAN 13A 320 435040112423801 4

09N 36E 33CBB1 158 440353112135701 4 06N 31E 13DBB4 TAN 18 321 435051112421401 5

09N 36E 04BAA1 164 440841112133001 5 06N 31E 13DBB5 TAN 19 322 435051112421501 4
Table 6 69

Table 6. Wells identified for removal based on genetic algorithm searches, eastern Snake River Plain, Idaho.—Continued

Number

of sites

removed

Optimized Co-op network Optimized USGS-INL network

Local name Map No. Site No. Times identified Local name Map No. Site No. Times identified

60 12S 20E 04DBC1 3 422424114070001 3 02N 29E 18CCD2 RWMC M4D 184 432939113030101 5

11S 22E 32CCC1 6 422501113543901 2 02N 29E 18DCB1 USGS 119 187 432945113023401 5

11S 18E 25DDC1 8 422555114172101 5 02N 29E 18DCA1 USGS 118 188 432947113023001 5

10S 21E 26AAA2 16 423159113570302 3 02N 29E 18CBD1 USGS 117 189 432955113025901 5

09S 22E 33ADA1 21 423604113522401 2 02N 29E 18CBA1 RWMC M1SA 190 432956113030901 4

09S 16E 21DCD1 22 423722114345101 2 02N 29E 18BDA1 USGS 87 194 433013113024201 4

09S 25E 23DBA1 23 423732113295801 2 03N 29E 36DDC1 ICPP 1798 206 433216112562601 4

09S 26E 13CCC2 25 423802113222701 1 03N 29E 36DCC2 CFA LF 2-10 207 433216112563301 2

09S 29E 18CDA1 26 423808113063601 2 03N 29E 36BCB1 USGS 85 212 433246112571201 2

09S 26E 07AAB1 32 423943113272001 4 03N 29E 36BDB3 USGS 128 213 433250112565601 5

09S 25E 03CAC1 33 424003113313101 4 03N 29E 25DDB1 USGS 113 218 433314112561801 3

09S 29E 04BCA1 34 424013113043801 4 03N 29E 25DCA1 USGS 112 219 433314112563001 3

08S 27E 31DDA1 35 424042113201101 3 03N 30E 30CCB1 USGS 77 220 433315112560301 3

08S 25E 36DAA1 39 424102113282101 2 03N 30E 30CBD1 USGS 114 221 433318112555001 3

08S 26E 33BCB2 41 424112113255402 2 03N 29E 25CAD1 USGS 38 224 433322112564301 4

08S 14E 16CBB1 47 424353114494701 3 03N 29E 25BDD1 USGS 36 226 433330112565201 3

08S 27E 07DBC1 48 424419113201801 3 03N 30E 30ACC1 USGS 116 227 433331112553201 2

08S 14E 12CBC1 49 424439114461201 2 03N 30E 30BCC1 USGS 111 228 433331112560501 3

08S 26E 03DCC1 50 424454113240101 2 03N 29E 25BDC1 USGS 34 230 433334112565501 4

07S 25E 19BAA1 56 424828113345201 2 03N 29E 25BDB1 USGS 35 231 433339112565801 3

05S 33E 35CDC1 68 425608112340901 2 03N 29E 25BBD1 USGS 39 232 433343112570001 3

05S 31E 19DDC2 72 425754112521601 4 03N 30E 30BAD1 USGS 67 233 433344112554101 3

05S 31E 27ABA1 73 425757112485201 2 03N 29E 25ABD1 USGS 57 234 433344112562601 3

04S 31E 20BBB1 80 430402112520301 5 03N 30E 30BBB1 USGS 51 235 433350112560601 3

04S 31E 11ABA1 81 430547112473701 3 03N 29E 25AAA2 USGS 123 236 433352112561401 4

04S 32E 01CBA1 82 430607112400501 2 03N 30E 30BBA2 USGS 122 237 433353112555201 4

04S 33E 03CBB2 84 430610112353301 4 03N 30E 30BAB1 USGS 59 238 433354112554701 3

03S 33E 25CCC1 86 430729112331201 2 03N 29E 23DCD1 USGS 84 239 433356112574201 2

03S 33E 17AAD1 90 430955112365001 2 03N 30E 19CCC1 USGS 48 241 433401112560301 3

03S 34E 02BCC3 92 431126112271503 4 03N 29E 24DDC1 USGS 45 242 433402112561801 3

02S 34E 33BBA1 94 431242112292801 2 03N 29E 24DDA2 USGS 42 243 433404112561301 3

02S 35E 11DDD1 96 431517112190101 3 03N 30E 19CCB1 USGS 47 244 433407112560301 3

03N 37E 12BDB1 109 433625112031801 3 03N 29E 24DDA3 USGS 46 245 433407112561501 5

06N 39E 35CBB2 118 434816111501302 3 03N 29E 24DDA1 USGS 41 246 433409112561301 3

06N 35E 27DDA1 119 434857112185801 2 03N 29E 24DAD1 USGS 40 248 433411112561101 4

06N 39E 30ADC1 120 434915111540501 5 03N 29E 24DAD2 USGS 43 250 433415112561501 3

06N 38E 25ACB4 121 434917111553102 2 03N 29E 23ADC1 USGS 76 253 433425112573201 2

06N 37E 29ACA2 122 434922112072202 3 03N 29E 23ABB1 USGS 65 254 433447112574501 2

06N 39E 28BBB1 124 434932111523701 5 03N 29E 14DDA2 USGS 58 257 433500112572502 4

06N 39E 23AAC2 125 435015111495302 3 03N 29E 14CBD1 USGS 79 259 433505112581901 2
70 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 6.

Wells identified for removal based on genetic algorithm searches, eastern Snake River Plain, Idaho.—Continued

Number

of sites

removed

Optimized Co-op network Optimized USGS-INL network

Local name Map No. Site No. Times identified Local name Map No. Site No. Times identified

60—Continued 06N 35E 21AAB1 126 435028112202601 2 03N 29E 14ADD1 MTR TEST 262 433520112572601 2

06N 39E 16DAA1 127 435048111512701 3 03N 32E 14AAC1 ANL OBS A 001 266 433545112394101 4

06N 39E 13ABA1 129 435118111481601 3 04N 30E 30DAB1 NRF 9 275 433840112550201 2

06N 36E 11ABA3 130 435208112105103 2 04N 30E 29CBB1 NRF 10 276 433841112545201 2

06N 38E 02DBD1 132 435228111563401 3 04N 30E 30ADC1 NRF 8 277 433843112550901 2

07N 39E 34CCB1 133 435314111511902 3 04N 30E 29BCD1 NRF 11 278 433847112544201 3

07N 35E 20CBD1 137 435504112222301 4 04N 30E 19DDD1 NRF 6 281 433910112550101 2

07N 38E 23DBA2 138 435506111563102 3 04N 30E 20CCA1 NRF 7 282 433920112543601 1

07N 40E 19ADD4 139 435516111464002 3 06N 31E 24DDA1 GIN 1 310 434947112414301 2

07N 36E 22ABD4 140 435528112121201 5 06N 32E 22CCB1 GIN 2 311 434949112413401 2

07N 39E 16DBB3 142 435605111515801 3 06N 32E 22CCB3 GIN 4 312 434949112413601 2

07N 35E 13AAD1 143 435626112164301 3 06N 32E 22CBC1 GIN 5 313 434953112413301 2

07N 36E 11ABB1 145 435723112111101 3 06N 32E 22BBD6 TAN 23A 314 435020112412704 5

07N 36E 09BBB1 146 435728112141301 4 06N 31E 13CDD1 TAN 8 317 435034112421701 3

07N 34E 04CDC1 147 435728112281101 4 06N 31E 13CCA3 TAN 14 319 435039112423701 2

08N 41E 33ABB1 149 435904111373101 3 06N 31E 13CCA2 TAN 13A 320 435040112423801 4

08N 34E 27CDD1 150 435912112264801 3 06N 31E 13DBB4 TAN 18 321 435051112421401 5

08N 36E 03DCD1 156 440239112121101 1 06N 31E 13DBB5 TAN 19 322 435051112421501 4

09N 36E 33CBB1 158 440353112135701 4 06N 31E 13DBB1 USGS 24 323 435053112420801 2

09N 36E 04BAA1 164 440841112133001 5 07N 31E 20BDB1 USGS 126A 334 435529112471301 2

80 12S 20E 04DBC1 3 422424114070001 3 02N 29E 19BCB1 USGS 120 180 432919113031501 1

11S 22E 32CCC1 6 422501113543901 2 02N 29E 20BBA1 RWMC M6S 182 432931113015001 1

11S 18E 25DDC1 8 422555114172101 5 02N 29E 18CCD2 RWMC M4D 184 432939113030101 5

10S 21E 28BCB1 15 423145114003001 1 02N 29E 18CCD1 USGS 88 185 432940113030201 1

10S 21E 26AAA2 16 423159113570302 3 02N 29E 18DCB1 USGS 119 187 432945113023401 5

10S 17E 14CCD1 19 423255114260601 1 02N 29E 18DCA1 USGS 118 188 432947113023001 5

09S 22E 33ADA1 21 423604113522401 2 02N 29E 18CBD1 USGS 117 189 432955113025901 5

09S 16E 21DCD1 22 423722114345101 2 02N 29E 18CBA1 RWMC M1SA 190 432956113030901 4

09S 25E 23DBA1 23 423732113295801 2 02N 29E 18BDA1 USGS 87 194 433013113024201 4

09S 29E 18CDA1 26 423808113063601 2 02N 29E 17BBA1 RWMC M7S 196 433023113014801 1

09S 28E 18BAD1 30 423837113134301 1 02N 29E 11CCA1 USGS 131 197 433036112581601 1

09S 26E 10DDD1 31 423855113233901 2 02N 29E 09CDA2 USGS 129 198 433036113002701 1

09S 26E 07AAB1 32 423943113272001 4 02N 29E 01DCA1 USGS 130 205 433130112562801 1

09S 25E 03CAC1 33 424003113313101 4 03N 29E 36DCC2 CFA LF 2-10 207 433216112563301 2

08S 27E 31DDA1 35 424042113201101 3 03N 29E 36CCC1 CFA LF 3-9 208 433216112571001 1

08S 29E 34CBC1 36 424052113033901 1 03N 29E 36DAC1 CFA LF 2-11 211 433230112561701 1

08S 24E 31DAC1 37 424053113412801 1 03N 29E 36BCB1 USGS 85 212 433246112571201 2

08S 25E 36DAA1 39 424102113282101 2 03N 29E 36BDB3 USGS 128 213 433250112565601 5

08S 26E 33BCB2 41 424112113255402 2 03N 29E 25DDB1 USGS 113 218 433314112561801 3
Table 6 71

Table 6. Wells identified for removal based on genetic algorithm searches, eastern Snake River Plain, Idaho.—Continued

Number

of sites

removed

Optimized Co-op network Optimized USGS-INL network

Local name Map No. Site No. Times identified Local name Map No. Site No. Times identified

80—Continued 08S 15E 32BBA1 42 424144114434101 1 03N 29E 25DCA1 USGS 112 219 433314112563001 3

08S 14E 16CBB1 47 424353114494701 3 03N 30E 30CCB1 USGS 77 220 433315112560301 3

08S 27E 07DBC1 48 424419113201801 3 03N 30E 30CBD1 USGS 114 221 433318112555001 3

08S 14E 12CBC1 49 424439114461201 2 03N 30E 30CAD1 USGS 115 223 433320112554101 1

08S 26E 03DCC1 50 424454113240101 2 03N 29E 25CAD1 USGS 38 224 433322112564301 4

07S 25E 19BAA1 56 424828113345201 2 03N 29E 25BDD1 USGS 36 226 433330112565201 3

07S 29E 12CCC2 57 424916113012001 1 03N 30E 30ACC1 USGS 116 227 433331112553201 2

06S 34E 09BCB1 66 425456112294001 1 03N 30E 30BCC1 USGS 111 228 433331112560501 3

05S 33E 35CDC1 68 425608112340901 2 03N 29E 26CAB1 ICPP MON A-167 229 433331112580701 1

05S 31E 19DDC2 72 425754112521601 4 03N 29E 25BDC1 USGS 34 230 433334112565501 4

05S 31E 27ABA1 73 425757112485201 2 03N 29E 25BDB1 USGS 35 231 433339112565801 3

04S 33E 20CBB1 79 430333112375801 1 03N 29E 25BBD1 USGS 39 232 433343112570001 3

04S 31E 20BBB1 80 430402112520301 5 03N 30E 30BAD1 USGS 67 233 433344112554101 3

04S 31E 11ABA1 81 430547112473701 3 03N 29E 25ABD1 USGS 57 234 433344112562601 3

04S 32E 01CBA1 82 430607112400501 2 03N 30E 30BBB1 USGS 51 235 433350112560601 3

04S 31E 05CBC1 83 430607112515601 1 03N 29E 25AAA2 USGS 123 236 433352112561401 4

03S 33E 25CCC1 86 430729112331201 2 03N 30E 30BBA2 USGS 122 237 433353112555201 4

03S 33E 17AAD1 90 430955112365001 2 03N 30E 30BAB1 USGS 59 238 433354112554701 3

03S 33E 14BBA1 91 431006112340901 4 03N 29E 23DCD1 USGS 84 239 433356112574201 2

03S 34E 02BCC3 92 431126112271503 4 03N 30E 19DDC2 USGS 82 240 433401112551001 1

02S 34E 33BBA1 94 431242112292801 2 03N 30E 19CCC1 USGS 48 241 433401112560301 3

02S 35E 22DAC1 95 431349112202001 1 03N 29E 24DDC1 USGS 45 242 433402112561801 3

02S 35E 11DDD1 96 431517112190101 3 03N 29E 24DDA2 USGS 42 243 433404112561301 3

02S 33E 16ABB1 97 431520112360901 1 03N 30E 19CCB1 USGS 47 244 433407112560301 3

03N 38E 22BAB1 108 433457111583701 1 03N 29E 24DDA3 USGS 46 245 433407112561501 5

03N 37E 12BDB1 109 433625112031801 3 03N 29E 24DDA1 USGS 41 246 433409112561301 3

05N 39E 08DAD1 115 434638111530401 1 03N 29E 24DDB1 USGS 44 247 433409112562101 1

05N 36E 02BDA2 116 434748112113602 1 03N 29E 24DAD1 USGS 40 248 433411112561101 4

06N 39E 35CBB2 118 434816111501302 3 03N 29E 24DAD2 USGS 43 250 433415112561501 3

06N 35E 27DDA1 119 434857112185801 2 03N 29E 23ADC1 USGS 76 253 433425112573201 2

06N 39E 30ADC1 120 434915111540501 5 03N 29E 23ABB1 USGS 65 254 433447112574501 2

06N 37E 29ACA2 122 434922112072202 3 03N 29E 14DDA2 USGS 58 257 433500112572502 4

06N 38E 30BAD2 USBR SITE 1 123 434924112013801 1 03N 29E 14CBD1 USGS 79 259 433505112581901 2

06N 39E 28BBB1 124 434932111523701 5 03N 29E 14DBD1 TRA DISP 260 433506112572301 1

06N 39E 23AAC2 125 435015111495302 3 03N 29E 14ADD1 MTR TEST 262 433520112572601 2

06N 35E 21AAB1 126 435028112202601 2 03N 32E 14AAC1 ANL OBS A 001 266 433545112394101 4

06N 39E 16DAA1 127 435048111512701 3 03N 29E 01DBB1 USGS 98 269 433657112563601 1

06N 39E 13ABA1 129 435118111481601 3 04N 30E 30DAB1 NRF 9 275 433840112550201 2

06N 36E 11ABA3 130 435208112105103 2 04N 30E 29CBB1 NRF 10 276 433841112545201 2
72 Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method Table 6.

Wells identified for removal based on genetic algorithm searches, eastern Snake River Plain, Idaho.—Continued

Number

of sites

removed

Optimized Co-op network Optimized USGS-INL network

Local name Map No. Site No. Times identified Local name Map No. Site No. Times identified

80—Continued 06N 39E 10BBB1 131 435209111512101 2 04N 30E 30ADC1 NRF 8 277 433843112550901 2

06N 38E 02DBD1 132 435228111563401 3 04N 30E 29BCD1 NRF 11 278 433847112544201 3

07N 39E 34CCB1 133 435314111511902 3 04N 30E 30ACA1 USGS 102 279 433853112551601 1

07N 35E 20CBD1 137 435504112222301 4 04N 30E 29BAC1 NRF 12 280 433855112543201 1

07N 38E 23DBA2 138 435506111563102 3 04N 30E 19DDD1 NRF 6 281 433910112550101 2

07N 40E 19ADD4 139 435516111464002 3 04N 30E 19DAD1 NRF 13 283 433928112545401 1

07N 36E 22ABD4 140 435528112121201 5 06N 32E 26CAB1 ANP 10 306 434909112400401 1

07N 34E 24BBA1 141 435540112243901 1 06N 31E 24DDA1 GIN 1 310 434947112414301 2

07N 39E 16DBB3 142 435605111515801 3 06N 32E 22CCB1 GIN 2 311 434949112413401 2

07N 35E 13AAD1 143 435626112164301 3 06N 32E 22CCB3 GIN 4 312 434949112413601 2

07N 36E 11ABB1 145 435723112111101 3 06N 32E 22CBC1 GIN 5 313 434953112413301 2

07N 36E 09BBB1 146 435728112141301 4 06N 32E 22BBD6 TAN 23A 314 435020112412704 5

07N 34E 04CDC1 147 435728112281101 4 06N 32E 22BBD1 TAN 15 315 435021112412701 1

08N 41E 33ABB1 149 435904111373101 3 06N 31E 13CDD1 TAN 8 317 435034112421701 3

08N 34E 27CDD1 150 435912112264801 3 06N 31E 13CCA3 TAN 14 319 435039112423701 2

08N 36E 21DCD1 152 440002112131801 3 06N 31E 13CCA2 TAN 13A 320 435040112423801 4

08N 34E 11DCC1 154 440151112252301 1 06N 31E 13DBB4 TAN 18 321 435051112421401 5

08N 40E 06CCC1 155 440236111474701 1 06N 31E 13DBB5 TAN 19 322 435051112421501 4

09N 36E 33CBB1 158 440353112135701 4 06N 31E 13DBB1 USGS 24 323 435053112420801 2

09N 34E 29DAB1 159 440447112284401 1 06N 31E 11CDC1 FET DISP 3 324 435124112433701 1

09N 36E 15CCC1 160 440608112125001 1 07N 31E 28DAB1 P AND W 2 331 435419112453101 1

09N 36E 04BAA1 164 440841112133001 5 07N 31E 20BDB1 USGS 126A 334 435529112471301 2
Appendixes 73

Appendix A. R-Package Documentation

Appendix A is available for viewing or download at http://pubs.usgs.gov/sir/2013/5120/.

Appendix B. Information about the R Session

Appendix B is available for viewing or download at http://pubs.usgs.gov/sir/2013/5120/.
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Fisher— Water-Level Monitoring Networks, Eastern Snake River Plain Aquifer Using a Kriging-Based Genetic Algorithm Method—Scientific Investigations Report 2013–5120