National Water-Quality Assessment Program

Numerical Model Simulations of Nitrate Concentrations in 

Groundwater Using Various Nitrogen Input Scenarios, 

Mid-Snake Region, South-Central Idaho

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2012–5237
Cover:

Top photograph: 

Photograph copyright Kestrel Aerial Services, Inc., ©www.kestrelaerial.com, used 

with permission.

Model image: Simulated nitrate concentrations in groundwater in 2048 from linearly 

projected nitrogen input.

Bottom photograph: Blue Lakes Spring. Photograph by Kenneth Skinner, U.S. 

Geological Survey, Idaho, 2005.
Numerical Model Simulations of Nitrate 

Concentrations in Groundwater Using 

Various Nitrogen Input Scenarios, 

Mid-Snake Region, South-Central Idaho

By Kenneth D. Skinner and Michael G. Rupert

National Water-Quality Assessment Program

Scientific Investigations Report 2012–5237

U.S. Department of the Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Suggested citation:

Skinner, K.D., and Rupert, M.G., 2012, Numerical model simulations of nitrate concentrations in groundwater 

using various nitrogen input scenarios, mid-Snake region, south-central Idaho: U.S. Geological Survey Scientific 

Investigations Report 2012–5237, 30 p.
iii

Foreword

The U.S. Geological Survey (USGS) is committed to providing the Nation with reliable scientific information 

that helps to enhance and protect the overall quality of life and that facilitates effective management of water, 

biological, energy, and mineral resources (http://www.usgs.gov/). Information on the Nation’s water resources 

is critical to ensuring long-term availability of water that is safe for drinking and recreation and is suitable 

for industry, irrigation, and fish and wildlife. Population growth and increasing demands for water make the 

availability of that water, measured in terms of quantity and quality, even more essential to the long-term 

sustainability of our communities and ecosystems.

The USGS implemented the National Water-Quality Assessment (NAWQA) Program in 1991 to support national, 

regional, State, and local information needs and decisions related to water-quality management and policy 

(http://water.usgs.gov/nawqa). The NAWQA Program is designed to answer: What is the quality of our 

Nation’s streams and groundwater? How are conditions changing over time? How do natural features and 

human activities affect the quality of streams and groundwater, and where are those effects most pronounced? 

By combining information on water chemistry, physical characteristics, stream habitat, and aquatic life, the 

NAWQA Program aims to provide science-based insights for current and emerging water issues and priorities. 

From 1991 to 2001, the NAWQA Program completed interdisciplinary assessments and established a baseline 

understanding of water-quality conditions in 51 of the Nation’s river basins and aquifers, referred to as Study 

Units (http://water.usgs.gov/nawqa/studies/study_units.html).

National and regional assessments are ongoing in the second decade (2001–2012) of the NAWQA Program as 

42 of the 51 Study Units are selectively reassessed. These assessments extend the findings in the Study Units 

by determining water-quality status and trends at sites that have been consistently monitored for more than a 

decade, and filling critical gaps in characterizing the quality of surface water and groundwater. For example, 

increased emphasis has been placed on assessing the quality of source water and finished water associated 

with many of the Nation’s largest community water systems. During the second decade, NAWQA is addressing 

five national priority topics that build an understanding of how natural features and human activities affect 

water quality, and establish links between sources of contaminants, the transport of those contaminants 

through the hydrologic system, and the potential effects of contaminants on humans and aquatic ecosystems. 

Included are studies on the fate of agricultural chemicals, effects of urbanization on stream ecosystems, 

bioaccumulation of mercury in stream ecosystems, effects of nutrient enrichment on aquatic ecosystems, and 

transport of contaminants to public-supply wells. In addition, national syntheses of information on pesticides, 

volatile organic compounds (VOCs), nutrients, trace elements, and aquatic ecology are continuing. 

The USGS aims to disseminate credible, timely, and relevant science information to address practical and 

effective water-resource management and strategies that protect and restore water quality. We hope this 

NAWQA publication will provide you with insights and information to meet your needs, and will foster 

increased citizen awareness and involvement in the protection and restoration of our Nation’s waters. 

The USGS recognizes that a national assessment by a single program cannot address all water-resource 

issues of interest. External coordination at all levels is critical for cost-effective management, regulation, 

and conservation of our Nation’s water resources. The NAWQA Program, therefore, depends on advice 

and information from other agencies—Federal, State, regional, interstate, Tribal, and local—as well as 

nongovernmental organizations, industry, academia, and other stakeholder groups. Your assistance and 

suggestions are greatly appreciated.

William H. Werkheiser, 

USGS Associate Director for Water
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v

Contents

Foreword ........................................................................................................................................................iii

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

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

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

Description of Study Area ...................................................................................................................3

Previous Investigations and Models .................................................................................................5

Eastern Snake River Plain Model .............................................................................................5

Advective Transport Modeling ..................................................................................................6

Subregional Model Development .............................................................................................6

Solute Transport Modeling................................................................................................6

Methods of Investigation..............................................................................................................................8

Hydrogeologic and Anthropogenic Data ..........................................................................................8

Groundwater-Quality Data .........................................................................................................8

Geographic Information System Data......................................................................................9

Delineating Areas Contributing to Wells................................................................................12

Logistic Regression Statistical Method ..........................................................................................12

Construction of Probability Maps ....................................................................................................14

Groundwater Probability Modeling ..........................................................................................................14

Probability Model Verification ..........................................................................................................14

Estimating Nitrogen Input to the Subregional Model ............................................................................15

Nitrogen Input by Skinner and Donato (2003) ................................................................................15

Nitrogen Input Prorated Using a Nitrate Probability Map ...........................................................15

Nitrogen Input Optimization and Comparison Using UCODE_2005 ............................................15

Nitrate Transport in the Subregional Model and Particle-Tracking Results......................................18

Transport Modeling Results ..............................................................................................................18

Particle-Tracking Simulations...........................................................................................................21

Model Use, Guidance, and Limitations ....................................................................................................26

Summary........................................................................................................................................................26

References Cited..........................................................................................................................................27
vi

Figures

Figure 1. Map showing study area location, subregional model boundary, perched

aquifer, and urbanized areas, mid-Snake region, south-central Idaho ............... 2

Figure 2. Graph showing number of dairy cattle in the six mid-Snake counties,

south-central Idaho ........................................................................ 3

Figure 3. Graph showing increased nitrogen input in the past 50 years for fertilizer, and

in the past 20 years for dairy and beef cattle, in six mid-Snake counties,

south-central Idaho ........................................................................ 4

Figure 4. Map showing extent of a subregional groundwater flow model of the

mid-Snake region within the eastern Snake River Plain regional model,

south-central Idaho ........................................................................ 7

Figure 5. Map showing nitrate concentration data from groundwater used to create

probability maps and to calibrate nitrogen input to the subregional model in

the mid-Snake region, south-central Idaho ............................................. 9

Figure 6. Conceptual diagram of wedge-shaped 90-degree buffer and circular buffer

used to delineate areas that contribute recharge to a well ........................... 13

Figure 7. Deciles of risk plot showing the high correlation between percentage of actual

nitrate detections and predicted probability of detecting nitrate concentrations

groundwater, using 10 equal intervals ................................................... 15

Figure 8. Graphs showing simulated nitrate concentrations and corresponding model

residuals with linear regression trend lines indicating model bias in nitrogen

input model updated from Skinner and Donato (2003) (SD) and nitrogen input

model updated from Skinner and Donato (2003) and prorated with a probability

model (SDPROB) ........................................................................... 17

Figure 9. Graph showing nitrogen trends based on data collected during 1999–2008,

south-central Idaho ........................................................................ 18

Figure 10. Maps showing simulated nitrate concentrations in groundwater from linearly

projected nitrogen input prorated by the probability map , eastern Snake River

Plain aquifer, Idaho, 2008 and 2048 ...................................................... 19

Figure 11. Map showing simulated nitrate concentrations in 2048 and time-series plots

for three locations in the Snake River Plain aquifer, south-central Idaho ............ 20

Figure 12. Map showing forward particle-tracking simulation starting in the easternmost

column of model cells of the subregional model, south-central Idaho ............... 22

Figure 13. Map showing forward particle-tracking simulation starting in the local

perched aquifer of the subregional model, south-central Idaho ..................... 23

Figure 14. Map showing backward particle tracking simulation in the subregional model,

south-central Idaho ........................................................................ 24

Figure 15. Cross section and map showing the eastern Snake River Plain aquifer thins to

the west toward the Thousand Springs area, south-central Idaho .................. 25
vii

Tables

Table 1. Independent variables used to estimate probability of nitrate concentrations

greater than 2 milligrams per liter in groundwater, mid-Snake region,

south-central Idaho ........................................................................ 10

Table 2. Logistic regression model simulation, coefficients, and individual p-values of

independent variables significantly related with the probability of detecting

measured nitrate concentrations greater than 2 milligrams per liter in

groundwater of the Mid-Snake Region, south-central Idaho ........................ 14

Table 3. Comparison of the effectiveness of two nitrogen input layers in the

subregional model using UCODE_2005 statistical results .............................. 16

Conversion Factors, Datums, and Abbreviations and 

Acronyms

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

gallon per minute (gal/min) 0.06309 liter per second (L/s)

Mass

ton, short (2,000 lb) 0.9072 megagram (Mg) 

Transmissivity*

foot squared per day (ft2/d) 0.09290 meter squared per day (m2/d) 

Application rate

pound per acre (lb/acre) 1.121 kilogram per hectare (kg/ha)

SI to Inch/Pound

Multiply By To obtain

Length

millimeter (mm) 0.03937 inch (in.)

Flow rate

meter per day (m/d) 3.281 foot per day (ft/d)

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

Mass

kilogram (kg) 2.205 pound avoirdupois (lb)
viii

Concentrations of chemical constituents in water are given in milligrams per liter (mg/L).

*Transmissivity: The standard unit for transmissivity is cubic foot per day per square foot times 

foot of aquifer thickness [(ft3/d)/ft2]ft. In this report, the mathematically reduced form, foot 

squared per day (ft2/d), is used for convenience.

Datums

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

(NAVD 88).

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

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

Abbreviations and Acronyms

Abbreviation 

or acronym

Definition

Reclamation Bureau of Reclamation

CEV Calculated error variance

CSS Composite scaled sensitivity

ESRP Eastern Snake River Plain

GIS Geographic information system

NAWQA National Water-Quality Assessment Program

PRISM Parameter-elevation Regression on Independent Slopes Model

RASA Regional Aquifer-System Analysis

SD Nitrogen input layer, from Skinner and Donato (2003)

SDPROB Nitrogen input layer, updated from Skinner and Donato (2003), and prorated by 

a map showing probability of detecting elevated concentrations of nitrate

SE Standard error

SSURGO Soil Survey Geographic Database from the Natural Resources Conservation 

Service

USGS U.S. Geological Survey

Conversion Factors, Datums, and Abbreviations and 

Acronyms—Continued
Abstract

As part of the U.S. Geological Survey’s National Water 

Quality Assessment (NAWQA) program nitrate transport in 

groundwater was modeled in the mid-Snake River region in 

south-central Idaho to project future concentrations of nitrate. 

Model simulation results indicated that nitrate concentrations 

would continue to increase over time, eventually exceeding the 

U.S. Environmental Protection Agency maximum contaminant 

level for drinking water of 10 milligrams per liter in some 

areas. A subregional groundwater model simulated the change 

of nitrate concentrations in groundwater over time in response 

to three nitrogen input scenarios: (1) nitrogen input fixed at 

2008 levels; (2) nitrogen input increased from 2008 to 2028 

using the same rate of increase as the average rate of increase 

during the previous 10 years (1998 through 2008); after 2028, 

nitrogen input is fixed at 2028 levels; and (3) nitrogen input 

related to agriculture completely halted, with only nitrogen 

input from precipitation remaining.

Scenarios 1 and 2 project that nitrate concentrations 

in groundwater continue to increase from 10 to 50 years 

beyond the year nitrogen input is fixed, depending on the 

location in the model area. Projected nitrate concentrations in 

groundwater increase by as much as 2–4 milligrams per liter in 

many areas, with nitrate concentrations in some areas reaching 

10 milligrams per liter. Scenario 3, although unrealistic, 

estimates how long (20–50 years) it would take nitrate in 

groundwater to return to background concentrations—the 

“flushing time” of the system. 

The amount of nitrate concentration increase cannot 

be explained solely by differences in nitrogen input; in fact, 

some areas with the highest amount of nitrogen input have the 

lowest increase in nitrate concentration. The geometry of the 

aquifer and the pattern of regional groundwater flow through 

the aquifer greatly influence nitrate concentrations. The 

aquifer thins toward discharge areas along the Snake River 

which forces upward convergence of good-quality regional 

groundwater that mixes with the nitrate-laden groundwater in 

the uppermost parts of the aquifer, which results in lowered 

nitrate concentrations. 

A new method of inputting nitrogen to the subregional 

groundwater model was used that prorates nitrogen input 

by the probability of detecting nitrate concentrations greater 

than 2 mg/L. The probability map is based on correlations 

with physical factors, and prorates an existing nitrogen input 

dataset providing an estimate of nitrogen flux to the water 

table that accounts for new factors such as soil properties. 

The effectiveness of this updated nitrogen input method was 

evaluated using the software UCODE_2005.

Introduction

Quality of groundwater is an ongoing concern in Idaho. 

In 1990, groundwater accounted for nearly 85 percent of the 

drinking water in the State; in 1995, it accounted for almost 

95 percent (Solley and others, 1993, 1998). Previous studies 

in Idaho detected high concentrations of nitrate in many of 

the State’s aquifers (Rupert, 1994; Crockett, 1995; Rupert 

and others, 1996). Previous studies also detected increasing 

concentrations of nitrate in a growing number of public- and 

domestic-use wells (Parliman and Young, 1987; Young and 

others, 1987a; Young and others, 1987b; Rupert, 1994; Clark 

and Ott, 1996; Rupert and others, 1996). The predominant 

sources of nitrate in groundwater throughout much of the 

State are inorganic fertilizer, cattle manure, and legume crops 

(Rupert, 1996).

Numerical Model Simulations of Nitrate Concentrations in 

Groundwater Using Various Nitrogen Input Scenarios, 

Mid-Snake Region, South-Central Idaho

By Kenneth D. Skinner and Michael G. Rupert
2 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

Increasing nitrate concentrations in groundwater are of 

particular concern in the mid-Snake region of south-central 

Idaho (fig. 1). The region is experiencing substantial growth 

in the dairy industry (fig. 2). Particularly large growth in 

dairy operations is occurring in Gooding, Jerome, and Twin 

Falls Counties (Idaho Agricultural Statistics Service, 1999; 

U.S. Department of Agriculture, 2010). These increased 

sources of nitrate to groundwater are in addition to sources 

from existing, long-term agricultural activities in the region. 

Because of declining groundwater quality in parts of the 

mid-Snake region, the Idaho Department of Environmental 

Quality designated those areas as priority water-quality 

management areas and targeted them for further study and 

assistance to improve land-use planning, nitrogen monitoring, 

and nitrogen management. In response to these concerns, 

the U.S. Geological Survey (USGS) National Water Quality 

Assessment (NAWQA) program conducted this investigation 

in the mid-Snake region of south-central Idaho (fig. 1). 

Thousand Springs Area

MILNER DAM

Blue Lakes Spring

BLAINE

CASSIA

LINCOLN

POWER

CAMAS BUTTE

TWIN FALLS

ELMORE

GOODING

JEROME

MINIDOKA

BINGHAM

King 

Hill

Twin 

Falls

Jerome

Buhl

Rupert

Paul

Gooding

Wendell

Snake River

Burley

Aerial imagery from the National Agriculture Imagery Program (NAIP), 

1-meter resolution, 2003 Universal Transverse Mercator, Zone 11 North, 

North American Datum of 1983

115° 114°30' 114° 113°30' 113°

43°30'

43°

42°30'

0 10 20 30 MILES

0 10 20 30 KILOMETERS

Eastern Snake River 

Plain boundary

IDAHO

Map

area

EXPLANATION

Study area—Sub-regional model boundary

Perched aquifer

City or town

Figure 1. Study area location, subregional model boundary, perched aquifer, and urbanized areas, 

mid-Snake region, south-central Idaho.
Introduction 3

Purpose and Scope

This report documents the modeling of nitrate 

concentrations in groundwater of the mid-Snake region 

of south-central Idaho (containing all or parts of Blaine, 

Bingham, Butte, Camas, Cassia, Elmore, Gooding, Jerome, 

Lincoln, Minidoka, Power, and Twin Falls Counties) 

using an existing three-dimensional groundwater-flow and 

transport model. The model simulates the change in nitrate 

concentrations in groundwater over time in response to three 

nitrogen input scenarios:

• Nitrogen input fixed at 2008 levels.

• Nitrogen input increased from 2008 to 2028 using the 

same rate of increase as the average rate of increase 

during the previous 10 years (1998 through 2008); 

after 2028, nitrogen input is fixed at 2028 levels.

• Nitrogen input related to agriculture completely halted, 

with only nitrogen input from precipitation remaining.

The study incorporated a new approach for estimating 

nitrogen input to the groundwater flow and transport 

model. A map estimating the probability of elevated nitrate 

concentrations in groundwater was used to prorate nitrogen 

input across the modeled area. The probability map was 

developed using logistic regression, which made correlations 

between measured nitrate concentrations in groundwater and 

cultural, hydrologic, land use, soil, and precipitation factors. 

The probability map then was used to prorate nitrogen input 

from the land surface to the water table. The groundwater-flow 

and transport model subsequently simulated transport of the 

nitrate from the water table, through the aquifer system, and 

finally to the discharge locations in the Thousand Springs area. 

The scope of this report is limited to describing 

the simulation of the change in nitrate concentration in 

groundwater based on the observed and predicted changes in 

nitrogen input to the groundwater-flow and transport model. It 

was beyond the scope of this report to make any changes in, or 

to evaluate the effectiveness of, the existing three-dimensional 

groundwater-flow and transport model. For this report, the 

nitrogen was input to the groundwater-flow and transport 

model by using the auxiliary parameter in the MODFLOW 

WEL package (Harbaugh and McDonald, 1996a and 1996b). 

This was the only model parameter changed for this report; 

all other aspects of the groundwater-flow and transport model 

were left alone. 

Description of Study Area

The study area (5,341 mi2 in south-central Idaho; fig. 1) 

is the groundwater flow and transport model boundary of 

Clark and others (2006), referred to as the “subregional 

model” in this report. The model boundary includes Jerome 

and Minidoka Counties and parts of Blaine, Bingham, Butte, 

Camas, Cassia, Elmore, Gooding, Lincoln, Power, and Twin 

Falls Counties. The study area is in the downstream part of 

the eastern Snake River Plain. About 66 percent of the area 

is rangeland, 11 percent is flood-irrigated land, 21 percent is 

sprinkler-irrigated land, and the remaining 2 percent consists 

of several other land-use types. The area predominantly is 

semiarid; mean annual precipitation ranges from 8 to 18 in. 

Most agricultural land in the study area is near the Snake 

River, a major source of water for irrigation.

tac12-0748_fig02 Number of dairy cattle

19201930

1940

19501960

19701980

19902000

2010

Year

The number of dairy cattle in this 6-county

region of the Upper Snake River Basin

has increased more than 300,000 head 

from 1987 to 2007. The increase of nitrogen 

input is roughly equivalent to an increase of 

4.6 million to 6.2 million human adults. 

150,000

125,000

100,000

75,000

50,000

25,000

0

Gooding County

Lincoln County

Minidoka County

Jerome County

Cassia County

Twin Falls County

EXPLANATION

IDAHO

Figure 2. Number of dairy cattle in the six mid-Snake counties, 

south-central Idaho. Data from U.S. Department of Agriculture 

(2010).
4 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

Nitrogen input from fertilizers and cattle manure in the 

study area has increased dramatically during the past 50 years 

(fig. 3), and increasing nitrate (the most common form of 

dissolved nitrogen because of its high mobility) concentrations 

in groundwater reflect the increase in nitrogen input from 

fertilizer use and cattle manure. Even though nitrogen input 

from fertilizer is much greater than that from cattle manure, 

the number of dairy cattle in the study area has increased fivefold during the last 30 years, which is a source of increasing 

nitrate in groundwater (fig. 2). A significant amount of the 

cattle manure is used as fertilizer on surrounding fields, but 

excess nitrogen from the cattle manure can contaminate 

groundwater if the manure is applied in excess of agronomic 

rates. Clark and others (2006) show that nitrogen source 

amounts vary throughout the study area with fertilizer and (or) 

cattle manure accounting for most nitrogen input. The 

dissolved nitrate from nitrogen input is readily transported 

to groundwater because of the well-drained soils and highly 

permeable basalt in the study area. Nitrate persists after it 

reaches the aquifer because the highly oxic groundwater 

prevents denitrification. Frans and others (2012) reported that 

nitrate concentrations in groundwater have increased across 

much of the study area since 1992.

The groundwater system in the study area consists of 

two aquifers: a regional basalt aquifer and a local perched 

aquifer (fig. 1). The regional basalt aquifer underlying the 

eastern Snake River Plain provides most of the groundwater 

that moves through the study area. The local perched aquifer 

(fig. 1) overlying the eastern Snake River Plain regional basalt 

aquifer in Minidoka and Cassia Counties is a small, localized 

aquifer that was not included in the subregional model. 

The eastern Snake River Plain regional basalt aquifer, 

which encompasses the study area, primarily comprises 

vesicular and fractured olivine basalt flows (Quaternary age) 

of the Snake River Group (Whitehead, 1992). These basalt 

flows average from 20 to 25 ft thick, but in places can exceed 

1,000 ft thick. The top of the basalt generally is less than 

100 ft below land surface throughout this part of the plain.

Layered basalt flows in the eastern Snake River Plain 

aquifer yield exceptionally large volumes of water to wells 

and springs. Individual well yields are some of the highest in 

the Nation, typically ranging from 2,000 to 3,000 gal/ min to as 

much as 7,000 gal/min with minimal drawdown (Whitehead, 

1992; Lindholm, 1996). Transmissivity commonly 

is 100,000 ft2/d and can be as high as 1,000,000 ft2/d 

(Whitehead, 1992). Locally, aquifer properties can vary 

greatly; however, on a regional scale the variability is 

minimal.

Regional groundwater in the eastern Snake River Plain 

aquifer moves from the northeast to the southwest (Rupert, 

1997). Groundwater is discharged as springs and seeps to the 

Snake River along the reach bordering Twin Falls, Jerome, 

and Gooding Counties (fig. 1). This reach of the Snake River 

is known as the Thousand Springs area and is located in the 

downstream part of the mid-Snake reach that extends from 

King Hill to Milner Dam. Groundwater discharge to this reach 

of the Snake River increased considerably from about 1910 

through the early 1950s (Kjelstrom, 1992). The increase is 

attributed to recharge from surface-water irrigation north of 

the Snake River. Since the early 1950s, groundwater discharge 

to the Snake River has decreased as a result of (1) increased 

groundwater withdrawals for irrigation (Moreland and others, 

0 

10 

20 

30 

40 

50 

60 

70 

1920 1935 1950 1965 1980 1995 2010 

Fertilizer

Cattle manure

Atmospheric deposition 

Year

EXPLANATION

Nitrogen, in millions of kilograms

Figure 3. Increased nitrogen input in the past 50 years for fertilizer, and in the past 20 years for dairy 

and beef cattle, in six mid-Snake counties, south-central Idaho. Data from Ruddy and others (2006) as 

modified by Gronberg and Spahr (U.S. Geological Survey, written commun., 2008), and U.S. Department of 

Agriculture (2010).
Introduction 5

1976), (2) introduction of efficient irrigation practices such 

as conversion from flood to sprinkler irrigation, and (3) local 

droughts (Kjelstrom, 1992).

In the Burley area (fig. 1), a local perched aquifer is 

located above a blue-clay layer about 60 to 120 ft below land 

surface (Rupert, 1997). In this perched aquifer, water moves 

northward at the southern boundary of the local alluvial 

aquifer and moves westward near the western boundary. 

The water level in the perched aquifer is about 100 ft above 

water levels of the Snake River Plain aquifer. The perched 

aquifer is recharged predominantly from the infiltration of 

irrigation water. Several wells completed in this aquifer go dry 

seasonally after irrigation ceases and become operational again 

after the start of the next irrigation season (Rupert, 1997). 

Previous Investigations and Models

The water resources of the mid-Snake region have been 

studied in investigations of regional and local hydrologic 

systems of Idaho, including investigations of the Snake 

River Plain in southern Idaho that were part of the USGS 

Regional Aquifer System Analysis (RASA) and National 

Water-Quality Assessment (NAWQA) programs. The RASA 

and NAWQA programs in southern Idaho have produced 

an assemblage of geologic, hydrologic, and water-quality 

information about the groundwater systems in the region, as 

well as computer simulation of those systems. Whitehead 

(1992) provided an extensive description of the regional 

hydrogeologic framework, hydrologic properties, and 

geologic controls on groundwater movement. Kjelstrom 

(1995) described streamflow gains and losses and compiled 

regional groundwater budgets. Garabedian (1992) synthesized 

hydrologic data and constructed a regional groundwater flow 

model. State agencies and universities also have created 

numerical groundwater flow models of the eastern Snake 

River Plain regional basalt aquifer. The Idaho Department of 

Water Resources and the University of Idaho created the first 

model of the aquifer. Many revisions to the model have been 

released (Cosgrove and others, 1999; Idaho Department of 

Water Resources 2011). These studies provide the regional 

context for describing flow in the model area.

Eastern Snake River Plain Model 

The first regional, three-dimensional groundwater flow 

model of the eastern Snake River Plain aquifer system was 

developed and calibrated during the USGS RASA program 

(Garabedian, 1992). The goal of the eastern Snake River Plain 

modeling study was to reproduce known aquifer conditions 

(for example, head and spring discharge) within reasonable 

ranges of values, and to examine the hydrologic effects of 

changes in model input data through sensitivity testing. Initial 

values of aquifer hydraulic properties, recharge, discharge, and 

pumpage were estimated for model input based on geologic 

and hydrologic information. Model output was compared with 

known aquifer conditions to determine whether the initial 

estimates were reasonable. The model was tested to determine 

its sensitivity to changes in transmissivity, storage coefficient, 

aquifer leakance, recharge, riverbed or spring-outlet 

conductance, groundwater pumpage, and boundary flux. Input 

data were adjusted within reasonable ranges to achieve a better 

fit to known conditions. Adjustments were made to leastknown and to most-sensitive model parameters. Simulation 

results indicated how the aquifer might have responded to past 

stresses, such as increased pumping and reduced recharge, and 

how the aquifer might respond to hypothetical stresses in the 

future.

The three-dimensional, finite-difference, groundwaterflow model MODFLOW-88 (McDonald and Harbaugh, 1988) 

was used to develop the eastern Snake River Plain model. 

Garabedian (1992) subdivided the regional aquifer system 

vertically into four model layers. Layer 1 represents the upper 

200 ft of the aquifer system; layer 2 represents the next 300 ft 

below. Layers 1 and 2 contain Quaternary basalt of the Snake 

River Group and Tertiary basalt. Layers 3 and 4 are of lesser 

areal extent, and are present only where basalt of the Snake 

River Group and interlayered sedimentary rocks are greater 

than 500 ft thick. Layer 3 is less than or equal to 500 ft thick, 

and is present only across the central part of the Snake River 

Plain. Layer 4 ranges in thickness from 0 to about 3,000 ft in 

the central part of the plain.

Local aquifers perched above the regional aquifer 

system were not simulated, although recharge to perched 

aquifers was assumed to reach the regional aquifer. Vertical 

hydraulic conductivity was assumed to be anisotropic because 

of low-hydraulic-conductivity basalt between permeable 

flow tops and fine-grained layers within sand and gravel 

zones. Horizontal hydraulic conductivity was assumed to be 

isotropic because two-dimensional simulations indicated small 

differences in model simulation results between isotropic and 

anisotropic conditions (Garabedian, 1992).

A steady-state model was constructed using 1980 wateryear fluxes (Garabedian, 1992, table 16). The 1980 fluxes were 

assumed to approximate the average annual flux for 1950–80. 

During that period, hydrologic conditions were stable relative 

to conditions from 1880 to 1950. Groundwater recharge was 

assumed to equal discharge (steady-state conditions) from 

1950 to 1980 because irrigation diversions and groundwater 

levels were assumed to be stable relative to conditions from 

1880 to 1950 (Garabedian, 1992, fig. 8 and pl. 4). Declines 

in groundwater levels due to pumping, climatic variations, 

and decreased surface-water diversions from 1950 to 1980 

generally were small, and changes in storage accordingly were 

small (about 1 percent). These small changes in storage were 

included as part of the recharge period by including them in 

the computation of approximate steady-state fluxes. Recharge 

from irrigation was assumed to take place directly below 

surface-water-irrigated areas.
6 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

Advective Transport Modeling

Quantitative estimates of groundwater flow directions 

and travel times for advective flow in the eastern Snake 

River Plain model were made by Ackerman (1995) using the 

MODPATH advective transport particle-tracking package 

(Pollock, 1989). The advective transport model required 

additional and updated information about the nature of flow 

across model boundaries, aquifer thickness, and porosity. 

Values of porosity used in calibration were constrained by 

prior information about basalt porosity reported for more than 

1,500 individual cores from test holes, wells, and outcrops 

near the south side of the Idaho National Engineering 

Laboratory (Knutson and others, 1990, 1992) occupying 

roughly the northwest one-third of the eastern Snake River 

Plain model area. Other estimates of porosity were derived 

from other transport models, materials balances, and general 

aquifer descriptions. Porosity values were determined for large 

core samples using volumetric and gravimetric methods and a 

helium porosimeter. Porosities were 0.08–0.25 in the central 

80 percent of samples, and porosities were 0.11–0.21 in the 

central 50 percent of samples. Previous models used porosity 

estimates of 0.10, materials balances had a porosity range of 

0.06 to 0.10, and general aquifer descriptions used porosity 

estimates of 0.05–0.15.

Two radiologic contaminants, iodine 129 and tritium 

(hydrogen-3), are relatively conservative tracers that were 

used to determine the optimum porosity value (Ackerman, 

1995). The contaminants were introduced directly through a 

590-ft-deep injection well at the Idaho Chemical Processing 

Plant at the Idaho National Engineering Laboratory starting in 

1953 (Mann and Cecil, 1990, p. 13). Tritium was first detected 

in wells near the south boundary of the Idaho National 

Engineering Laboratory 30 years later (Mann and Cecil, 

1990, p. 27), and iodine-129 was detected approximately 38 

years later (Ackerman, 1995). The half-life of iodine-129 

is 15.7 million years and that of tritium is 12.4 years. The 

porosity was adjusted until simulated travel times matched 

measured travel times. The calibration value for porosity was 

0.21 for iodine-129 and 0.25 for tritium, which corresponds 

with the higher porosity values measured in the core samples. 

The travel time for iodine-129 was considered more useful 

for comparison with predicted travel times because of a lower 

analytical detection limit, longer half-life, and longer flow 

path. The resulting value for porosity was 0.21.

Subregional Model Development

Clark and others (2006) converted the eastern Snake 

River Plain model from MODFLOW-88 to MODFLOW-2000 

(Harbaugh and others, 2000), and rediscretized the model 

grid from the original 4-mi grid cells to approximately 1-mi 

grid cells. Clark and others (2006) derived flow boundary 

conditions and initial estimates of aquifer properties from 

the calibrated eastern Snake River Plain model. Clark and 

others (2006) “clipped” the eastern Snake River Plain model 

at column 25, near the eastern boundary of Minidoka County 

(fig. 4), to include only the western part of the model. Flow 

values across column 25 of the model were input as constant 

flow into the subregional model cells in each model layer 

along the upgradient boundary using the well package. The 

model was run with this new configuration; model simulated 

results of water levels and budget values were checked 

qualitatively against the original model. Comparison results 

were nearly identical to the original model.

No changes were made to the model configuration that 

affected groundwater elevations, discharge from springs, 

recharge sources, and other critical values or conditions. 

Boundary conditions also were the same, simulated as 

specified flux and head-dependent flux using the recharge, 

well, and river packages except, as noted above, along the 

eastern boundary of the model at column 25. Additionally, 

Clark and others (2006) did not change the hydraulic 

parameters from the original eastern Snake River Plain model. 

The only change to the subregional flow model resulted from 

the use of a smaller grid cell size than the one used in the 

eastern Snake River Plain model. The eastern Snake River 

Plain model incorporated the use of wells as recharge sources. 

Because of the smaller grid size used in the subregional 

model, Clark and others (2006) increased the number of cells 

to which recharge was input to incorporate more accurately 

the recharge volume; otherwise, the model would not match 

the original results. Therefore, the total volume of recharge 

remained the same as in the eastern Snake River Plain model, 

but this volume was distributed over 16 times the number of 

recharge wells. Wherever possible, Clark and others placed 

these additional recharge wells in the rediscretized adjacent 

grid cell blocks. The volume of each original recharge well 

was evenly distributed between the 16 smaller cells. A few 

minor adjustments in the locations of these new recharge wells 

were needed so the new simulations matched the original 

simulations, especially along the model boundary.

Solute Transport Modeling

The transport of dissolved nitrate in the mid-Snake 

region groundwater system was simulated by Clark and others 

(2006) by using the USGS three-dimensional solute-transport 

model MOC3D (Konikow and others, 1996; Kipp and others, 

1998), which was developed for use with MODFLOW 2000. 

MOC3D simulates three-dimensional solute transport in 

flowing groundwater, computing changes in concentrations of 

a dissolved constituent over time that can be caused by factors 

such as advective transport, hydrodynamic dispersion, and 

mixing or dilution. MOC3D uses the method of characteristics 

to solve the transport equation for a given time step based on 

groundwater flow rates computed with MODFLOW (Konikow 

and others, 1996; Kipp and others, 1998). 
Introduction 7

tac12-0748_fig04

Study area 

subregion model

Active area of eastern

Snake River Plain model

Eastern Snake River

Plain regional model grid

Column 25 

"clipped line"

EXPLANATION

Study area subregion model

0 25 50 75 100 MILES

0 25 50 75 100 KILOMETERS

CUSTER

BLAINE

LINCOLN

CASSIA

NEVADA UTAH POWER

BUTTE

TWIN FALLS

CAMAS

GOODING

MINIDOKA

ELMORE

JEROME

BINGHAM

OWYHEE

ONEIDA

115°15' 115° 114°45' 114°30' 114°15' 114° 114°45' 114°30' 114°15' 113° 112°45'

42°15'

42°

42°30'

42°45'

43°

43°15'

43°30'

Buhl Paul

Filer

Carey

Hansen Burley

Rupert

Jerome

Heyburn

Wendell

Gooding

Kimberly

Hagerman

Shoshone

Twin

Falls

SNAKE

RIVER

Basemap modified from U.S. Geological Survey, 

digital data, 1:100,000, 1999

Transverse_Mercator projection

False_Easting: 500000.000000

False_Northing: 100000.000000

Central_Meridian: -114.000000

Scale_Factor: 0.999600

Latitude_Of_Origin: 42.000000

Horizontal datum is North American Datum 1983

Figure 4. Extent of a subregional groundwater flow model of the mid-Snake region within the eastern Snake River 

Plain regional model, south-central Idaho (from Clark and others, 2006).
8 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

The conceptual model of nitrogen input developed by 

Skinner and Donato (2003) was used in the subregional 

model as a Geographic Information System (GIS) nitrogeninput layer. Skinner and Donato estimated nitrogen input and 

losses using county-level estimates of nitrogen input from five 

sources including cattle manure and fertilizer, and improved 

on the county-level estimates by applying the nitrogen input 

only to land-use categories related to that particular type of 

nitrogen input. For example, nitrogen input from fertilizer was 

applied only to agricultural and residential lands. Rangeland 

received nitrogen input only from precipitation. Input from 

the nitrogen layer was incorporated in the subregional model 

by using the area wells function in all active model cells in 

layer 1. The area wells function allows for the nitrogen input 

layer to be introduced in the subregional model by assigning 

a volumetric stress to each cell. The stress applied is a small 

volume of water that contains a specified concentration 

of nitrogen derived from the source layer. The initial preagriculture background concentration applied throughout the 

model was 0.5 mg/L in the upper model layer, 0.4 mg/L in the 

second and third underlying layers, and 0.3 mg/L in the fourth 

underlying layer.

Calibration of the transport results by Clark and others 

(2006) was accomplished by comparing nitrate concentrations 

from the simulations with nitrate concentration data from 

about 300 wells throughout the study area, and temporal 

nitrate concentrations from 11 springs that discharge into 

the Snake River. The calibration involved adjusting a 

multiplication factor applied to a nitrogen source layer to 

best represent nitrate conditions in groundwater. Rigorous 

calibration techniques, such as parameter estimation, were not 

used in the calibration process. Calibration was done mostly 

using trial and error techniques.

Methods of Investigation

Groundwater nitrate concentration data were compiled 

from several sources and imported into a GIS data layer. GIS 

data on depth to groundwater, groundwater velocity, total 

nitrogen input from cattle manure, total nitrogen input from 

fertilizer, percent land use, hydrogeology, precipitation, and 

soils were compiled to provide input data to groundwater 

nitrate probability models. Upgradient 90-degree, wedgeshaped zones of influence were calculated for each well 

where groundwater-quality data were available to determine 

either the total or average of the probability model input data 

upgradient from each sampled well. The optimum size for 

each 90-degree, wedge-shaped zone was calculated using 

groundwater age and groundwater-flow velocity. Two nitrogen 

input layers were evaluated for this report to determine 

which input layer is most effective in simulating measured 

nitrate concentrations in groundwater. The first nitrogen 

input layer is the one used by Skinner and Donato (2003), 

referred to hereafter as “SD.” The second nitrogen input 

layer is the same as used for SD, but was modified using a 

map of the probability of elevated nitrate concentrations to 

prorate nitrogen input to groundwater. The second nitrogen 

input layer is referred to hereafter as “SDPROB.” Each 

nitrogen input layer (SD and SDPROB) was used in separate 

solute transport simulations, and the results were compared 

to determine which layer was best reproduced measured 

nitrate concentrations. In each transport simulation model, 

the parameter estimation software UCODE_2005 (Poeter and 

others, 2005; Hill and Tiedeman, 2007) was used to estimate 

the multiplication factor for the nitrogen input layers that 

accounts for the conversion of nitrogen to nitrate in soils and 

the transport of nitrate through the unsaturated zone to the 

aquifer. The UCODE_2005 software also was used to compare 

the effectiveness of the two alternative nitrogen input layers, 

as compared to measured nitrate concentrations. 

Hydrogeologic and Anthropogenic Data

Groundwater-Quality Data

Groundwater-quality data on nitrate concentrations in 

samples collected from water in wells and springs for 2003 

through 2007 were compiled from several sources: the Idaho 

State Department of Agriculture (Tonia Mitchell, Idaho 

Department of Environmental Quality, unpub. data, 2008), 

the Idaho Division of Environmental Quality (Joe Baldwin 

and Tonia Mitchell, Idaho Department of Environmental 

Quality, unpub. data, 2008), the Bureau of Reclamation 

(Bill Stroud, Bureau of Reclamation, unpub. data, 2008), 

the U.S. Environmental Protection Agency (2008), and the 

U.S. Geological Survey (2008) (fig. 5). The nitrate data 

were filtered for data collected only in July during 2003–07, 

resulting in 525 separate nitrate concentration values from 

wells and springs. The nitrate data were filtered further for 

only the most recent data for a well or spring. All nitrate data 

were from water samples collected in wells completed at 

approximately the same depth and in the same hydrogeologic 

unit, the Snake River Group basalts.
Methods of Investigation 9

Geographic Information System Data

GIS data on cultural, hydrologic, land use, soils, 

and precipitation factors were compiled to input to the 

groundwater-nitrate-probability models. The GIS data 

consisted of depth to groundwater, groundwater velocity, total 

nitrogen input from cattle manure, total nitrogen input from 

fertilizer, percentages of land use, precipitation, and soils 

(table 1). 

Two sets of land-use data were evaluated: (1) a landuse dataset developed by the Bureau of Reclamation 

(Reclamation) (Bureau of Reclamation, unpub. data, 1987, 

1992), and (2) the 2001 National Land Cover Database 

(NLCD) from the USGS (LaMotte, 2008). The Reclamation 

and NLCD data were evaluated independently of each other 

during construction of the probability models to determine 

which land-use data produced the best correlation with 

elevated nitrate concentrations.

tac12-0748_fig05

Lincoln

Blaine

Jerome

Minidoka

Power

Gooding

Cassia

Twin Falls

114°W

114°30'W

115°W

43°N

42°30'N

43°30'N

EXPLANATION

Less than 2

2 to 10

Greater than 10

Nitrate concentration,

in milligrams per liter 0 6 12 18 24 MILES

0 6 12 18 24 KILOMETERS

County boundaries from U.S. Department of Agriculture, 2004, 1:24,000

Universal Transverse Mercator, Zone 11 North, North American Datum of 1983

Figure 5. Nitrate concentration data from groundwater used to create probability maps and to calibrate 

nitrogen input to the subregional model in the mid-Snake region, south-central Idaho.
10 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

Table 1. Independent variables used to estimate probability of nitrate concentrations greater than 2 milligrams per liter in 

groundwater, mid-Snake region, south-central Idaho.

[Independent variable: Reclamation, land cover developed by the Bureau of Reclamation; Daymet, a model that generates daily surfaces of temperature, 

precipitation, humidity, and radiation over large regions of complex terrain; NLCD, National Land Cover Database; PRISM, Parameter-elevation Regressions on 

Independent Slopes Model. Abbreviations: ft, foot; in., inch; lbs/acre, pounds per acre; mm, millimeter; mm/yr, millimeter per year; m/d, meter per day; 

>, greater than]

Independent variable Data source

County U.S. Department of Agriculture

Depth to ground water (ft) U.S. Geological Survey

Groundwater flow direction (zero to 360 degrees) U.S. Geological Survey

Groundwater velocity (m/d) U.S. Geological Survey

Irrigation district Idaho Department of Water Resources

Nitrogen input from cattle manure (animal units) U.S. Geological Survey

Nitrogen input from fertilizer [(lbs/acre)/yr] U.S. Geological Survey

Percentage of agricultural land (Reclamation, dryland 

agriculture, gravity and sprinkler irrigation, and feedlot 

combined)

Bureau of Reclamation

Percentage of rangeland (Reclamation, public and native land 

combined)

Bureau of Reclamation

Percentage of urban land (Reclamation, commercial and 

residential land combined)

Bureau of Reclamation

Percentage of agricultural land cover (NLCD, pasture/hay, 

rows crops, small grains combined)

U.S. Geological Survey

Percentage of rangeland (NLCD, bare rock, shrubland, 

grassland combined)

U.S. Geological Survey

Percentage of urban land cover (NLCD, commercial/industrial,

low- and high-intensity residential combined)

U.S. Geological Survey

Precipitation (mm/yr, PRISM, total precipitation for 2002) Daly and others (1994); Oregon State University

Precipitation (mm/yr, PRISM, total precipitation for 2007) Daly and others (1994); Oregon State University

Precipitation (mm/yr, PRISM, normal precipitation for

1961–90)

Daly and others (1994); Oregon State University

Precipitation (mm/yr, PRISM, normal precipitation for

1971–2000)

Daly and others (1994); Oregon State University

Precipitation (mm/yr, Daymet) Thornton and others (1997); Oak Ridge National Laboratory

Soil available water capacity (in./in.) U.S. Department of Agriculture, Natural Resources Conservation Service

Soil clay content (percentage of material less than 0.002 mm) U.S. Department of Agriculture, Natural Resources Conservation Service

Soil depth to bedrock within soil horizon (in.) U.S. Department of Agriculture, Natural Resources Conservation Service

Soil drainage (1 through 7; 1 has the lowest drainage rate) U.S. Department of Agriculture, Natural Resources Conservation Service

Soil hydrologic group (1 through 4; 1 has the highest

 infiltration rate)

U.S. Department of Agriculture, Natural Resources Conservation Service

Soil liquid limit (percentage of moisture by weight) U.S. Department of Agriculture, Natural Resources Conservation Service

Soil organic matter content (percent by weight) U.S. Department of Agriculture, Natural Resources Conservation Service

Soil plasticity (percentage of moisture by weight) U.S. Department of Agriculture, Natural Resources Conservation Service

Soil sand content (percentage of material > 0.05 mm) U.S. Department of Agriculture, Natural Resources Conservation Service

Soil silt content (percentage of material between 0.05 and

0.002 mm)

U.S. Department of Agriculture, Natural Resources Conservation Service

Soil septic suitability factor (1 through 3 with 3 = not suitable) U.S. Department of Agriculture, Natural Resources Conservation Service
Methods of Investigation 11

The Reclamation land-use dataset contains several 

categories, including dry farm agriculture, gravity irrigation, 

sprinkler irrigation, feedlot, public land, native land, 

commercial land, and residential land. Similar Reclamation 

land use categories were combined to make Reclamation 

and NLCD data comparable and to ease development of the 

probability models. An agricultural land category was created 

by combining the dry farm, gravity irrigation, sprinkler 

irrigation, dairy, and feedlot categories. A rangeland category 

was created by combining the public land and native land 

categories. An urban land category was created by combining 

the commercial land and residential land categories. 

The NLCD land-use data contain several categories, 

including pasture/hay, row crops, small grains, bare rock, 

shrubland, grassland, commercial/industrial, low-intensity 

residential, and high-intensity residential. NLCD land use 

categories also were combined. An agricultural land category 

was created by combining the pasture/hay, row crops, and 

small grains categories. A rangeland category was created by 

combining the bare rock, shrubland, and grassland categories. 

An urban category was created by combining the commercial/

industrial, low-intensity residential, and high-intensity 

residential categories. 

Precipitation data sets were derived from two sources. 

One precipitation data set was developed by Daly and 

others (1994) using the Parameter-elevation Regressions on 

Independent Slopes Model (PRISM). PRISM uses climatic 

point data and a digital elevation model to generate gridded 

estimates of climatic parameters. PRISM has been used 

extensively to map precipitation and minimum and maximum 

temperatures throughout the United States, Canada, and 

other countries. For this study, four PRISM data sets were 

considered: the total annual precipitation for 2002 and 2007, 

and the normal precipitation for 1961–90 and 1971–2000. 

The other precipitation data set, Daymet, was developed 

by Thornton and others (1997) from daily observations 

from approximately 6,000 stations in the U.S. National 

Weather Service Co-op network and the Natural Resources 

Conservation Service SNOTEL network. The interpolated 

daily meteorological observations were summarized to 

represent the period of record, 1980–97.

Soils data were obtained from the Soil Survey 

Geographic database (SSURGO), which was developed by 

the Natural Resources Conservation Service at approximately 

1:24,000 scale (U.S. Department of Agriculture, 2008). The 

SSURGO soils data include information on soil available 

water capacity, soil clay content, depth to bedrock within the 

soil horizon, soil drainage, soil hydrologic group, soil liquid 

limit, soil organic matter content, soil plasticity, soil sand 

content, and soil silt content. The SSURGO data include a 

soil factor called “the soil septic suitability factor,” which 

rates the suitability of a certain soil for use by septic-tank 

absorption fields. The suitability factor is based on soil ratings 

of saturated hydraulic conductivity, depth to water table in 

the soil layer, ponding, depth to bedrock in the soil layer, and 

flooding between soil depths of 24–72 in. Some SSURGO 

soil variables, such as soil hydrologic group and soil drainage, 

are categorical variables. To facilitate modeling with logistic 

regression, categorical variables were converted to numerical 

variables (table 1). For instance, soil drainage has seven 

categories: very poorly drained, poorly drained, somewhat 

poorly drained, moderately well-drained, well-drained, 

somewhat excessively drained, and excessively drained. Soil 

drainage was converted to a numerical rating of 1 for very 

poorly drained soil to 7 for excessively drained soil.

The depth-to-groundwater map was produced using 

a GIS. Depth-to-groundwater data were obtained from the 

USGS National Water Information System database, where 

groundwater level or depths-to-groundwater data for 436 wells 

were retrieved (U.S. Geological Survey, 2008). The data were 

filtered for water levels measured during May to September 

2001–04. Only the earliest groundwater-level measurement 

was used from wells containing multiple water-level data. 

The depth–to-groundwater map was created using a spline 

interpolator technique (Environmental Systems Research 

Institute, Inc., 2011) that includes barriers to incorporate 

the effects of linear features on groundwater levels, such 

as aquifer boundaries and rivers. The spline interpolator is 

a deterministic interpolator that creates a smooth surface 

that must pass through each measured value. It is ideal for 

smoothly varying surfaces such as a potentiometric surface. 

The output of the spline interpolator is a grid with an 

interpolated value at each cell except for cells that contain an 

actual groundwater-level measurement, which are assigned 

the measured groundwater level. Groundwater-flow system 

boundaries and the Snake River were incorporated as barriers 

to the interpolator.

The 1:24,000-scale county boundaries of Idaho are from 

the U.S. Department of Agriculture (2003), and the irrigation 

district dataset is from the Idaho Department of Water 

Resources (2009). The county boundary and irrigation district 

data sets were clipped to the model area. County boundaries 

and irrigation districts were evaluated as discrete rank 

variables for the probability models. 

Groundwater velocity and flow-direction data were 

obtained from the subregional model of the eastern Snake 

River Plain regional aquifer system (Clark and others, 2006). 
12 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

The nitrogen input from fertilizer data (October 21, 2008) 

used for this report were developed by Ruddy and 

others (2006); the farm and nonfarm estimates by Ruddy and 

others (2006) were modified by Gronberg and Spahr (U.S. 

Geological Survey, written commun., 2008). Although the 

2002 nitrogen input fertilizer data are estimated for each 

county, each county in the study area contains a significant 

fraction of rangeland. Fertilizers would not likely be applied 

to rangeland so a GIS was used to apply the nitrogen input 

to land-use types typical of fertilizer use, (for example, 

agricultural and residential land). Because it was not known 

whether the Reclamation or the NLCD data were more 

accurate, both datasets were used to assign fertilizer use 

separately. During logistic regression modeling, nitrogen input 

was simulated using both land-use data sets and the most 

significant data set was used in the final probability models 

and maps.

A dairy GIS dataset was created for this report by hand 

digitizing dairy polygons from digital aerial photography (U.S. 

Department of Agriculture, 2004). These digitized polygons 

were compared with point locations of dairies and feedlots 

obtained from the Idaho State Department of Agriculture 

(written commun., 2004) to verify the digitized polygons were 

accurately identified. The numbers of dairy cattle located at 

each polygon were estimated using 2002 data from the U.S. 

Department of Agriculture National Agricultural Statistics 

Service (U.S. Department of Agriculture, 2010). 

Delineating Areas Contributing to Wells

Previous statistically based groundwater probability 

assessments have delineated the land cover and hydrogeologic 

factors potentially affecting groundwater quality by using 

circular buffers of various radii (Rupert, 2003). These buffers 

represent a percentage of total area, an average, or the sum 

of the factors affecting groundwater quality. A 1,640-ft 

(500 m) radius has been the most common radial length for 

the circular buffer method (Eckhardt and Stackelberg, 1995; 

Nolan and others, 2002). If the groundwater flow direction is 

not known, circular buffers provide a first approximation of 

the contributing area. However, if groundwater flow directions 

are known, an upgradient, wedge-shaped buffer might provide 

a more accurate delineation of the contributing area (fig. 6). 

Lorenz and others (2003), Gurdak and Qi (2006), Gurdak 

and others (2007), and Gurdak (2008), characterized land use 

near individual wells within an upgradient, wedge-shaped 

sector and determined that the sector method presented a more 

accurate estimate of land use affecting water quality in an 

individual well than the circular-buffer method.

The sector method to delineate the contributing area of 

recharge to wells was used for this report using information 

from the subregional model along with groundwater ages 

to determine the size and orientation of the sector. The flow 

direction at each well was derived from the upper layer of 

the subregional model. The radial length of each sector was 

determined based on groundwater age and groundwater flow 

velocity. Groundwater ages were determined by Plummer and 

others (2000), who used chlorofluorocarbons, and tritium and 

helium-3 to determine apparent groundwater recharge dates 

in the study area. Plummer and others (2000) subdivided the 

groundwater quality into two groups, one located beneath 

agricultural areas and affected by anthropogenic activities, 

and the other located beneath rangeland areas (upgradient 

of the agricultural areas) that have not been affected by 

anthropogenic activities. The median age of groundwater 

beneath the agricultural areas is about 5 years. The median 

groundwater flow velocity calculated by the flow model 

is 12 ft/d. After groundwater velocity was multiplied by 

groundwater age, 21,982 ft (6,700 m) was determined to be 

a reasonable radius for the pie-shaped areas of influence. 

A radial sector length of 21,982 ft (6,700 m) was used to 

determine average depth to groundwater, groundwater 

velocity, nitrogen input from fertilizer and manure, percentage 

of land cover, and soil properties within each wedge-shaped 

area, and then was used in the logistic regression modeling.

Logistic Regression Statistical Method

Logistic regression (Hosmer and Lemeshow, 1989; Helsel 

and Hirsch, 1992; Kleinbaum, 1994) is a statistical method 

that predicts the probability of the occurrence of the target 

water-quality constituent to be greater than a certain level 

or threshold. For instance, for this report, logistic regression 

was used to predict the probability of detecting nitrate 

concentrations in groundwater greater than or equal to 2 mg/L. 

Logistic regression is similar conceptually to multiplelinear regression because relations between one dependent 

variable and several independent variables are evaluated. 

Whereas multiple-linear regression returns a continuous value 

for the dependent variable, logistic regression returns the 

probability of a positive binomial outcome (in this case, nitrate 

concentrations were or were not greater than 2 mg/L) in the 

form:

0 11 2 2

1

where

is the probability of nitrate concentrations 

being greater than 2 mg/L,

is .... ,

is logistic regression coefficient,

is value for the independent variables such as

so

x

x

i i

i

i

e P

e

P

x xx x

x

= +

β +β +β + +β

β

il drainage or depth to groundwater, and

i is the number of variables.

(1)
Methods of Investigation 13

Logistic regression calculates several statistical 

parameters that determine the predictive success of the model. 

The log-likelihood ratio measures the success of the model 

as a whole by comparing measured values with predicted 

values (Hosmer and Lemeshow, 1989, p. 13); specifically, 

the ratio tests whether model coefficients of the entire model 

are significantly different from zero. The most significant 

model is the one with the highest log-likelihood ratio, taking 

into account the number of independent variables (degrees of 

freedom) used in the model. The log-likelihood ratio follows 

a chi-squared distribution, and the computed p-value indicates 

whether model coefficients are significantly different from 

zero. Therefore, the computed p-value is the significance 

level attained by the data; the smallest p-value indicates 

the best model. A p-value of 0.05 indicates a probability 

of a correct decision (regarding null hypothesis rejection) 

of 0.95; a p-value of 0.01 indicates a probability of 0.99. 

tac12-0748_fig06

Alluvial aquifer

90-degree

wedge-shaped

buffer

Groundwater

flow path 

Circular 

buffer

Bedrock

 Direction of regional

groundwater flow

Unsaturated zone Water table

Figure 6. Conceptual diagram of wedge-shaped 90-degree buffer and circular buffer used to delineate areas 

that contribute recharge to a well (from Gurdak and Qi, 2006).
14 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

McFadden’s rho-squared (SYSTAT Software, Inc., 2004) is a 

transformation of the log-likelihood statistic and is intended 

to mimic the r-squared of linear regression. Rho-squared is 

always between zero and 1; a rho-squared approaching 1 

corresponds to results that are more significant. Rho-squared 

tends to be smaller than r-squared, so a small number does 

not necessarily imply a poor fit. P-values calculated for each 

independent variable indicate the statistical significance that 

each variable has on the overall logistic regression model. 

Independent variables were excluded from the models if their 

individual p-values were greater than 0.1. The sensitivity is 

calculated as the number of correctly predicted events (nitrate 

detections greater than 2 mg/L) divided by the total number 

of observed events (SYSTAT Software, Inc., 2004). The 

specificity is calculated as the number of correctly predicted 

reference events (no nitrate detections greater than 2 mg/L) 

divided by the total number of observed reference events. 

The total correct predictions is calculated as the number 

of correctly predicted events plus the number of correctly 

predicted reference events divided by the total number of 

all events. To verify the models, the percentage of actual 

detections was plotted with the predicted probability of 

detections by using deciles of risk, in which observations are 

partitioned into 10 groups (SYSTAT Software, Inc., 2004, 

p. II–238).

The logistic regression model was built by including 

each individual variable in the model, evaluating the resulting 

test statistics, and deciding whether to include or reject the 

variable. Model validity and accuracy were determined by 

evaluating the log-likelihood ratio, McFadden’s rho-squared, 

the model sensitivity and specificity, and the p-values 

calculated for each independent variable. During construction 

of the logistic regression model, the data were checked for 

multicolinearity by calculating the variance inflation factor. 

Multicolinearity means that at least one independent variable 

is closely related to one or more other independent variables. 

All variance inflation factors in the final models were less 

than 10, which is the level of concern indicated by Helsel and 

Hirsch (1992, p. 306).

Construction of Probability Maps

A map showing the probability of elevated concentrations 

of nitrate in groundwater was developed using the logistic 

regression methods. Before constructing the map, the GIS data 

were converted to grids with a 295-ft (90 m) cell size. The 

GIS independent variables then were converted to an average, 

a sum, or percentage of composition of the area upgradient 

within a wedge-shaped buffer with a 21,982-ft (6,700-m) 

radius, depending on the independent variable. The logistic 

regression model, similar to equation 1, was entered into a 

GIS, and a probability rating was calculated for each grid cell 

in the study area.

Groundwater Probability Modeling

Logistic regression statistical modeling techniques 

were used to develop the statistical model that estimates the 

probability of nitrate concentrations in groundwater greater 

than 2 mg/L. A concentration of 2 mg/L was selected because 

it is close to the median concentration of all nitrate samples 

used to build the logistic regression models. Using a nitrate 

concentration close to the median concentration helps to 

develop the most robust statistical model (Rupert, 2003).

Depth to groundwater, precipitation, soil available water 

capacity, soil depth to bedrock within soil horizon, and soil 

liquid limit were significant variables in the model (table 2). 

Individual p-values for each of these variables were less than 

0.05, indicating that they are significantly correlated with 

elevated concentrations of nitrate in groundwater (table 2). 

Many of the same variables are significant variables in 

nitrate probability maps in other areas of the Nation (Rupert, 

2003; Rupert and Plummer, 2009). The rho-squared value 

for the final probability model was 0.158. The total correct 

predictions of the model is 0.616 with a sensitivity of 0.677 

and a specificity of 0.527.

Probability Model Verification

To verify the model, the percentage of actual detections 

was plotted with the predicted probability of detections using 

deciles of risk (fig. 7). An r-squared value of the plot of actual 

detections with the predicted probability of detections was 

0.998, verifying it was a highly effective model.

Table 2. Logistic regression model simulation, coefficients, and 

individual p-values of independent variables significantly related 

with the probability of detecting measured nitrate concentrations 

greater than 2 milligrams per liter in groundwater of the MidSnake Region, south-central Idaho.

[Independent variables: Daymet, a model that generates daily surfaces 

of temperature, precipitation, humidity, and radiation over large regions of 

complex terrain. Probability of nitrate greater than 2 mg/L: Values not 

enclosed in parentheses are logistic regression coefficients; values enclosed in 

parentheses are individual p-values. Abbreviations: ft, foot; in., inch; in./in., 

inch per inch; mg/L, milligram per liter; mm/yr, millimeter per year]

Independent variables Probability of nitrate

greater than 2 mg/L

Depth to groundwater (ft) -8.864 (0.000)

Precipitation (mm/yr, Daymet) 0.463 (0.012)

Soil available water capacity (in./in.) 0.028 (0.000)

Soil depth to bedrock within soil 

horizon (in.)

0.014 (0.000)

Soil liquid limit (percentage of moisture 

by weight)

0.058 (0.034)
Estimating Nitrogen Input to the Subregional Model 15

Estimating Nitrogen Input to the 

Subregional Model

The SD and SDPROB nitrogen input layers were 

evaluated using the subregional model of Clark and others 

(2006). Both nitrogen input layers used the same 2002 

estimates of county-level fertilizer and cattle manure, which 

allowed the two methods to be directly compared. The primary 

differences between the two methods are that SD uses the 

2002 county-level nitrogen input to agricultural and residential 

land-use types defined by the Reclamation land-use data set, 

whereas SDPROB prorates the same nitrogen input based on 

the logistic regression model. 

Nitrogen Input by Skinner and Donato (2003)

Clark and others (2006) used the SD nitrogen input layer 

as the basis for the nitrogen source term in the subregional 

model. They applied a multiplication factor uniformly to SD 

to adjust the nitrogen input to correlate with measured nitrate 

concentrations in groundwater. The multiplication factor 

accounts for the conversion of nitrogen to nitrate in soils and 

the transport through the unsaturated zone to the aquifer. No 

rigorous calibration techniques, such as parameter estimation, 

were used to determine the multiplication factor; calibration 

was done using trial-and-error techniques. In this study, the 

multiplication factor was estimated using UCODE_2005. 

Groundwater-quality data collected during 2003–07 were used 

in the parameter estimation.

Figure 7. Deciles of risk plot showing the high 

correlation between percentage of actual nitrate 

detections and predicted probability of detecting nitrate 

concentrations groundwater, using 10 equal intervals.

tac12-0748_fig07

1:1 line

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Predicted probability of detecting nitrate concentrations 

greater than 2 milligrams per liter

Percentage of actual nitrate detections 

greater than 2 milligrams per liter

y = 0.993 - 0.003

r2

 = 0.998

Nitrogen Input Prorated Using a Nitrate 

Probability Map

The SDPROB nitrogen input layer was created by 

multiplying SD cell values by the corresponding value on the 

nitrate probability map. The probability map ratings range 

from close to zero (very low probability of nitrate greater 

than 2 mg/L in groundwater) to close to one (very high 

probability of nitrate greater than 2 mg/L in groundwater). 

When SD is multiplied by the probability, areas with low 

probability ratings have a reduced nitrogen input, and areas 

with high probability ratings maintain the nitrogen input level 

corresponding to the SD layer. Therefore, the probability map 

reduces the nitrogen input in areas with a low probability of 

detecting nitrate concentrations greater than 2 mg/L, but does 

not increase the nitrogen input in areas with a high probability 

of detection. The probability map ratings are based on 

correlations with physical factors, so SDPROB incorporates 

a more realistic estimate than SD of nitrogen flux to the water 

table. An optimal multiplication factor for SDPROB also was 

estimated using UCODE_2005. 

Nitrogen Input Optimization and Comparison 

Using UCODE_2005

The two nitrogen input layers were compared using 

the methods of Hill and Tiedeman (2007) and the software 

UCODE_2005 (Poeter and others, 2005) to determine 

which layer produces simulated nitrate concentrations from 

the subregional model that best match measured nitrate 

concentrations. These comparisons were based on overall 

model-fit statistics and measured, simulated, and residual 

point statistics determined at measured nitrate concentration 

locations.

In this study, UCODE_2005 was used to estimate 

the optimal multiplication factor for the SD and SDPROB 

nitrogen input layers by minimizing the sum of squared 

differences between simulated nitrate concentrations in 

layer one of the models and corresponding measured nitrate 

concentrations. The UCODE_2005 calibration data used 

the 525 nitrate concentration values collected from wells 

and springs (see section “Groundwater-Quality Data”). The 

525 nitrate concentration values represented 320 individual 

calibration model cells because some model cells contained 

more than one nitrate concentration sample. In instances where 

a model cell contained more than one nitrate concentration 

sample, the mean of the nitrate concentrations was used for 

calibration of that cell. UCODE_2005 calculates overall 

model-fit statistics that can be used to evaluate individual 

model performance. These model-fit statistics were used to 

determine which nitrogen input layer, SD or SDPROB, had the 
16 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

best fit. The model with the best fit was then selected for use 

in further analysis. It was beyond the scope of this report to 

evaluate the flow or transport performance of the model; only 

nitrogen input to the model and the resulting simulated nitrate 

concentrations were evaluated.

UCODE_2005 model files were created according to 

the methods described in Poeter and others, (2005), and Hill 

and Tiedeman (2007). Because the same single parameter 

(nitrogen input multiplication factor) was estimated in each 

model, comparison was relatively straightforward using 

model-fit statistics. UCODE_2005 requires weighting 

observation data, which also was simplified because only one 

type of observation data (nitrate concentration) was used, and 

all weights were set to 1.0 (Hill and Tiedeman 2007, p. 294). 

After the UCODE_2005 models were established with SD 

and SDPROB, model-fit-independent statistics were analyzed 

to identify the effect that measured nitrate concentrations had 

on the parameter estimation. These statistics identified two 

observations that had an unusually high effect on parameter 

estimation. Upon further review, these two observations were 

determined to represent individual point sources of nitrate 

that were surrounded by lower areal nitrate concentrations. 

These observations were omitted from subsequent model runs 

because they were outliers. Because the model is not capable 

of single-cell precision (see section, “Model Limitations”), 

these outliers weaken model performance.

All UCODE_2005 model runs used three different 

starting values to determine the optimal multiplication factor 

of either SD or SDPROB: (1) a low-end value of 1, (2) an 

estimated value of 225 (the multiplication factor used in 

the Clark and others [2006]), and (3) a high-end value of 

10,000. Using different starting values establishes model 

uniqueness. Each UCODE_2005 model calculated the same 

resulting parameter value (232 for SD, and 392 for SDPROB), 

regardless of the starting value, indicating that a unique 

solution for the nitrogen input layer multiplication factor is 

being calculated. 

Model linearity, which can affect parameter confidence 

intervals, was estimated using the Beale’s measure that 

indicates a linear model for values less than 0.26. All models 

had a Beale’s measure four orders of magnitude less than the 

cutoff value indicating that all the models are linear.

The overall model-fit statistics measure how well a model 

re-creates the measured observations. The overall model-fit 

statistics (table 3) indicate that SDPROB more accurately 

represents nitrogen input than SD; hence, SDPROB re-creates 

a better match than SD with the measured nitrate observations. 

The composite scaled sensitivity (CSS) is used comparatively 

between models; larger CSS values indicate that the 

observations provide a better estimated model parameter. In 

this case, SDPROB (CSS=1.97) provides more information 

in estimating the model parameter than SD (CSS=1.90). 

The calculated error variance (CEV) and the standard error 

(SE) are indicators of the overall magnitude of residuals 

(measured nitrate concentration minus the simulated nitrate 

concentration), with the SE as the square root of the CEV. As 

with the overall model fit statistics, smaller values of the CEV 

and SE indicate a closer fit between simulated values and the 

observations. The smaller CEV and SE values for SDPROB 

indicate that SDPROB better estimates the measured nitrate 

concentrations than does SD (table 3). However, because 

the CEV and SE values for each model occur within the 

confidence interval of the other model, there is little statistical 

difference between the two nitrogen input models.

Residual analysis indicates less bias in the SDPROB 

model than in the SD model. Ideally, plots of simulated nitrate 

concentrations show a random scatter about the zero residual 

value. Any variation from this pattern indicates model bias. 

Simulated nitrate concentrations plotted with residuals for SD 

and SDPROB, with linear trend lines are shown in figure 8. 

Although both plots show a slight bias with increasing 

simulated nitrate concentrations, the SD model shows a more 

pronounced bias. This greater bias precludes the use of the 

SD nitrogen input model from further investigation. Maps 

showing the spatial distribution of residuals were analyzed; 

however, no spatial pattern was discernible. 

Table 3. Comparison of the effectiveness of two nitrogen input 

layers in the subregional model using UCODE_2005 statistical 

results.

Model statistic

Nitrogen input updated from

Skinner and 

Donato (2003) 

(SD)

Skinner and Donato 

(2003) and prorated 

with a probability 

map (SDPROB)

UCODE optimized nitrogen 

input multiplication factor

232 392

Average weighted residual 0.34 0.40

Composite scaled sensitivity 

(CSS)

1.90 1.97

Least-squares objective 

function

1,187.60 1,098.70

Calculated error variance 

(CEV)

3.72 3.44

CEV 95-percent confidence 

interval

3.21–4.36 2.97–4.04

Standard Error (SE) 1.93 1.86

SE 95-percent confidence 

interval

1.79–2.09 1.72–2.01

Maximum likelihood 

objective function

419.64 394.75
Estimating Nitrogen Input to the Subregional Model 17

tac12-0748_fig08

-6 

-4 

-2 

0 

2 

4 

6 

8 

10 

-6 

-4 

-2 

0 

2 

4 

6 

8 

10 

0 1 2 3 4 5 6 7 8

Nitrate concentration, in milligrams per liter

Simulated nitrate concentration, in milligrams per liter

A. Nitrogen input model updated from Skinner and Donato (2003)

B. Nitrogen input model updated from Skinner and Donato (2003) and prorated with the probability model

Figure 8. Simulated nitrate concentrations and corresponding model residuals with linear regression 

trend lines indicating model bias in (A) nitrogen input model updated from Skinner and Donato (2003) (SD) 

and (B) nitrogen input model updated from Skinner and Donato (2003) and prorated with a probability model 

(SDPROB).
18 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

Nitrate Transport in the Subregional 

Model and Particle-Tracking Results

Transport modeling and particle tracking were used to 

simulate the movement, fate, and concentrations of nitrate in 

groundwater in the mid-Snake region of south-central Idaho 

under different nitrogen input scenarios. The nitrogen input 

layer, SDPROB, was used to estimate the spatial distribution 

and relative amount of nitrogen input to the water table, and 

the subregional model then simulated transport of nitrate 

through the aquifer system to the final discharge locations 

in the Thousand Springs area. The solute transport model 

was a useful tool for simulating present and future nitrate 

concentrations in the aquifer system. Particle tracking was a 

useful tool for visualizing the three-dimensional flow of nitrate 

through the aquifer system.

Transport Modeling Results

Nitrogen input from cattle manure has increased 

substantially in parts of the eastern Snake River Plain because 

the number of dairy cattle has increased five-fold from 1987 

through 2007 (fig. 2). Fertilizer use also has increased during 

the same period as the result of crops such as feed corn grown 

to support the dairy industry (fig. 9; Ruddy and others, 2006; 

U.S. Department of Agriculture, 2010). The largest sources of 

nitrate in groundwater are chemical fertilizer and dairy cow 

manure applied to the land surface. Lesser contributions are 

from human waste, beef cow manure, decomposition of soil 

organic material, and atmospheric deposition (Rupert, 1996; 

Skinner and Donato, 2003). 

Nitrate transport was simulated for 50 years starting in 

2008, with three fertilizer and dairy manure nitrogen input 

scenarios: 

• Scenario 1 estimates nitrogen input from fertilizer and 

dairy cows to 2008 levels based on U.S. Department 

of Agriculture county-level census data, and holds 

nitrogen input fixed at these levels to simulate 

the effects of current nitrogen input on nitrate 

concentrations in groundwater over time. 

• Scenario 2 uses a linear projection of nitrogen input 

based on the previous 10 years (1998–2008) of 

fertilizer and dairy manure input for the model area 

(fig. 9) to project nitrogen input 20 years into the 

future. After 2028, nitrogen input is fixed at 2028 

levels. This scenario examines long-term effects using 

best estimates of projected nitrogen input.

• Scenario 3 has zero local anthropogenic nitrogen input; 

all nitrogen input from fertilizer and dairy manure 

is halted completely, with only nitrogen input from 

precipitation remaining. This scenario is not likely to 

occur, but provides an estimate of how long it would 

take nitrate in groundwater to return to background 

concentrations—the “flushing time” of the system.

Scenario 1 simulates the effects of constant nitrogen 

input set at 2008 levels, which is the date of the most recent 

agricultural census. Although nitrogen input is held constant, 

nitrate concentrations in the groundwater continue to increase 

from 10 to 50 years after 2008, depending on the location in 

the model area (fig. 10). 

Scenario 2 uses the previous 10 years of county fertilizer 

and dairy cattle nitrogen input to project linearly (fig. 9) 

the change in nitrogen input through the year 2028, after 

which the nitrogen input is constant. Although nitrogen input 

is held constant after the year 2028, groundwater nitrate 

concentrations continue to increase from 10 to 50 years into 

the future depending on the location in the model area (figs. 10

and 11).

Scenario 3 involves the cessation of all local 

anthropogenic nitrogen input to the system. Although stopping 

all local anthropogenic input is not a realistic scenario, it 

provides insight to how long it would take the aquifer system 

to discharge the residual nitrate to the Thousand Springs 

region and to return to background conditions. The models 

indicate that it will take from 20 to 50 years for the aquifer 

system to return to background conditions (fig. 11). The length 

of time depends on the location in the model area. 

Nitrogen from fertilizer

Nitrogen from dairy 

cow manure

EXPLANATION

Nitrogen, in thousands of tons

Year

Estimated nitrogen

input

Projected nitrogen

for modeling

Fixed nitrogen

for modeling

0

10

20

30

40

50

60

70

80

1990 2000 2010 2020 2030 2040

Figure 9. Nitrogen trends based on data 

collected during 1999–2008, south-central Idaho. 

Simulated nitrogen inputs to the subregional 

model were projected 20 years into the future 

from 2008, and then held constant from 2028 

onward. Nitrogen input from dairy cows is 

projected to approach nitrogen input from 

fertilizer in about 20 years if trends continue as 

projected.
Nitrate Transport in the Subregional Model and Particle-Tracking Results 19tac12-0748_fig10

TWIN FALLS

Groundwater nitrate concentration,

in milligrams per liter

B. Estimated nitrogen input, probability weighting model, and modeled nitrate concentrations for 2048

0 10 20 30 40 MILES

0 10 20 30 40 KILOMETERS

Probability of detecting nitrate concentrations greater 

than 2 milligrams per liter in groundwater

0 1 0.50 0.75 0.25

Nitrogen input, in pounds per acre per year

0 800 200 400 600

A. Estimated nitrogen input, probability weighting model, and simulated nitrate concentration for 2008

EXPLANATION

< 11 – 2> 2 – 3> 3 – 4> 4 – 5> 5 – 6

> 6 – 7> 7 – 8> 8 – 9> 9 – 10> 10 – 1111 – 12

BLAINE

CASSIA

LINCOLN

POWER

GOODING

JEROME MINIDOKA

Snake River

County boundaries from U.S. Department of Agriculture, 2004, 1:24,000 Universal Transverse Mercator, Zone 11 North, North American Datum of 1983

Figure 10. Simulated nitrate concentrations in groundwater (right) from linearly projected nitrogen input (scenario 2) prorated by the probability map (center), eastern Snake 

River Plain aquifer, Idaho, 2008 and 2048. Simulated nitrate concentrations indicate that they are not solely dependent on nitrogen input (left). Nitrate concentrations are low north of the Snake River in western Jerome County and southern Gooding County despite high nitrogen input rates there; upward convergence of good-quality water dilutes nitrate-laden irrigation recharge in those areas. In contrast, nitrate concentrations are high on the Jerome–Minidoka County border and south of the Snake River in northern Cassia County and western Twin Falls County because they do not benefit from the dilution effects of low nitrate groundwater upwelling from deeper parts of the aquifer.
20 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

tac12-0748_fig11

EXPLANATION 

2008 

2028 

2048 

2008 

2028 

2048 

0 

1 

2 

3 

4 

5 

6 

7 

8 

2000 2020 2040 2060 2000 2020 2040 2060 2000 2020 2040 2060 

Nitrate concentration,

2008 

2028 

2048 

in milligrams per liter

Year

Scenario 1: Nitrogen input 

fixed at 2008 levels

Scenario 2: Nitrogen input 

increases at current rates 

until 2028, and then 

held constant

Scenario 3: All local agricultural nitrogen 

input is stopped after 2008 

(input from precipitation only) 

Time-series plots of simulated groundwater nitrate concentrations for model scenarios 1, 2, and 3

Map of simulated groundwater nitrate concentrations in 2048, 

model scenario 2.

LINCOLN

BLAINE

JEROME

MINIDOKA

POWER

GOODING

CASSIA

TWIN FALLS

0 5 10 15 20 MILES

0 5 10 15 20 KILOMETERS

114°W

113°30'W

113°W

114°30'W

115°W

43°N

42°30'N

EXPLANATION 43°30'N

Less than 2

2 to 4

Greater than 4 to 6

Greater than 6 to 8

8 to 12

Simulated groundwater nitrate 

concentrations in 2048, model

scenario 2, in milligrams per liter

Snake River

Figure 11. Simulated nitrate concentrations in 2048 and time-series plots for three locations in the Snake River Plain aquifer, southcentral Idaho. Estimated nitrate concentrations were as much as 11 milligrams per liter, using projected nitrogen input from scenario 

2. Concentrations increase for as many as 50 years even though nitrogen input is held constant after 2028. With nitrogen input held 

constant at 2008 levels (scenario 1), the time response is similar, but concentrations level out at lower values. If local anthropogenic 

nitrogen input is halted in the model (scenario 3; improbable, but useful for judging system response time), it also takes much of the 21st 

century for nitrate to decline to a background concentration of less than 1 milligram per liter.
Nitrate Transport in the Subregional Model and Particle-Tracking Results 21

Simulation estimations indicate that it will take decades 

for nitrate concentrations from groundwater in the Snake 

River Plain to respond to the full effects of changes in nitrogen 

input (fig. 11). Even if nitrogen input were held constant at 

2008 values (scenario 1), it would take about 40 years (around 

2048) for nitrate concentrations to begin to level off. It takes 

about 50 years for concentrations to stabilize for the projected 

and 2008-fixed nitrogen input scenarios 1 and 2, with scenario 

2 stabilizing at higher values corresponding to the larger 

quantities of nitrogen in the projected scenario compared 

to 2008 levels. North of the Snake River in south Gooding 

County (upper middle graph), nitrate concentrations are kept 

low by upward convergence of low-nitrate concentration of 

regional groundwater that mixes with shallow, nitrate-laden 

groundwater.

Model simulation results for current (2008) conditions 

and for future (2048) groundwater nitrate conditions are 

shown in figure 11. In both cases, nitrate concentrations 

are low (less than 2 milligrams per liter) north of the Snake 

River in western Jerome County and southern Gooding 

County, despite high nitrogen input rates in those areas. 

Simulation results indicate that the nitrogen input layer 

was not necessarily the overall driving factor in resulting 

nitrate concentrations; rather, aquifer thinning near its 

terminus and the resulting upwelling of low-nitrate regional 

groundwater (section “Particle-Tracking Simulations”) help 

explain the long-standing dilemma of how groundwater 

nitrate concentrations can remain low in much of Jerome and 

Gooding Counties despite large quantities of nitrogen input 

from fertilizer and cattle manure (fig. 10).

In contrast, simulated nitrate concentrations are high 

south of the Snake River in Cassia County and in western 

Twin Falls County. Although the aquifer is thin in these areas, 

there is little to no upwelling of good-quality groundwater 

from deeper parts of the aquifer, so groundwater quality is 

influenced by recharge of nitrate-laden irrigation water. Nitrate 

concentrations south of the Snake River are forecast to rise 

above the U.S. Environmental Protection Agency maximum 

contaminant level for drinking water of 10 mg/L by 2048 

under the nitrogen-input scenario 2. Nitrate concentrations 

in groundwater throughout the model area were simulated to 

increase by as much as 2 to 4 mg/L under projected nitrogen 

input estimates for scenario 2, with some areas reaching or 

exceeding 10 mg/L (figs. 10 and 11).

Particle-Tracking Simulations

Forward and backward particle-tracking simulations 

using the subregional model (Clark and others, 2006) were 

performed using MODPATH (Pollock, 1994) to visualize 

groundwater flow directions and velocities. For the first 

simulation (fig. 12), one particle was placed in the middle of 

each grid cell in layer 1 of column 96 (easternmost column of 

the model), and MODPATH was run forward to completion. 

The particles moved roughly east to west across the Snake 

River Plain until they discharged into the Snake River in 

the Thousand Springs area. Much of groundwater flowing 

from the east edge of the model area primarily runs beneath 

rangeland (fig. 12), and does not flow beneath agricultural 

lands until it reaches Jerome and Gooding Counties on the 

western part of the model area. Nitrate concentrations in 

groundwater are at background concentrations of less than 

1 mg/L until they flow beneath the agricultural areas of Jerome 

and Gooding Counties (Rupert, 1997, fig. 21). 

Elevated nitrate concentrations have been measured in 

groundwater in the local alluvial aquifer and in groundwater 

north of the local perched aquifer in the southern part of 

Minidoka County (Young and others, 1987b; Rupert, 1994; 

Crockett, 1995; Rupert and others, 1996; Rupert, 1997). 

Groundwater with elevated nitrate concentrations could 

be leaching from the local alluvial aquifer vertically to the 

regional basalt aquifer. Rupert (1997, p. 8) reported that 

groundwater in the local alluvial aquifer generally flows 

northward and westward, indicating that groundwater also 

could be “spilling” off the sides of the local perched aquifer 

on the north and west margins downward to the regional basalt 

aquifer below. To track the flow direction of the groundwater 

with elevated nitrate concentrations, one particle was placed 

in the top of each cell located beneath the local alluvial aquifer 

(fig. 13), and the particles were tracked until they exited 

from the subregional model as spring discharge to the Snake 

River. Most groundwater exited the subregional model at Blue 

Lakes Spring (see cover photograph). These pathlines indicate 

that the groundwater originates and flows beneath intensely 

cultivated cropland, and the elevated nitrate concentrations 

reflect large nitrogen input from agricultural activities. This 

increase in nitrate concentrations is due to fertilizer-enriched 

groundwater recycled through multiple pumping and irrigation 

cycles (Rupert, 1997).

Most groundwater in the eastern Snake River Plain 

discharges to the Snake River from large springs between 

Milner Dam and King Hill. Some of these springs are large 

enough to generate hydropower and to support large fish 

hatcheries. Combined discharge in this reach was about 

6,000 ft3/s during 1980 (Kjelstrom, 1992, p. 2). Backward 

particle tracking from these large springs was used to observe 

groundwater flow paths in relation to the overlying land 

use and recharge locations. Backward particle tracking was 

calculated by placing one particle in the middle of each cell 

where a spring was located and letting the model run for 

100 years (fig. 14). Square, black boxes are shown on each 

particle pathline at 1-year intervals to indicate groundwater 

flow velocities, which are reflected in the distance between 

the boxes (more distance means greater groundwater 

velocity). Pathlines that underlie the greatest proportion of 

irrigated agricultural lands correspond with elevated nitrate 

concentrations measured by Young and others (1987a, 1987b), 

Rupert (1994), Rupert and others (1996), and Rupert (1997). 
22 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

tac12-0748_fig12

0 20 MILES

0 20 KILOMETERS

County boundaries from U.S. Department of Agriculture, 2004, 1:24,000

Universal Transverse Mercator, Zone 11 North, North American Datum of 1983, 

North American Vertical Datum of 1988.

4,000

3,800

3,800

3,600

3,400

3,200

4,000

4,100

LINCOLN

MINIDOKA

GOODING

TWIN FALLS

CASSIA

POWER

BUTTE

BLAINE

Snake River

Snake River

Milner Dam

Thousand 

Springs

area

Snake River

Agricultural land

Rangeland

Riparian land

Urban lands

Study area

Water-level contour, 

50-foot contour interval 

Particle pathline

EXPLANATION

3,800

114°W

114°30'W

115°W

43°N

42°30'N

43°30'N

Figure 12. Forward particle-tracking simulation starting in the easternmost column of model cells of the 

subregional model, south-central Idaho. Particles began in the middle of layer 1, column 96, the easternmost 

column of model cells. Particles were tracked until exiting the model as spring discharge to the Snake River in 

the Thousand Springs area.
Nitrate Transport in the Subregional Model and Particle-Tracking Results 23

tac12-0748_fig13

3800

Snake River

Milner Dam

County boundaries from U.S. Department of Agriculture, 2004, 1:24,000

Universal Transverse Mercator, Zone 11 North, North American Datum of 1983, 

North American Vertical Datum of 1988.

3,800

3,600

3,400

3,200

4,000

4,100

3,800

3,600

3,400

3,200

4,000

4,100

LINCOLN

MINIDOKA

GOODING

TWIN FALLS

CASSIA

POWER

BUTTE

BLAINE

Thousand 

Springs

area

Blue Lakes Spring

Snake River

Snake River

114°W

114°30'W

115°W

43°N

42°30'N

43°30'N

0 20 MILES

0 20 KILOMETERS

Agricultural land

Rangeland

Riparian land

Urban lands

Study area

Local perched aquifer

Water-level contour, 

50-foot contour interval 

Particle pathline

EXPLANATION

3,800

Figure 13. Forward particle-tracking simulation starting in the local perched aquifer of the subregional 

model, south-central Idaho. Particles were tracked generally northward and westward until they exited the 

model as spring discharge to the Snake River from Blue Lakes Springs.
24 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

tac12-0748_fig14

114°W

114°30'W

115°W

43°N

42°30'N

43°30'N

0 20 MILES

0 20 KILOMETERS

LINCOLN

MINIDOKA

GOODING

TWIN FALLS

CASSIA

POWER

BUTTE

BLAINE

Thousand 

Springs

area

King 

Hill

Snake River

Milner Dam

Snake River

Snake River

Agricultural land

Rangeland

Riparian land

Urban lands

Study area

Particle pathline—Square 

indicates particle at 

1-year increment

Spring

EXPLANATION

County boundaries from U.S. Department of Agriculture, 2004, 1:24,000

Universal Transverse Mercator, Zone 11 North,

North American Datum of 1983

Figure 14. Backward particle tracking simulation in the subregional model, south-central Idaho. Particles 

began in each large spring in the Thousand Springs area and traveled backward for 100 years.

This pattern is obvious particularly in nitrate concentrations 

downgradient of agricultural areas in southwest Minidoka 

County, but much less apparent downgradient of agricultural 

areas in southern Jerome and Gooding Counties. The 

difference between the amounts of increased nitrate cannot be 

explained solely by the difference in nitrogen input; in fact, 

nitrogen input in the Jerome County and Gooding County 

agricultural areas is estimated to be much greater than input 

in the other areas because of more dairies and associated 

agriculture and, therefore, greater nitrogen input (fig. 9).

The geometry of the aquifer and the pattern of regional 

groundwater flow through the aquifer greatly influences 

nitrate concentrations (fig. 15). The eastern Snake River Plain 

aquifer is estimated to reach a maximum saturated thickness 

of about 4,000 ft in the central part of the plain, from which 

the aquifer thins to the west. Thinning of the aquifer forces 

upward convergence of regional groundwater, much of it 

having originated as good-quality, mountain-front recharge 

along the north margin of the plain or as recharge through 

undeveloped rangeland. The flow convergence also coincides 

with the observed steepening of the water table, as the entire 

aquifer flow is funneled through an increasingly thinner 

aquifer toward discharge areas along the Snake River, most 

notably in the Thousand Springs area. Good-quality regional 

water converges upward and mixes with the nitrate-laden 

groundwater in the uppermost parts of the aquifer (fig. 15), 

and then discharges to the Snake River in the Thousand 

Springs area. This hypothesis of flow-system control on water 

quality is supported by measured nitrate concentrations in 

wells located near agricultural areas and the subregional model 

simulation results.
Nitrate Transport in the Subregional Model and Particle-Tracking Results 25

tac12-0748_fig15

Regional groundwater flow

Regional groundwater flow

FEET

County boundaries from U.S. Department of Agriculture, 2004, 1:24,000

Universal Transverse Mercator, Zone 11 North, North American Datum of 1983, 

North American Vertical Datum of 1988

Lincoln

Blaine

Jerome

Minidoka

Power

Cassia

Twin Falls

115 ̊W

43 ̊N

A’

A

Line of cross section

Geology modified from 

Garabedian, 1992

7,000

6,000

5,000

4,000

3,000

2,000

1,000

Sea level

-1,000

Recharge through agricultural lands, largely from 

irrigation of fields and seepage losses from irrigation 

ditches and canals

Deep regional groundwater converges upward 

and mixes with contaminated irrigation recharge 

before discharging in the Thousand Springs area 

along the Snake River

Recharge through rangeland 

and from stream and river 

infiltration to the Plain 

farther east

A A’

114 ̊30’W

114 ̊W

113 ̊30’N

42 ̊30’N

Thousand Springs area

Gooding

0 10 20 30 40 MILES

0 10 20 30 40 KILOMETERS

EXPLANATION

Layers 1 and 2

Layer 3

Layer 4

Model layer active extent

Water table

Recharge through agricultural lands

Recharge through rangeland

Elevation in feet above North American Vertical Datum of 1988

Horizontal distance not to scale

Figure 15. The eastern Snake River Plain aquifer thins to the west toward the Thousand Springs area, south-central 

Idaho. Good-quality regional groundwater converges upward and mixes with nitrate-laden irrigation recharge from 

overlying agricultural lands in west Jerome and south Gooding Counties, where nitrate concentrations in water samples 

collected from wells are low despite large amounts of nitrogen input from fertilizers and cattle manure.
26 Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, South-Central Idaho

Model Use, Guidance, and Limitations

The subregional model used for this study is intended 

for large-scale, nitrate-transport simulations. The model lacks 

sufficient details for small-scale applications such as modeling 

the effects on individual farms or dairies. The accuracy of the 

model is limited because of the simplifying assumptions used, 

model parameter uncertainties, and the relatively large size 

of the grid cells. These limitations must be understood and 

considered when applying the model to resource-management 

issues. The nitrogen-input data from cattle manure and 

fertilizer are general values representing large areas (counties). 

Small spatial changes in land use or nitrogen input cannot be 

simulated effectively with the subregional model; however, 

the model is well suited for assessing county-based nutrient 

management plans. 

The greatest limitations in using the subregional model 

for this study are the assumptions inherent in the model. 

These assumptions are carried forward cumulatively from 

each model version. The model assumption that likely affects 

model accuracy the most is the early timeframe. Since 

the development of the eastern Snake River Plain model 

(Garabedian, 1992), the flow system has undergone a decrease 

in recharge from an extended drought, changes in land use, 

and more efficient irrigation methods. A decrease in flow 

through the system would result in longer travel times and 

altered mixing rates between local and regional groundwater 

flow.

Summary

Groundwater provides most of the drinking water in 

Idaho, and groundwater quality is an ongoing concern because 

of rising nitrate concentrations in an increasing number of 

wells. The predominant sources of nitrate throughout much 

of the State are inorganic fertilizer, cattle manure, and legume 

crops. Increasing nitrate concentrations are of particular 

concern in the mid-Snake region of south-central Idaho 

because the region is experiencing substantial growth in 

some agricultural industries associated with the discharge of 

nitrogen to the environment. Along with the increase in dairy 

cattle, fertilizer use also has increased during the same time 

as the result of an increase in crops such as feed corn grown 

to support the dairy industry. The current and future effect of 

expanded agriculture on groundwater quality in this area is 

not fully understood. In response to these concerns and needs, 

the U.S. Geological Survey conducted this investigation in the 

mid-Snake region of south-central Idaho.

To assess current and future nitrate concentrations in 

groundwater in the Mid-Snake region, nitrate concentrations 

in groundwater were simulated using an existing threedimensional subregional groundwater transport model. A 

statistical model that estimated the probability of groundwater 

contamination from elevated nitrate concentrations was 

developed using logistic regression techniques and was 

incorporated into the subregional model to assist nitrogen 

input to the subregional model. The subregional model 

simulated the change of nitrate concentrations in groundwater 

over time in response to three nitrogen input scenarios: 

(1) nitrogen input fixed at 2008 levels; (2) nitrogen input 

increased from 2008 to 2028 using the same rate of increase 

as the average rate of increase observed during the previous 

10 years (1998–2008); after 2028, nitrogen input was fixed at 

2028 levels; and (3) nitrogen input related to local agriculture 

completely halted, with only nitrogen input from precipitation 

remaining.

Geographic Information System data on nitrate 

concentrations in groundwater, depth to groundwater, 

groundwater velocity, total nitrogen input from dairies, total 

nitrogen input from fertilizer, percent land use, precipitation, 

and soils were compiled to provide data for the groundwater 

nitrate probability model. Upgradient 90-degree, wedgeshaped zones of influence were calculated for each well where 

groundwater-quality data were collected to determine either 

the sum or the average for nitrogen input, land use, and soils 

properties upgradient of each sampled well. The optimum size 

for each wedge-shaped zone was calculated using groundwater 

age and groundwater flow velocity. The logistic model had 

statistically significant independent variables of depth to 

groundwater, soil available water capacity, soil liquid limit, 

soil depth to bedrock within soil horizon, and precipitation. 

The logistic regression model was verified by plotting the 

percentage of actual detections with the predicted probability 

of detections using deciles of risk. An R-squared value of 

the plot of actual detections with the predicted probability of 

detections was 0.998, verifying the high effectiveness of the 

model. A new method was used to determine nitrogen input to 

a subregional groundwater transport model. A map predicting 

the probability of nitrate concentrations greater than 2 mg/L in 

groundwater was used to prorate estimated nitrogen input to 

the subregional model. 

Two nitrogen input layers for the subregional model are 

evaluated in this report. The first nitrogen input layer is the 

one used by Skinner and Donato (2003). The second nitrogen 

input layer is the same as the Skinner and Donato (2003) input 

layer, but modified using a map of the probability of elevated 

nitrate concentrations to prorate nitrogen input to groundwater. 

The probability map ratings range from close to zero (very 

low probability of nitrate concentrations greater than 2 mg/L 

in groundwater) to close to one (very high probability of 

nitrate concentrations greater than 2 mg/L in groundwater). 

By multiplying the Skinner and Donato nitrogen input by 

the probability ratings, areas with low probability ratings 

would have a reduced nitrogen input, and areas with high 

probability ratings would maintain the level of nitrogen input 

corresponding to the Skinner and Donato (2003) nitrogen 

input layer. Because the probability map ratings are based 

on correlations with physical factors, the probability-mapmodified, nitrogen-input layer incorporates a much more 

realistic estimate of nitrogen flux to the water table than 

Skinner and Donato (2003).
References Cited 27

The effectiveness of the two nitrogen input layers was 

tested by estimating a multiplication factor, applied to each 

input layer, and comparing overall model fit statistics. This 

analysis indicated that nitrogen input estimated using the 

probability map more accurately represents the measured 

nitrate concentrations than the Skinner and Donato (2003) 

nitrogen input layer. Residual analysis indicates less bias in 

the probability-modified subregional model than without the 

probability modification, further proving it is a more effective 

model than the Skinner and Donato (2003) nitrogen input 

layer. 

The nitrate-transport model simulation results indicate 

that, even when the nitrogen input is held constant, nitrate 

concentrations in the groundwater continue to increase for 10 

to 50 years after 2008, depending on the location in the model 

area. It takes decades for nitrate concentrations in groundwater 

in the mid-Snake region to respond completely to changes in 

nitrogen input. If nitrogen input to the mid-Snake region keeps 

increasing as it has for the past 10 years, some areas in Cassia, 

Minidoka, and Twin Falls Counties are estimated to exceed the 

U.S. Environmental Protection Agency maximum contaminant 

level for drinking water of 10 milligrams per liter. North of 

the Snake River in southern Gooding and western Jerome 

Counties, nitrate concentrations are low because the shallow, 

nitrate-laden groundwater mixes with low-nitrate groundwater 

that is upwelling from deeper parts of the aquifer. Nitrate 

concentrations in groundwater are simulated to increase by 

as much as 2 to 4 milligrams per liter in many areas, with 

nitrate concentrations in some areas reaching or exceeding 

10 milligrams per liter.

Particle-tracking simulations show that groundwater 

pathlines underlying the greatest proportion of irrigated 

agricultural lands correspond with elevated nitrate 

concentrations. This pattern is obvious particularly in nitrate 

concentrations downgradient of agricultural areas in southwest 

Minidoka County, but much less apparent downgradient of 

southern Jerome and Gooding County agricultural areas. The 

difference between the quantities of increased nitrate cannot 

be explained solely by difference in nitrogen input; in fact, 

nitrogen input in the Jerome and Gooding County agricultural 

areas is estimated to be greater than that in the other areas 

because more dairies and associated agriculture are in the area 

and, therefore, greater nitrogen input. The geometry of the 

aquifer and the pattern of regional groundwater flow through 

the aquifer greatly influences nitrate concentrations. Thinning 

of the aquifer forces upward convergence of regional, goodquality groundwater that also coincides with the observed 

steepening of the water table, as the entire aquifer flow is 

funneled through an increasingly thinner aquifer toward 

discharge areas along the Snake River in the Thousand Springs 

area. Good-quality regional water converges upward and 

mixes with the nitrate-laden groundwater in the uppermost 

parts of the aquifer, resulting in lowered nitrate concentrations 

in groundwater. 

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Publishing support provided by the U.S. Geological Survey

Publishing Network, Tacoma Publishing Service Center

For more information concerning the research in this report, contact the

Director, Idaho Water Science Center

U.S. Geological Survey

230 Collins Road

Boise, Idaho 83702

http://id.water.usgs.gov
Skinner and Rupert— Numerical Model Simulations of Nitrate Concentrations in Groundwater, Mid-Snake Region, Idaho—Scientific Investigations Report 2012–5237