National Water-Quality Assessment Program

Simulations of Groundwater Flow, Transport, and Age in 

Albuquerque, New Mexico, for a Study of Transport of 

Anthropogenic and Natural Contaminants (TANC) to 

Public-Supply Wells

Supply

well

Recharge

Discharge

Urban area

Generalized

groundwater

 flow

Water table

Coarser-grained

unconsolidated

alluvium

Finer-grained

unconsolidated

alluvium

FAULT

Bedrock

Mountain

front

RIO GRANDE AQUIFER SYSTEM

Floodplain 

deposits

Rio

Grande

Not to scale

Scientific Investigations Report 2012–5242

U.S. Department of the Interior

U.S. Geological Survey

Simulations of Groundwater Flow, 

Transport, and Age in Albuquerque, 

New Mexico, for a Study of Transport of 

Anthropogenic and Natural Contaminants 

(TANC) to Public-Supply Wells

By Charles E. Heywood 

National Water-Quality Assessment Program

Scientific Investigations Report 2012–5242

U.S. Department of the Interior

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

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

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

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Suggested citation:

Heywood, C.E., 2013, Simulations of groundwater flow, transport, and age in Albuquerque, New Mexico, for a study 

of transport of anthropogenic and natural contaminants (TANC) to public-supply wells: U.S. Geological Survey 

Scientific Investigations Report 2012–5242, 51 p.

ISBN 978-1-4113-3583-7
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 ).

In the second decade of the Program (2001–12), a major focus is on regional assessments of 

water-quality conditions and trends. These regional assessments are based on major river 

basins and principal aquifers, which encompass larger regions of the country than the Study 

Units. Regional assessments extend the findings in the Study Units by filling critical gaps in 

characterizing the quality of surface water and groundwater, and by determining water-quality 

status and trends at sites that have been consistently monitored for more than a decade. In 

addition, the regional assessments continue to build an understanding of how natural features 

and human activities affect water quality. Many of the regional assessments employ modeling 

and other scientific tools, developed on the basis of data collected at individual sites, to help 

extend knowledge of water quality to unmonitored, yet comparable areas within the regions. 

The models thereby enhance the value of our existing data and our understanding of the hydrologic system. In addition, the models are useful in evaluating various resource-management 

scenarios and in predicting how our actions, such as reducing or managing nonpoint and point 

sources of contamination, land conversion, and altering flow and (or) pumping regimes, are likely 

to affect water conditions within a region.

Other activities planned during the second decade include continuing national syntheses of 

information on pesticides, volatile organic compounds (VOCs), nutrients, trace elements, and 

aquatic ecology; and continuing national topical studies on the fate of agricultural chemicals, 

effects of urbanization on stream ecosystems, bioaccumulation of mercury in stream ecosystems, effects of nutrient enrichment on stream ecosystems, and transport of contaminants to 

public-supply wells.
iv

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 waterresource 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
v

Contents

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

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

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

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

Description of Local-Scale Model Area ...........................................................................................4

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

Hydrogeology of the Local-Scale Model Area .........................................................................................6

Aquifer-Pumping Test ..........................................................................................................................6

Characterization of Tertiary Alluvium................................................................................................6

Methods for Simulation of Groundwater Flow and Solute Concentrations.........................................9

Coupling of Regional and Local-Scale Groundwater Flow Models .............................................9

Modification of the Regional Groundwater Flow Model..............................................................11

Local-Scale Model Geometry and Discretization .........................................................................13

Local-Scale Groundwater-Flow Model Boundary Conditions ....................................................13

Recharge.....................................................................................................................................13

Leakage from sewer and water-distribution pipelines ..............................................15

Channel Leakage ..............................................................................................................15

Infiltration from Ponds and Urban Turf Areas..............................................................15

Groundwater Withdrawals.......................................................................................................15

Zonation of Simulated Hydraulic Properties ..................................................................................16

Local-Scale Transport Simulation ...................................................................................................16

Governing Equation ...................................................................................................................17

Specified-Concentration Boundary Conditions....................................................................18

Specified-Concentration Initial Conditions ...........................................................................20

Model Calibration................................................................................................................................20

Concentration Observations ....................................................................................................20

Water-Level Observations .......................................................................................................26

Calibrated Model Parameter Values ......................................................................................26

Simulated Water Levels..............................................................................................................................26

Simulated Intra-Borehole Flow Log..........................................................................................................29

Simulated Concentrations of Carbon-14, Tritium, and Chlorofluorocarbons ....................................30

Carbon-14.............................................................................................................................................31

Tritium ..................................................................................................................................................32

Chlorofluorocarbons...........................................................................................................................32

Simulated Binary Age Mixtures in and Surrounding the SSW ............................................................33

Simulated Fraction of Young Groundwater in and Surrounding the SSW................................34

Simulated Fraction of Old Groundwater in the SSW ....................................................................35

Simulated VOC Plume..................................................................................................................................36

Model Uncertainties and Limitations .......................................................................................................40

Summary and Conclusions.........................................................................................................................43

Acknowledgments.......................................................................................................................................44

References Cited..........................................................................................................................................44

Appendix Simulation of the regional distribution of carbon-14 concentration..............................47
vi

Figures

1. Map showing shaded relief of the Middle Rio Grande Basin in New Mexico, 

showing major regional features and the location of the local-scale groundwater 

model ..............................................................................................................................................3

2. Map showing land-surface imagery of the local-scale model area from 2005, and 

locations of public-supply wells and selected known contaminant sources, 

Albuquerque, New Mexico .........................................................................................................5

3. Graph showing short-normal resistivity log of borehole FP1 in Albuquerque, 

New Mexico, 2007 ........................................................................................................................8

4. Block diagram showing (A) block and (B) dissected perspective views of one hydrofacies realization in the local model area ..................................................................................10

5. Cross section showing layer-thickness ranges of the modified regional flow 

model ............................................................................................................................................12

6. Map showing distribution of simulated recharge sources in the local-scale model, 

Albuquerque, New Mexico .......................................................................................................14

7. Graph showing stacked column chart of daily withdrawals from public-supply 

wells in the local study area .....................................................................................................16

8. Graph showing reported groundwater withdrawals within the local-scale model 

domain and within the regional-model domain ....................................................................17

9. Graph showing atmospheric concentrations of chlorofluorocarbons (CFC-11, 

CFC-12, and CFC-113) above Albuquerque, New Mexico, 1950–2010................................18

10. Graph showing atmospheric carbon-14 concentration and specified concentrations 

for 10- and 20-year transport to the water table ...................................................................19

11. Graph showing atmospheric tritium concentration above Albuquerque, 

New Mexico, and specified concentration for 10- and 20-year transport to the 

water table ..................................................................................................................................19

12. Map showing regional and local-scale finite difference cells, with carbon-14 

concentration interpolated from the regional transport simulation and specified 

as initial concentration for the top layer of the local-scale model, Albuquerque, 

New Mexico ................................................................................................................................21

13. Map showing carbon-14 concentration interpolated from layer 5 of the regional 

transport simulation and specified as initial concentration for layers 35–39 of the 

local-scale model, Albuquerque, New Mexico .....................................................................22

14. Map showing boundary of the local-scale model, locations of age-tracer observation wells, and predevelopment water-table altitudes (in meters) simulated with 

the local-scale flow model, and with the coupled regional-scale flow 

model, location ...........................................................................................................................24

15. Map showing boundary of the local-scale model, locations of water-level observation and public-supply wells, water-table altitudes (in meters) simulated for 

December 31, 2009, with layer 37 of the local-scale flow model, and with layer 5 of 

the coupled regional-scale flow model, location..................................................................27

16. Graph showing observed and simulated water levels for public-supply and observation wells in the local model, Albuquerque, New Mexico...............................................28

17. Graph showing water levels observed in the studied supply well (SSW) and PSW3, 

and simulated with the local model, Albuquerque, New Mexico.......................................30

18. Graph showing relation between water levels observed and simulated with the 

local model, Albuquerque, New Mexico ................................................................................30

19. Graph showing simulated and observed flow logs for the studied supply well 

(SSW), Albuquerque, New Mexico..........................................................................................31
vii

20. Graph showing relation between observed carbon-14 concentrations and 

concentrations simulated with the model of the local study area in Albuquerque, 

New Mexico ................................................................................................................................32

21. Graph showing carbon-14 concentrations observed and simulated in the studied 

supply well in Albuquerque, New Mexico..............................................................................33

22. Graph showing relation between observed tritium concentrations and 

concentrations simulated with the model of the local study area in Albuquerque, 

New Mexico ................................................................................................................................34

23. Graph showing tritium concentrations observed and simulated in the studied 

supply well in Albuquerque, New Mexico..............................................................................35

24. Graph showing relation between simulated and observed CFC-113 concentrations.....36

25. Graph showing relation between simulated and observed CFC-12 concentrations.......36

26. Graph showing relation between simulated and observed CFC-11 concentrations.......36

27. Graph showing simulated and observed CFC concentrations in the studied supply 

well in Albuquerque, New Mexico ..........................................................................................37

28. Graph showing simulated percentages of post-1950 water in the SSW, 1975–2005 ......38

29. Graph showing simulated percentages of post-1950 water in the aquifer 

surrounding the SSW.................................................................................................................39

30. Graph showing simulated fraction of water originating more than 250 meters below 

land surface in the studied supply well in Albuquerque, New Mexico.............................40

31. Map showing flow paths of particles released from the contaminant source from 

1975 until 1989 simulated with 8 percent effective porosity, public-supply wells, 

and monitoring wells with observed chlorinated solvents, Albuquerque, 

New Mexico ................................................................................................................................41

32. Map showing flow paths of particles released from the contaminant source in 1976 

simulated with 1.1 percent effective porosity, public-supply wells, and monitoring 

wells with observed chlorinated solvents, Albuquerque, New Mexico............................42

Tables

1. Drillers’ and laboratory lithologic logs of borehole FP1 in Albuquerque, 

New Mexico, 2007 ........................................................................................................................7

2. Child-model refinement with respect to regional model, Albuquerque Basin, 

New Mexico ................................................................................................................................13

3. Summary of parameters representing hydraulic property values in the local 

scale model in Albuquerque, New Mexico ............................................................................23

4. Concentration observations used to calibrate the local-scale flow and transport 

model in Albuquerque, New Mexico.......................................................................................25
viii

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2

) 0.0002471 acre

square kilometer (km2

) 247.1 acre

square meter (m2

) 10.76 square foot (ft2)

square kilometer (km2

) 0.3861 square mile (mi2)

Volume

liter (L) 0.2642 gallon (gal)

cubic meter (m3

) 264.2 gallon (gal)

cubic meter (m3

) 35.31 cubic foot (ft3)

cubic kilometer (km3

) 0.2399 cubic mile (mi3)

cubic meter (m3

) 0.0008107 acre-foot (acre-ft)

Flow rate

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

cubic meter per day (m3

/d) 35.31 cubic foot per day (ft3/d)

cubic meter per day (m3

/d) 264.2 gallon per day (gal/d)

Hydraulic conductivity

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

Hydraulic gradient

meter per kilometer (m/km) 5.27983 foot per mile (ft/mi)

Transmissivity*

meter squared per day (m2

/d) 10.76 foot squared per day (ft2/d)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F–32)/1.8

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).

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

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

or micrograms per liter (μg/L).
ix

Acronyms and Abbreviations

ABCWUA Albuquerque Bernalillo County Water Utility Authority

BFH Boundary flow and head package

CFC Chlorofluorocarbon

DEM Digital Elevation Model

DNAPL Dense Non-Aqueous Phase Liquid

GIS Geographic Information System

HOB Head Observation Package

LGR Local Grid Refinement

LPF Layer-Property Flow package

MNW2 Multi-node well package

MRGB Middle Rio Grande Basin

MTBE Methyl tert-butyl ether

NAWQA National Water-Quality Assessment 

PCE Tetrachloroethene

pmc Percent modern carbon

ppt Parts per trillion

pptv Parts per trillion by volume

PSW Public-Supply Well

RCH Recharge package

RMSE Root mean square error

SSW Studied Supply Well

TANC Transport of Anthropogenic and Natural Contaminants

TCE Trichloroethene

TDS Total Dissolved Solids

TOB Transport Observation Package

TPROGS Transition Probability Geostatistical Software

TU Tritium units

TVD Total Variation Diminishing scheme

USGS U.S. Geological Survey

 VOC Volatile Organic Compound

Simulations of Groundwater Flow, Transport, and Age 

in Albuquerque, New Mexico, for a Study of Transport 

of Anthropogenic and Natural Contaminants (TANC) to 

Public-Supply Wells

By Charles E. Heywood

Abstract

Vulnerability to contamination from manmade and 

natural sources can be characterized by the groundwater-age 

distribution measured in a supply well and the associated 

implications for the source depths of the withdrawn water. 

Coupled groundwater flow and transport models were developed to simulate the transport of the geochemical age-tracers 

carbon-14, tritium, and three chlorofluorocarbon species to 

public-supply wells in Albuquerque, New Mexico. A separate, 

regional-scale simulation of transport of carbon-14 that used 

the flow-field computed by a previously documented regional 

groundwater flow model was calibrated and used to specify 

the initial concentrations of carbon-14 in the local-scale transport model. Observations of the concentrations of each of the 

five chemical species, in addition to water-level observations 

and measurements of intra-borehole flow within a publicsupply well, were used to calibrate parameters of the localscale groundwater flow and transport models. 

The calibrated groundwater flow model simulates the 

mixing of “young” groundwater, which entered the groundwater flow system after 1950 as recharge at the water table, 

with older resident groundwater that is more likely associated with natural contaminants. Complexity of the aquifer 

system in the zone of transport between the water table and 

public-supply well screens was simulated with a geostatistically generated stratigraphic realization based upon observed 

lithologic transitions at borehole control locations. Because 

effective porosity was simulated as spatially uniform, the 

simulated age tracers are more efficiently transported through 

the portions of the simulated aquifer with relatively higher 

simulated hydraulic conductivity. Non-pumping groundwater 

wells with long screens that connect aquifer intervals having different hydraulic heads can provide alternate pathways 

for contaminant transport that are faster than the advective 

transport through the aquifer material. Simulation of flow and 

transport through these wells requires time discretization that 

adequately represents periods of pumping and non-pumping. 

The effects of intra-borehole flow are not fully represented in 

the simulation because it employs seasonal stress periods,

which are longer than periods of pumping and non-pumping.

Further simulations utilizing daily pumpage data and

model stress periods may help quantify the relative 

effects of intra-borehole versus advective aquifer flow on the 

transport of contaminants near the public-supply wells. The 

fraction of young water withdrawn from the studied supply 

well varies with simulated pumping rates due to changes in the 

relative contributions to flow from different aquifer intervals.

The advective transport of dissolved solutes from a 

known contaminant source to the public-supply wells was 

simulated by using particle-tracking. Because of the transient 

groundwater flow field, scenarios with alternative contaminant release times result in different simulated-particle fates, 

most of which are withdrawn from the aquifer at wells that are 

between the source and the studied supply well. The relatively small effective porosity required to simulate advective 

transport from the simulated contaminant source to the studied 

supply well is representative of a preferential pathway and not 

the predominant aquifer effective porosity that was estimated 

by the calibration of the model to observed chemical-tracer 

concentrations.

Introduction

Clean groundwater is a valuable source of public drinking water throughout the world, especially in arid areas with 

limited surface-water availability. Groundwater wells that 

supply drinking water to the public are vulnerable to contamination from natural and anthropogenic (manmade) sources. An 

improved understanding of the sources of water to publicsupply wells and the processes that lead to contamination 

is needed to reduce the incidence of natural and manmade 

contamination in wells that supply this resource. In order to 

increase understanding of the factors that affect transport of 

anthropogenic and natural contaminants (TANC) to publicsupply wells, the U.S. Geological Survey (USGS) National 

Water-Quality Assessment (NAWQA) Program has conducted 
2 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

a series of groundwater studies in areas representative of the 

different types of aquifer systems that exist in the United 

States (Paschke, 2007). One component of these studies has 

been the use of groundwater models to investigate the sources 

of contaminants, their fate and transport within groundwater 

systems, and their effects on public-supply-well water quality 

at regional and local scales. The regional-scale groundwater 

flow models helped to establish relations between hydrogeologic and land-use variables with observed public-supplywell water-quality trends. Local-scale groundwater flow and 

transport models nested within the regional-scale model areas 

have provided increased detail for understanding physical 

and chemical processes that affect public-supply-well water 

quality.

Supply-well vulnerability can be characterized by the 

groundwater age, which is a measure of the travel time from 

recharge-source areas, and associated implications for the 

depths of the withdrawn water. In general, young groundwater 

in a well can indicate a vulnerability to manmade contaminants in the aquifer. However, management actions based 

on avoidance of young groundwater can lead to increased 

vulnerability to natural contaminants, such as arsenic (Hinkle and others, 2009). Because the age of groundwater is 

strongly correlated to its depth within an aquifer system, the 

groundwater-age distribution measured in a supply well can 

be used to imply the source depths of the withdrawn water 

and characterize its vulnerability to contamination from either 

deep or shallow sources. Although natural contaminants may 

be associated with sources that reside in either shallow or deep 

portions of an aquifer system, anthropogenic contamination 

usually originates from a shallow source.

Within the Middle Rio Grande Basin (MRGB) in New 

Mexico (fig. 1) considered in this study, aquifer-system 

recharge is thought to principally occur from losing reaches 

of the Rio Grande (Plummer and others, 2004) and adjacent 

to mountain fronts, where overland runoff across relatively 

impermeable bedrock first infiltrates into more permeable 

basin-fill sediments. Infiltration of precipitation through 

the thick unsaturated zone (tens to hundreds of meters) that 

exists over most of the remainder of the basin is not thought 

to substantively occur or to contribute to recharge away from 

the mountain fronts. Long times are required for groundwater 

to travel from either of these recharge locations; therefore, 

anthropogenic contaminants, such as volatile organic compounds (VOCs) and the groundwater age-tracers tritium and 

chlorofluorocarbons (CFCs), which are indicative of a component of relatively recent recharge, would not be present at 

shallow and intermediate depths within the study area if these 

distant recharge locations were the only pathways for young 

water to enter the groundwater system. However, measureable concentrations of these chemicals from production and 

observation wells within the study area are documented in the 

companion report by Bexfield and others (2012), implying the 

existence of closer sources of young water and relatively fast 

pathways for transport of that water into wells.

For the TANC study area of the MRGB, a regionalscale groundwater model simulated transient groundwater 

flow through 2008 and was used to compute areas contributing recharge and the transport times from recharge areas to 

public-supply wells (Bexfield and others, 2011). Although 

the discretization of this regional-scale model was finer than 

that used for previous models of the MRGB, it remained too 

coarse to adequately simulate heterogeneity within the shallow 

and intermediate depths of the aquifer system and pathways 

through which anthropogenic contaminants and young groundwater-age tracers may be transported to wells screened at such 

depths within the aquifer system. 

The local-scale groundwater model documented in this 

report includes additional sources of local recharge that can 

simulate introduction of young (post-1950s) recharge water, 

and its entrained age-tracers or contaminants, into the saturated aquifer system. The simulated mixing of this younger 

water with resident older groundwater is constrained by observations of the concentrations of carbon-14, tritium, and CFCs. 

Contaminants associated with the younger water may be 

transported relatively quickly through the more hydraulically 

conductive portions of the simulated aquifer system. Well 

boreholes connecting aquifer intervals with different hydraulic 

heads can provide an alternate pathway for groundwater to 

flow and transport dissolved solutes.

The transient groundwater flow model simulates the 

effects of seasonal changes in pumping rates on hydraulic 

gradients and consequent transport rates between different 

aquifer intervals and the screened intervals of the simulated 

withdrawal wells. Known sources of anthropogenic contamination, including VOCs, exist within the local study area, and 

contaminants believed to have originated at those sources 

have been observed at various observation wells (Bexfield and 

others, 2012). The likely advective transport pathways and 

contaminant source-release times were investigated with the 

MODPATH particle-tracking program.

Purpose and Scope

This report describes the construction and calibration 

of a local-scale model that simulates the groundwater flow, 

transport, and age within the local-scale TANC study area in 

Albuquerque, New Mexico. The local-scale model was numerically coupled with a new local-grid-refinement technique to a 

previously documented (Bexfield and others, 2011) regionalscale groundwater model of the MRGB of New Mexico. This 

coupling provided accurate boundary conditions around the 

perimeter of the local-scale model throughout the course of the 

108-year simulation, during which the simulated water levels 

and flows across model boundaries changed significantly. The 

simulated groundwater flow field computed by the local-scale 

model was used as input for an associated solute-transport 

model, which encompasses the same study area. The simulated 

tracers of young groundwater (3H and CFCs) were surrogates 

for potentially harmful anthropogenic contaminants 
Introduction 3

VALENCIA

BERNALILLO

SANDOVAL

TORRANCE

SANTA FE

SOCORRO

CIBOLA

MC KINLEY

Albuquerque

Rio Rancho

Bernalillo

Arroyo Chico

Rio

Puerco

Tijeras

Arroyo

Rio

Salado

Jemez

River

Jemez Canyon

 Reservoir

R i o

G r a n d e

R i o

G r a n d e

Santa

Fe River

Galisteo

Creek

Arroyo

Las

Tonque

Huertas

Creek

Abo

Arroyo

Rio

Puerco

Rio

Salado

Rio

San Jose

Cochiti

Reservoir Jemez

Mountains Nacimiento

Uplift

San Juan Basin

Sandia 

Mountains

Mountains

Monzanita

Rio

Puerco

fault

zone

NEW MEXICO

30’

35°45’

15’

35°00’

45’

30’

34°15’

107°15’ 107°00’ 45’ 30’ 106°15’

0 10 20 KILOMETERS

0 10 20 MILES

Base modified from U.S. Geological Survey digital data, 1:24,000, 1999

Universal Transverse Mercator, Zone 13N, North American Datum of 1983.

EXPLANATION

Middle Rio Grande Basin

Elevation, in meters–

 High: 2,702

 Low: 1,424

Urban area

Rio Grande inner valley

Southern limit of Middle 

Rio Grande Basin–

regional-scale study area

Local-scale study area 

Figure 1. Shaded relief of the Middle Rio Grande Basin in New Mexico, showing major regional features and the location of the localscale groundwater model.
4 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

(such as VOCs) that may be transported along similar pathways in the aquifer system. Simulation of carbon-14 concentrations required specification of initial concentration conditions that realistically represented the carbon-14 concentration 

distribution throughout the three-dimensional local-scalemodel domain prior to the onset of groundwater pumping and 

recharge of water with carbon-14 concentrations affected by 

nuclear-bomb testing. When combined with observed waterlevel and borehole-flow observation data, observed isotopic 

and chemical concentration data provided an observation database for calibration of groundwater flow and solute-transport 

model parameters with non-linear regression.

A companion report by Bexfield and others (2012) 

describes the hydrogeology, water chemistry, and factors 

affecting the transport of selected anthropogenic and natural 

contaminants in the zone of contribution to the studied publicsupply well in the local-scale TANC study area. Together, 

these reports enable further future comparisons amongst 

TANC local-scale study areas.

Description of Local-Scale Model Area

Bexfield and others (2012) identified an area for detailed 

study within metropolitan Albuquerque, the largest city in 

the MRGB in New Mexico (fig. 1). The local-scale model 

described in this report was designed to simulate groundwater 

flow and solute transport beneath this area. The horizontal 

area encompassed by the model is 24 square kilometers (km2

) 

[4 kilometers (km) in the east-west direction and 6 km in the 

north-south direction] and is located approximately south of 

Lomas Boulevard, north of the Albuquerque International 

Airport, east of the Rio Grande, and west of Girard Boulevard 

(fig. 2). This area encompasses some of the inner valley east 

of the Rio Grande where agriculture began in the 1900s and 

where areas of residential, commercial, and industrial land 

use were developed in the 1930s. Current land use within this 

area can be characterized as “urban,” with several recreational 

parks and small open-space areas.

The western half of the local-scale study area lies within 

the Rio Grande inner valley and is relatively flat with altitudes 

ranging from 1,505 to 1,525 meters (m). An abrupt change in 

slope separates the inner valley from the terrace which gradually rises toward the mountain front to the east. The eastern 

half of the local-scale study area is on this terrace with moderate relief and altitudes that range from about 1,560 to 1,620 m. 

The local-model area (fig. 2) contains at least seven public water-supply wells (PSWs), one of which Bexfield and others (2012) selected for intensive study, known hereafter as the 

studied supply well (SSW). Three known sites of groundwater 

contaminated with chlorinated solvents, including trichloroethene (TCE), are present in the local-model area, and petroleum 

storage tanks have leaked at two or more sites.

The SSW is a high-production public-supply well located 

in an area where the depth to the water table is greater than 

30 m. Despite having well screens installed at an even greater 

depth, anthropogenic contamination in the forms of chlorinated solvents and methyl tert-butyl ether (MTBE) has been 

measured at low concentrations in the SSW. Chlorinated solvents and MTBE have also been detected at concentrations far 

below drinking-water standards in other nearby PSWs, which 

were installed in the 1960s and 1970s (Bexfield and others, 

2012). 

Of the three sites known to be contaminated with chlorinated solvents, the site located 3 km northwest of the SSW 

is of particular interest as a likely source of contaminants to 

the SSW. The documented contamination at this site, where a 

(now defunct) dry-cleaning facility was located, is in an area 

of the Rio Grande inner valley where the depth to the water 

table is 9 m or less. Monitoring wells have indicated the presence of younger, more contaminated water beneath older, less 

contaminated ground water (U.S. Environmental Protection 

Agency, 2011). It is likely that vertical contaminant transport 

to more than 60 m below the water table has resulted from 

pumping of supply wells that induced downward hydraulic 

gradients and (or) from flow down well boreholes between the 

contamination site and the SSW.

A network of monitoring wells provided ground-water 

level and chemical data within the local study area. In addition 

to the 12 existing wells that were utilized for data collection, 

13 new wells at 4 flow-path (FP) locations were installed for 

the TANC study (Bexfield and others, 2012). Wellbore flow 

and water-chemistry data were also obtained from various 

depths within the SSW.

Previous Investigations

Summaries of the extensive previous hydrogeologic 

investigations are contained in publications by Thorn and others (1993), Bartolino and Cole (2002), Bexfield (2010), and 

Bexfield and others (2011, 2012). Because the present work is 

based in part on previous investigations involving groundwater modeling, a brief synopsis of the recent pertinent studies is included here. McAda and Barroll (2002) developed a 

transient regional groundwater flow model of the Albuquerque 

basin that simulated predevelopment conditions and anthropogenic changes for the years 1900 through March 2000. 

Sanford and others (2004) used carbon-14 concentrations 

and hydrochemical-zone locations to constrain the hydraulic 

parameters of an alternative regional groundwater flow model 

that was calibrated with the program UCODE (Poeter and 

Hill, 1998). Bexfield and others (2011) modified the McAda 

and Barroll (2002) model by refining the horizontal discretization, adding transient stress data (representing periods through 

Dec. 31, 2008), simulating urban recharge, and revising the 

hydraulic parameters, which were estimated by calibrating the 

model using the program PEST (Doherty, 2004). The basin-fill 

thickness estimates, refined on the basis of recent analyses of 

seismic reflection data and isostatic-residual gravity anomalies (Grauch and Connell, 2013), were incorporated into the 

model-layer thicknesses of the version of the regional model 

documented and utilized for the analyses in this report.
Introduction 5

!

FP4

FP3

FP2 FP1

FP4

FP3

FP2 FP1

k

!

!

!

PSW 6 PSW 5

PSW 3

SSW

PSW 1

PSW 6 PSW 5

PSW 3

SSW

PSW 1

PSW 4

106°39' 106°38'30" 106°38' 106°37'30" 106°37'

35°05'30"

35°05'

35°04'30"

35°04'

35°03'30"

35°03'

0 1 MILE

0 1 KILOMETER FP2

PSW 6

Contaminant source 

Observation well with electric logs and cuttings 

Public-supply well with electric log 

Other withdrawal wells

EXPLANATION

Albuquerque Int lbuquerque International Airport ational Airport

Lomas Blvd

Base modified from U.S. Geological Survey digital data, 1:24,000, 1999

Universal Transverse Mercator, Zone 13N, 

North American Datum of 1983.

Figure 2. Land-surface imagery of the local-scale model area from 2005, and locations of public-supply wells and selected known 

contaminant sources, Albuquerque, New Mexico.
6 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

Hydrogeology of the Local-Scale 

Model Area

The alluvial-fill within the MRGB consists primarily of 

the Santa Fe Group of late Oligocene to middle Pleistocene 

age, which consists of unconsolidated to moderately consolidated sediments deposited in fluvial, lacustrine, or piedmontslope environments. The screened intervals of most wells 

within the study area are within the Rio Puerco member of the 

Ceja Formation of the Santa Fe Group, which consists of fluvial deposits (sand, gravel, and mud) of Pliocene age (Connell 

and others, 1998; Connell, 2006). Within the local-scale model 

area, the Ceja Formation is overlain by Quaternary alluvium 

within the Rio Grande inner valley and overlain by the Sierra 

Ladrones Formation of Pliocene to lower Pleistocene age 

beneath the higher terrace areas to the east (Connell, 2006). 

The lower Atrisco member of the Ceja Formation is generally 

finer grained and lies beneath the screened intervals of most 

wells in the study area.

The natural, predevelopment direction of groundwater flow through this area was from the north-northeast and 

toward the Rio Grande. Considerable groundwater withdrawals in the area have redirected the flow directions to the east 

and steepened both horizontal and vertical head gradients. 

More detailed discussions of the climate, hydrology, geology, land use, topography, water chemistry, installation of 

flow-path (FP) well nests, and other aspects of the regional 

and local-scale study area are found in the companion reports 

Bexfield and others (2011, 2012). 

Aquifer-Pumping Test

Operation of the SSW in February 2008 provided data 

that were used to obtain hydraulic conductivity estimates of 

the sediments in which the SSW was screened. Following a 

2-month period of inactivity, the SSW was pumped for about 

10 hours and was subsequently turned off for several days. 

The time-drawdown data recorded in the FP1 nest of wells 

located about 33 m east of the SSW were used to calculate 

a transmissivity of the aquifer in the screened interval of the 

SSW. The analysis employed a Neuman (1974) unconfined 

solution assuming partial penetration of the SSW to the top of 

the relatively fine-grained Atrisco member of the Ceja Formation (Scott Christenson, U.S. Geological Survey (retired), 

written commun., 2011). Dividing the analytically derived 

transmissivity values for each of the four piezometers in the 

FP1 nest by the length of the screened interval of the SSW 

yielded four estimates of hydraulic conductivity in this interval, which range from 3.1 to 7.9 meters per day (m/d). These 

values and the average of the four interpreted values (4.5 m/d) 

are similar to previous estimates of about 6.1 m/d based on 

aquifer tests that used public-supply wells in the vicinity of the 

SSW (Thorn and others, 1993).

Characterization of Tertiary Alluvium

Within the local study area, electric-resistivity and 

lithologic logs from 10 sites and borehole cuttings from 4 sites 

were used to characterize lithologic variations with depth 

(fig. 2). In addition, four resistivity logs of the Burton WellField Wells (#1–4), located within 1.2 km east of Girard Blvd., 

were used for lithologic characterization. Short-normal resistivity logs from the 14 sites, spanning resistivity ranges on the 

order of 100 ohm-meters (ohm-m), provided the primary basis 

for lithologic interpretation. The portions of the individual 

resistivity logs used for classification ranged in length from 47 

to 134 m; the average thickness was 108 m. Major resistivityresponse patterns correlated well between boreholes in the 

north-south direction and weakly in the east-west direction. 

The enhanced correlation in the north-south direction may be 

related to the primary flow direction of the fluvial depositional 

system, which was predominantly north-south.

A 3-m depth interval was chosen for characterizing lithology in each borehole, which was somewhat greater than the 

resolution of the short-normal resistivity logs, and this length 

was for the vertical finite-difference cell dimension within the 

finest-resolution depth interval of the numerical flow model. 

Most of the driller’s lithologic logs summarized lithology at 

intervals much larger than 3 m and were therefore unsuitable 

for classifying lithology at 3-m intervals. Driller’s logs from 

the four FP monitoring wells were primarily summarized at 

3-m intervals and were based on drilling rate and subjective 

judgment of drilling vibration, in addition to summaries of 

borehole cuttings collected from each 3-m depth interval. The 

cuttings were collected at the top of the borehole with a fine 

strainer that allowed capture of material that was approximately sand sized and larger. Additional samples of sand-sized 

cuttings were collected from the drilling-mud recycling equipment. Because drilling mud (consisting of clay- and silt-sized 

particles) was used to excavate cuttings from the borehole, the 

quantity of clay- and silt-sized cuttings could not be reliably 

determined. Other complications associated with use of the 

cuttings to represent the lithology of the borehole include (1) 

the small sample taken from each 3-m borehole interval, (2) 

increased travel distance to the surficial collection point with 

increased borehole depth and the consequent greater uncertainty of the representative depth of cuttings, and (3) greater 

upward travel-time for gravel-sized cuttings relative to sandsized cuttings.

The alluvial aquifer in the local-study area is dominantly 

sand fill with gravel lenses and thin layers of finer-grained sediment. In order to correlate aquifer lithology with the response 

of the resistivity logs, two lithologic logs from borehole FP1, 

one prepared onsite by the drillers and a second prepared in 

a laboratory after washing of the cuttings to remove residual 

drilling mud (table 1), were compared with the resistivity log 

from borehole FP1 (fig. 3). The resistivity logs show distinct 

patterns within three particular depth ranges: large-amplitude 

fluctuations between high- and low-resistivity peaks from 
Hydrogeology of the Local-Scale Model Area 7

Table 1. Drillers' and laboratory lithologic logs of borehole FP1 in Albuquerque, New Mexico, 2007.

Depth 

(meters below land surface) Drillers’ lithologic log Lithologic log based on laboratory analysis of borehole cuttings1

55–58 Sand, small gravel Fine silty sand

58–61 Sand, small gravel, silty clay NS2

61–64 Sand, silty clay Fine sand

64–67 Sand, silty clay, small gravel Coarse to medium sand

67–70 Silty clay, sand Coarse to medium sand

70–73 Sand, small gravel Coarse to medium sand

73–76 Sand, small gravel Very fine to fine sand

76–79 Sand, small gravel Very fine to fine sand

79–82 Sand, silty clay Medium to coarse sand

82–85 Core Medium to coarse sand

85–88 Core Medium to coarse sand

88–91 Sand, silty clay Very fine to fine sand

91–94 Sand, small gravel Coarse to very coarse sand

94–98 Sand, gravel, silty clay Very fine to fine sand

98–101 Small gravel, sand Coarse to very coarse sand

101–104 Small gravel, sand Fine sand

104–107 Sand, silty clay Fine sand

107–110 Sand, silty clay Fine sand

110–113 Sand, silty clay Very fine to fine sand

113–116 Sand, small gravel, silty clay Very fine to fine sand

116–119 Sand, sandy clay Very fine to fine sand

119–122 Sand, sandy clay Very fine to fine sand

122–125 Sand, silty clay Very fine to fine sand

125–128 Sand, silty clay Very fine to fine sand

128–131 Sand, silty clay Very fine to fine sand

131–134 Sand, silty clay, small gravel Very fine silty sand

134–137 Sand, silty clay Very fine silty sand

137–140 Sand, silty clay Very fine silty sand

140–143 Sand, silty clay Coarse sand with very fine silty sand

143–146 Sand, silty clay, small gravel Coarse sand with very fine silty sand

146–149 Sand, silty clay, small gravel Coarse sand with very fine silty sand

149–152 Core Very fine clay-rich sand

152–155 Core Very fine clay-rich sand

155–158 Sand, gravel Very fine clay-rich sand

158–162 Gravel Very fine clay-rich sand

162–165 Gravel Very fine silty sand

165–168 Sand, small gravel Very fine silty sand

168–171 Sand, small gravel Coarse sand

1

 Silt and clay were generally not included in the descriptions because they were difficult to distinguish from the drilling mud.

2 NS - No sample.
8 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

60

70

80

90

100

110

120

130

140

150

160

170

180

190

200

210

220

230

240

250

260

270

280

290

300

310

320

330

340

350

360

370

0 20 40

Depth below land surface, in meters

Electrical resistivity, in ohm-meters

Resistivity 

Resistivity division

EXPLANATION

60 80 100

Figure 3. Short-normal resistivity log of borehole FP1 in Albuquerque, New Mexico, 2007.
Methods for Simulation of Groundwater Flow and Solute Concentrations 9

depths of 60 to 100 m, small-amplitude fluctuations and low 

resistivity between depths of 100 and 137 m, and intermediate-amplitude fluctuations around moderate resistivity values 

between depths of 137 and 171 m. Comparison of the resistivity and lithologic logs indicates that between depths of 60 and 

100 m, the high resistivity peaks are generally associated with 

gravels and coarse sands, while the low peaks are generally 

associated with fine sands and (or) silty clays. Between depths 

of 100 and 137 m, the low resistivity is again generally associated with fine sands and silty clays. Between depths of 137 

and 171 m, gravels, sands, and silty clays exist, but the correlation between the lithologic and resistivity logs is poor. This 

may be caused by less-representative cuttings of the actual 

lithology with increased depth of the borehole.

The resistivity log for borehole FP1 was divided into 

three resistivity intervals based on the sediment lithology 

and the electrical resistivity values. The division between the 

low and intermediate interval, at 37 ohm-m, is the high-resistivity limit of measurements between 100 and 137 m depth. 

The division between the intermediate and upper interval, 

at 55 ohm-m, is the low-resistivity limit of measurements 

between 55 and 100 m in which primarily gravel or coarse 

sand lithologies exist. Three-meter depth intervals where 

measured resistivity was generally less than 37 ohm-m were 

assumed to be fine sand and silty clays. Three-meter depth 

intervals where measured resistivity was generally larger 

than 55 ohm-m were assumed to represent gravel and coarse 

sand. Three-meter depth intervals where measured resistivity 

between these two values were assumed to represent a mixture 

of sands with lenses of gravel and silty clay. 

Because electrical-resistivity data from the 14 boreholes 

were measured using different resistivity sensors in various 

holes, the borehole resistivity logs do not all provide the same 

response to materials of identical lithology. As a result, absolute resistivity values could not be compared among logs from 

the different boreholes. However, because the sediments sampled during drilling of each of the 14 boreholes were deposited 

by the same ancestral fluvial system, it is assumed that the patterns in the relative resistivity response in each borehole correspond to similar changes in the subsurface lithology. Under 

this assumption, lower and upper divisions were plotted on the 

resistivity logs of the 13 other boreholes by either (1) correlation with the resistivity log of borehole FP1 or (2) observing 

patterns of low, medium, and high resistivity responses similar 

to the resistivity log from borehole FP1. For each resistivity log, the upper resistivity division varied but was usually 

20–40 ohm-m higher than the lower division.

Based on the observed correlation between electric-log 

responses and grain sizes in the sample cuttings, as well as 

documented relations among permeability, grain size, and 

electric-log responses (Alger, 1966; Croft, 1971) and between 

hydraulic conductivity and grain size (Hazen, 1911; Shepherd, 1989), the gross electric-log responses were assumed to 

indicate grain size and relative hydraulic conductivity. Aquifer 

depth intervals corresponding to low, intermediate, and high 

resistivity intervals, such as those previously described for 

well FP1, were assigned to one of three hydrofacies representing fine, moderate, and coarse grain-sized zones, which, 

in turn, have relatively low, intermediate, and high hydraulic 

conductivity, respectively. The simulated hydraulic conductivities for each of these three zones were estimated by the 

parameter-estimation process described in the Model Calibration section of this report.

The distribution of the low, intermediate, and high resistivity intervals in the 14 boreholes were used to synthesize 10 

realizations of hydrostratigraphy using Transition Probability 

Geostatistical Software (TPROGS) (Carle, 1999). Corresponding fine, moderate, and coarse hydrofacies occupied 59, 

33, and 8 percent, respectively, of the total volume sampled. 

Each realization honored the stratigraphic data at the borehole 

control sites and had identical transition probabilities, mean 

hydrofacies lengths, and other geostatistical properties. The 

synthetic hydrostratigraphic realizations resemble maps of 

similar surficial alluvial deposits in the Albuquerque basin 

deposits in scale, orientation, and relative distribution and 

shapes of the hydrofacies. 

Each realization was generated with a base altitude 

of 1,341 m and discretized the hydrofacies on a grid of 

40 x 60 x 60 cells in the east-west, north-south, and vertical 

directions, respectively, with cell spacing of 100 m in both 

horizontal directions and 3 m vertically. The portion of each 

realization corresponding to thirty-two 3-m-thick local-scale 

model layers (realization layers 14 through 45) were extracted 

to simulate a complex hydraulic conductivity distribution in 

the portion of the local groundwater flow model between the 

altitudes of 1,381 and 1,478 m. Block and dissected perspectives (fig. 4) of a hydrofacies realization depict the simulated 

continuity amongst the three facies. 

Methods for Simulation of 

Groundwater Flow and Solute 

Concentrations

Although this study focuses on simulation of groundwater flow and solute transport within the local study area, simulations of regional groundwater flow and carbon-14 transport 

were necessary to provide boundary and initial conditions for 

the local-scale flow and transport simulations. The boundary 

and initial conditions required for the local-scale groundwater 

flow model were provided by use of numerical coupling with 

a modified version of the regional-scale groundwater flow 

model documented by Bexfield and others (2011). 

Coupling of Regional and Local-Scale 

Groundwater Flow Models

The lateral boundaries of the regional-scale model correspond with physical hydrologic or geologic features, but those 

of the local-scale model do not; consequently, groundwater 
10 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

1,350

1,375

1,400

1,425

z, in meters

x, in meters -57,500

565,000

562,500

y, in meters

1,450

1,475

1,500

Fines 

Coarse 

Sand 

Well

EXPLANATION

A

1,350

1,375

1,400

1,425

z, in meters

x, in meters

-57,500

565,000

562,500

y, in meters

1,450

1,475

1,500

B

Figure 4. (A) Block and (B) dissected perspective views of one hydrofacies realization in the local model area.
Methods for Simulation of Groundwater Flow and Solute Concentrations 11

levels and flows across lateral boundaries of the local-scale 

model change over time due to groundwater pumpage and 

other transient conditions that are simulated by both the 

regional and local-scale models. In order to provide physically realistic lateral boundary conditions for the local-scale 

model that are based on the regional groundwater system, the 

transient groundwater levels on the local-scale-model lateral 

boundaries were computed with the local grid refinement 

(LGR) method.

MODFLOW-LGR (Mehl and Hill, 2013) was used to 

simulate constant-density groundwater flow at the regional and 

local-area scales. MODFLOW-LGR is a version of the MODFLOW-2005 code (Harbaugh, 2005) that enables numerical 

coupling of a “parent model,” which is typically regional 

in scale, with a “child model” that has finer-detailed spatial 

discretization in an area where more accurate simulation is 

needed. The local-scale, or “child model,” developed for 

this study was coupled to a modified version of the regional 

groundwater flow simulation documented by Bexfield and others (2011). Initially, the local-scale model grid was designed 

to couple with the “shared node” method (LGR1) of local-grid 

refinement (Mehl and Hill, 2005). While this coupling method 

was satisfactory for a steady-state simulation and several early 

transient stress periods, the method failed during subsequent 

transient stress periods because the simulated water-table 

decline caused “drying out” and deactivation of finite-difference cells along the parent–child model interface that are 

required for numerical coupling. Fortunately, an alternative 

code, LGR2 (Mehl and Hill, 2013), became available, which 

uses a new “ghost node” method of local grid refinement 

(Dickinson and others, 2007). Testing with a beta-version 

of LGR2 was eventually successful in coupling the local- to 

regional-scale models during all transient stress periods and 

was subsequently used for all groundwater flow simulations.

In flow simulations with the coupled regional-scale and 

local-scale flow models, regional-flow computations within 

the area encompassed by the local-model (fig. 2) are deactivated in the regional model. When using LGR2, heads 

within each model and fluxes across the interface between 

the two models are iteratively computed for each time step 

by both regional (parent) and local-scale (child) models until 

satisfactory convergence between the values computed by 

both models is achieved. This numerical coupling effectively 

specifies the transient heads around the perimeter of the localscale model.

After model calibration produced a finalized set of flowmodel parameters, the calibrated local-scale flow simulation 

could be run independently of the regional-scale model using 

the Boundary Flow and Head Package (BFH2) of MODFLOW-LGR (Mehl and Hill, 2013) and a previously saved 

file containing specified-head boundary conditions along the 

perimeter of the local-scale model. Running the decoupled 

local-scale model is faster and convenient if the model must 

be rerun to produce the head and cell-by-cell flow files 

required for subsequent particle tracking and solute-transport 

simulations.

Specification of initial carbon-14 concentrations was 

required for the local-scale solute-transport model. Although it 

could be advantageous to couple a local-scale transport simulation to the regional-scale transport simulation documented 

in appendix 1 of this report, a local-grid-refinement method 

for solute transport (analogous to MODFLOW-LGR) is not 

currently available. Accordingly, the initial carbon-14 concentrations required for the local-scale transport model were 

interpolated from concentrations simulated for the steady-state 

stress period of the regional-scale carbon-14 transport simulation documented in appendix 1.

Modification of the Regional Groundwater Flow 

Model

Recent reprocessing and subsequent reinterpretation 

of seismic data combined with modeling of gravitational 

anomalies have provided a revised interpretation of the 

spatial distribution of thickness of alluvial-fill sediments in 

the MRGB (Grauch and Connell, in press). The thickness of 

finite-difference layers in the model previously documented in 

Bexfield and others (2011) was revised somewhat to simulate 

this new interpretation of the depth of alluvial-fill sediments. 

As depicted in figure 5, the ranges of steady-state saturated 

thickness of layers 2, 3, 4, and 5 are 15–23, 30–47, 65–103, 

and 24–184 m, respectively. The base of layer 5 is at an 

altitude 244 m below the Rio Grande, and it maintains that 

altitude perpendicular to the trend of the river except where 

basement rock is at a higher altitude near the basin perimeter. 

Layers 6 and 7 have constant thicknesses of 183 and 305 m, 

respectively, except near the basin perimeter, where layerbottom altitudes rise with the basement rock and the thickness 

of these layers thins to about 30 meters. The top of layer 8 is at 

an altitude 732 m below the Rio Grande, except near the basin 

perimeter, where the layer-bottom altitude also rises, causing 

a range in layer thickness from 15 to 1,532 m. The thickness 

of layer 9 ranges from 32 to 3,064 m. Cells in layers 1–9 are 

active where the base of the cell is higher than the base of 

the Santa Fe Group basin fill. Because the greatest thickness 

changes occur in the deepest layer (regional model layer 9), 

within which the simulated hydraulic conductivity is small, the 

changes in simulated regional groundwater flow are relatively 

minor.

The temporal discretization of the regional model was 

refined by replacing a stress period representing the autumn 

of 2007 with three stress periods, as described in more detail 

in the following section. This modification was required to 

enable coupling with the local-scale model, which must have 

identical time discretization. 

When the revised regional-scale model is coupled to 

the embedded local-scale model with LGR2, the portion of 

the regional model domain encompassed by the local-scale 

model is deactivated, and that portion of the model domain is 
12 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells Rio Grande

WEST EAST

Land surface

Predevelopment water table

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 6

Layer 7

9 – 119 meters thick

15 – 23 meters thick

30 – 47 meters thick

65 – 103 meters thick

24 – 184 meters thick

183 meters thick

305 meters thick

15 – 1,532 meters thick

32 – 3,064 meters thick

Layer 8

Layer 9

Screened

interval

Typical publicsupply well

completion

Deep publicsupply well

completion

Model base

Not to scale Pre-Santa Fe Group

basement rock

Figure 5. Layer-thickness ranges of the modified regional flow model.
Methods for Simulation of Groundwater Flow and Solute Concentrations 13

represented by the hydraulic properties and stresses simulated 

in the corresponding local-scale model. Because hydraulic 

properties simulated with the local-scale model typically differ from those simulated in the corresponding portion of the 

regional model, the regional model is effectively modified in 

the refined area when coupled to the local-scale model.

Local-Scale Model Geometry and Discretization

The local-scale model fully penetrates and replaces a 

12-row by 8-column portion of the regional-model domain 

in which groundwater flow within the 24-km2 area of the 

detailed study area is simulated. With respect to the regional 

model, the local-scale model domain is discretized with a 

5:1 horizontal refinement ratio, resulting in a 40 by 60 grid of 

square cells, each 100 m on a side. Each model cell therefore 

encompasses an area of 0.01 km2. The 9 layers in the regional 

model are refined vertically into 45 child-model layers. The 

vertical refinement ratios of parent-model to child-model 

layers is variable, with no refinement of the top two and bottom three parent-model layers and substantial refinement of 

parent-model layers 3 and 4 (table 2). The relatively thin 3-m 

thickness of child-model layers 3–34 was required to simulate 

hydraulic-conductivity variability within the hydrogeologicfacies realizations described previously. Child-model layers 

35–42 incorporate coarser vertical-refinement ratios that 

transition layer thicknesses to the substantial aquifer intervals 

represented by the unrefined layers 43–45.

The temporal discretizations of the regional- and localscale models are identical. Conditions prior to 1900 are 

represented by a steady-state stress period, which is followed 

by transient stress periods that simulate the 109 years through 

December 31, 2008. Fifteen 5-year stress periods simulate 

the time from 1900 to 1974, and fifteen 1-year stress periods 

Table 2 Child-model refinement with respect to regional 

model, Albuquerque Basin, New Mexico. 

[Layers numbered downward from the top; m, meters]

Regional

layer1

Vertical

refinement ratio Local layers Local-layerthickness (m)

1 1:1 1 9.0–119

2 1:1 2 15.2–15.6

3 10:1 3–12 3.05

4 22:1 13–34 3.05

5 5:1 35–39 24.5

6 3:1 40–42 61.0

7 1:1 43 305

8 1:1 44 675–991

9 1:1 45 1351–1983

1

Regional model layering documented in Bexfield and others (2011).

simulate the time from 1975 through 1989. Following a 

74-day stress period representing the winter of 1990, seasonal stress periods representing time after March 15, 1990, 

generally simulate 230-day “irrigation season” periods that 

extend from March 16 through October 31 and 135-day (or 

136-day during leap years) “winter” periods that extend from 

November 1 through March 15. The 5-year, 1-year, 230-day, 

and 135-day stress periods are divided into 8, 6, 5, and 4 time 

steps, respectively. The time-step lengths were specified with 

a geometric time-step-length multiplier of 1.5, resulting in an 

initial time-step length of about 17 days for the annual and 

seasonal stress periods.

The 136-day “winter” period that simulates conditions 

from November 1, 2007, through March 15, 2008, was divided 

into three stress periods of 31 days, 1 day, and 104 days to 

enable accurate simulation of non-pumping and pumping 

periods before and during collection of wellbore-flow data in 

the SSW.

Local-Scale Groundwater-Flow Model 

Boundary Conditions

Specified flux and head-dependent flux boundaries in 

the local model were used to simulate vertical recharge and 

groundwater withdrawals. Similar to the regional-scale model 

(Bexfield and others, 2011), recharge through the unsaturated 

zone in the area simulated by the local model was assumed to 

be insignificant prior to urbanization. Accordingly, recharge 

to and withdrawals from the local model were assumed to be 

zero during the steady-state stress period, which represents 

time prior to 1900. Anthropogenic recharge that was simulated 

for times after 1900 may have resulted from concentrated 

water sources that have the potential to wet portions of the 

vadose zone sufficiently to cause downward migration of 

water through the vadose zone, resulting in recharge at the 

underlying water table.

Recharge

Although the magnitude and distribution of water infiltrating to the water table is uncertain, the presence of tracers of young (post-1950s) groundwater, such as tritium and 

chlorofluorocarbons, suggests that such recharge does occur. 

Likely sources of recharge water include leakage from sewer 

and water-distribution pipelines, infiltration from an unlined 

stormwater diversion channel, infiltration beneath ponds, and 

infiltration of excess irrigation to urban turf and park areas. 

The distribution of these sources within the local-scale model 

area (fig. 6) was specified using zonation in the MODFLOW 

recharge package. The magnitude of recharge flux associated 

with each source was estimated by parameterization during 

model calibration. Although the simulated total recharge is 

reasonable, the relative contributions from each source, while 

reasonable, are not well constrained and thus uncertain.
14 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

Recharge Zone

Pipeline areas

EXPLANATION

Diversion Channel

Ponds

Park

Leak since 1900

Leak since 1950

Leak since 1970

Leak since 1991

35°06'

35°05'30"

35°05'

35°04'30"

35°04'

35°03'30"

35°03'

35°02'30"

106°39'30" 106°39' 106°38'30" 106°38' 106°37'30" 106°37'

0 1 MILE

0 1 KILOMETER

Figure 6. Distribution of simulated recharge sources in the local-scale model, Albuquerque, New Mexico.
Methods for Simulation of Groundwater Flow and Solute Concentrations 15

Leakage from sewer and water-distribution pipelines

The areas susceptible to recharge from leaky sewer and 

water-distribution pipelines were specified in a fashion similar 

to that used for the regional-scale model (Bexfield and others, 

2011). Geographic information system (GIS) databases of 

the extent of the Albuquerque Metropolitan area in the years 

1935, 1951, 1973, and 1991 (Feller and Hester, 2001) were 

intersected with GIS databases of the City of Albuquerque 

water-distribution and sewage-pipe systems to approximate the service areas that existed during four time periods: 

1900–49, 1950–69, 1970–90, and 1991–2007. All areas of 

service during one of the four time periods were considered 

equally susceptible to recharge due to leakage from the waterdistribution and sewage-pipe systems. Although leakage from 

the water-distribution and sewage-pipe systems occurs at the 

discrete locations where breakage occurs and changes through 

time, these locations are unknown. This recharge is therefore 

simulated as a diffuse source over a broader area than the 

more focused sources that occur in reality (but with unknown 

locations).

The magnitude of recharge in the specified possible 

pipe-leakage areas was parameterized as a fraction of the 

total reported groundwater withdrawals for each stress period, 

including those in the coupled regional model. This fraction, 

which was constant throughout the transient simulation, was 

estimated by model calibration to be about 3.3 percent. The 

recharge simulated from leaky pipes was about 600 cubic 

meters per day (m3/d), or 13 percent of the total simulated 

recharge within the local model for the year 1988. In comparison to the simulated pipe leakage, during a water-loss 

analysis (New Mexico Environmental Finance Center, 2006) 

for the Albuquerque Bernalillo County Water Utility Authority (ABCWUA), it was estimated that about 18,650 m3

/d of 

“unaccounted-for water” may be due to “undetectable” and 

“detectable” distribution leakage throughout the ABCWUA 

service area, which was about 4.8 percent of the ABCWUA 

groundwater withdrawals during 2005.

Channel Leakage

The South Diversion Channel in Albuquerque was 

constructed in 1968 (Peterson, 1992) to provide flood protection by diverting stormwater to Tijeras Arroyo (fig. 1). Within 

the local model area, the unlined portion of this channel from 

which leakage is simulated during and after the year 1970 is 

depicted in figure 6. Although a gage exists where the diversion channel discharges into Tijeras Arroyo, there is not a gage 

on the upper reach of the channel with which to calculate flow 

loss along the channel. The average leakage from the channel within the local model area was estimated during model 

calibration to be 1,297 m3

/d, or about 0.5 cubic feet per second 

(ft3

/s). This leakage equates to an average flux on the order 

of 10 centimeters per day over the unlined area of the channel. For comparison, measured discharge from the channel at 

Tijeras Arroyo between June 8, 1988, and March 25, 2008, 

varied from 0 to 230 ft3

/s. Although the recharge simulated 

from this channel was about 29 percent of the total simulated 

recharge into the local model for 1988, this quantity is poorly 

constrained and likely smaller in reality, with a correspondingly greater portion of recharge actually coming from leaking 

pipes.

Infiltration from Ponds and Urban Turf Areas

The location of the duck pond west of the library on the 

University of New Mexico Campus, which was simulated as 

a source of recharge, is shown in figure 6. The vegetated areas 

in several parks, fields, and other urban turf areas, which are 

occasionally watered, are also depicted. The recharge simulated from ponds and urban turf areas was about 58 percent of 

the total simulated recharge into the local model for 1988.

Groundwater Withdrawals

Groundwater-withdrawal records were obtained from the 

New Mexico Office of the State Engineer, ABCWUA, and 

Bjorklund and Maxwell (1961). Because groundwater-withdrawal data prior to the 1960s were limited, earlier withdrawal 

rates from ABCWUA and University of New Mexico wells 

were extrapolated from later records (Kernodle and others, 

1995). Withdrawals from other commercial wells were specified only in years for which records were available. Consequently, model-simulated withdrawals may under-represent 

actual withdrawals.

Reported historical groundwater withdrawals since 1900 

from 33 commercial and public-supply wells within the local 

model area were simulated as head-dependent flux boundaries with the revised Multi-Node Well Package (MNW2) of 

MODFLOW (Konikow and others, 2009). Use of MNW2 

was preferred over the specified-flux Well Package (WEL) 

in this simulation because the simulated screened intervals 

of the withdrawal wells spanned many finite-difference cells, 

among which differences in simulated hydraulic conductivity 

exist. Although the total reported withdrawal is specified for 

each well with MNW2, the layer-by-layer distribution of each 

specified withdrawal depends on the hydraulic conductivities 

and heads in each of the finite-difference cells connected to the 

simulated withdrawal well.

The contribution from each of the three major publicsupply wells within the local study area to the total daily 

withdrawal from the three wells is depicted with a different 

color in figure 7, which illustrates well-field operation over 

a typical 1.5-year period from July 1, 2007, to December 31, 

2008. Turbine pumps in the public-supply wells typically run 

at a constant rate for the time required to supply the daily 

withdrawal from the well. During times of peak demand, a 

supply well such as PSW1 may be in operation constantly for 

many days. Public-supply wells may also be in a non-pumping 

status for extended periods during times of reduced demand 

or if operations are limited by water-quality factors or other 

constraints.
16 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

2007 2008

0

5

10

15

20

25

30

35

Thousands of cubic meters per day

Public-Supply Well 3 

Studied-Supply Well 

Public-Supply Well 1

EXPLANATION

Figure 7. Stacked column chart of daily withdrawals from public-supply wells in the local study area.

The total quantity of reported withdrawals that were 

simulated in the local-scale and regional (exclusive of the 

local-scale domain) models for each stress period is depicted 

in figure 8. Until about 1940, all of the reported withdrawals 

from the MRGB were within the Albuquerque area and were 

encompassed by the local-scale model domain. The effect of 

urban growth and consequent expansion of municipal well 

fields and industrial and other wells into areas surrounding the 

local study area is shown by the greater proportion of pumpage from the regional model (exclusive of the local-scale 

domain) after about 1950.

Zonation of Simulated Hydraulic Properties

The spatial distribution of vertical and horizontal hydraulic conductivities control the initial (steady-state) hydraulic 

head configuration, and, together with specific storage and 

specific yield, the response to transient stresses, such as 

pumpage and recharge. Model parameters representing these 

hydraulic properties were initially assigned values equal to 

those in the corresponding portion of the regional model. 

Within the portion of the model domain simulated with the 

TPROGS hydrogeologic-facies realization, values of hydraulic 

conductivities for the coarse and fine hydrofacies were constrained to be higher and lower, respectively, than the intermediate facies. Estimation of the values of parameters representing hydraulic properties within the local model is discussed in 

a subsequent section of the report on model calibration.

Local-Scale Transport Simulation

The transport of carbon-14 (14C), tritium (3H), and three 

chlorofluorocarbon (CFC) species—trichlorofluoromethane 

(CFC-11), dichlorodifluoromethane (CFC-12), and trichlorotrifluoromethane (CFC-113)—was simulated with the groundwater solute-transport code MT3DMS (Zheng and Wang, 1999; 

Zheng, 2010). Measurements of the concentrations of each of 

these species in groundwater samples, in addition to measured 

water-level data, were used to simultaneously calibrate the 

local-scale groundwater flow (MODFLOW) and the transport 

(MT3DMS) simulations.
Methods for Simulation of Groundwater Flow and Solute Concentrations 17

0

100

200

300

400

500

19001910192019301940195019601970198019902000

Groundwater withdrawals, in thousands of cubic meters per day

600

700

Groundwater pumpage from regional model 

Groundwater pumpage from local-scale model 

EXPLANATION

Years

Figure 8. Reported groundwater withdrawals within the local-scale model domain and within the regional-model domain (exclusive of 

the local-scale model domain).

dispersion equation:

Governing Equation

MT3DMS numerically solves the transient advection-

( ) ( ) 











θ

θ θ

∂

∂ = ∂

∂

∂

∂ − ∂∂

+ +

C

t x

D

C

x x

v C q C R

k

i

ij

k

i i

i

k

s s

k

n

 , (1)

where 

	 θ is the effective porosity, 

C k is the concentration of the dissolved chemical 

species k, 

t is time, 

x is distance, 

i and j are indices referencing a Cartesian coordinate 

system, 

D is the hydrodynamic dispersion tensor, 

vi is the seepage velocity, 

qs is the volumetric flow rate of fluid sources 

and sinks, 

Cs

k is the concentration of dissolved chemical 

species k in the fluid sources or sink, and 

Rn is a chemical reaction term.

The advection term of equation 1 was solved with the 

mass conservative third-order total-variation-diminishing 

(TVD) scheme of MT3DMS (Zheng and Wang, 1999). 

Although some numerical dispersion occurs in the solution of 

equation 1 with TVD, the calibration of model-simulated to 
18 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

observed concentrations was not improved with simulation of 

additional hydrodynamic dispersion. Accordingly, the dispersion package of MT3DMS was not used to simulate additional 

dispersion.

Because the three CFC species (CFC-11, CFC-12, and 

CFC-113) are not known to decay significantly in the oxic 

groundwater conditions which exist along most transport paths 

from the water table to sampling wells, they were simulated as 

conservative tracers, with no reaction term (Rn in equation 1). 

For the simulation of 14C and 3H, the reaction term represents 

their respective radioactive decays.

Specified-Concentration Boundary Conditions

The concentrations of 14C, 3H, and each CFC species 

in water that infiltrates into the vadose zone near the ground 

surface were assumed to be equilibrated with their respective 

atmospheric concentrations, each of which are known to vary 

with time because of variable production rates (for 14C, 3H, 

and the CFCs) and radioactive decay (for 14C and 3H). Because 

the CFC transport rate through the vadose zone by gaseous 

diffusion was unknown, it was assumed to be less than 1 year. 

Accordingly, the atmospheric concentrations of CFCs (fig. 9) 

0

100

200

300

400

500

600

1950196019701980199020002009

Chloroflurocarbon (CFC) concentration, in parts per trillion

Year

CFC-12 

CFC-11 

CFC-113 

EXPLANATION

Figure 9. Atmospheric concentrations of chlorofluorocarbons (CFC-11, CFC-12, and CFC-113) above Albuquerque, New Mexico, 

1950–2010.

ing water molecules, and 14C, which is present as inorganic 

carbon dissolved within infiltrating water, likely require a 

longer time interval to transit the vadose zone before entering 

the saturated zone in recharge at the water table. During this 

time interval, the concentration of 3H, which has a half-life 

of 12.32 years, may substantially decrease due to radioactive decay. Although the change in 14C concentration due to 

radioactive decay is relatively minor during this time interval, 

the concentrations of 3

H and 14C at the water table may differ 

significantly from their atmospheric concentrations because 

the atmospheric concentrations of 14C (fig. 10) and 3H (fig. 11) 

are strongly time dependent due to the effects of atmospheric 

nuclear testing. The effect of this delay on simulated concentrations was tested for vadose-zone transit times of zero, 10, 

and 20 years by comparing simulated concentrations from 

simulations using specified 14C and 3

H recharge concentrations 

equal to the atmospheric concentration (for an “instantaneous” 

vadose-zone transit time) and corrected for radioactive decay 

during 10- and 20-year vadose-zone transit times. Although 

actual transit times likely vary across the study area, a uniform 

delay was simulated for the entire model domain. Because 

were used to specify the CFC concentrations in recharge at the 

water table. In contrast, 3

H, which is a component of infiltrat-
Methods for Simulation of Groundwater Flow and Solute Concentrations 19

90

100

110

120

130

140

150

160

170

180

190

1950196019701980199020002009

Percent modern carbon

Year

Atmospheric carbon-14 

10-year transport 

20-year transport 

EXPLANATION

Figure 10. Atmospheric carbon-14 concentration and specified concentrations for 10- and 20-year transport to the water table, 

Albuquerque, New Mexico, 1950–2009.

0

5

10

15

20

25

30

35

1950196019701980199020002009

Atmospheric tritium concentration, in hundreds of tritium units

Year

Atmospheric tritium 

10-year transport 

20-year transport 

EXPLANATION

Figure 11. Atmospheric tritium concentration above Albuquerque, New Mexico, and specified concentration for 10- and 20-year 

transport to the water table.
20 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

parameters representing delay time and the effective porosity 

in the saturated zone were correlated, the delay time could not 

be reliably determined through model calibration. Bexfield 

and others (2012) estimated that unsaturated zone travel times 

ranged from 8 to 20 years in the local study area. A delay time 

of 10 years was used to specify the 14C and 3H concentrations 

in recharge in this simulation. 

Specified-Concentration Initial Conditions

Initial concentrations for the three CFC species (CFC11, CFC-12, and CFC-113), which are anthropogenic and 

synthesized after 1945, were simply specified as equal to zero 

throughout the model domain. Prior to 1945, the “pre bomb 

era” background atmospheric 3H concentration was about 

4 parts per trillion (ppt; Michel, 1989). The subsequent radioactive decay of this small background concentration while 

it was transported through the vadose and saturated zones 

ensured concentrations were negligible throughout the aquifer 

system. Accordingly, the initial 3H concentration was also 

specified as equal to zero throughout the model domain. 14C concentrations implying groundwater residence times 

up to about 50,000 years have been measured in groundwater samples from wells in the MRGB (Sanford and others, 

2004). The predevelopment distribution of 14C concentration 

throughout the local study area is unknown but likely was 

spatially variable and a function of the groundwater residence 

time at each location. The importance of specifying a “reasonably accurate” 14C initial condition throughout the local-scale 

model domain motivated the regional 14C transport simulation documented in appendix 1. 14C concentrations within the 

local-scale model domain were interpolated from the simulated concentrations for the steady-state stress period of the 

regional simulation, which represents carbon-14 concentration 

during predevelopment time. The simulated initial carbon-14 

concentration distribution within the top regional and localscale model layers (fig. 12), which contain the predevelopment 

water-table results from the simulated convergence of relatively young water (still generally more than 1,000 years old) 

that recharged from the Rio Grande (to the north and west) 

and at the base of the Sandia Mountains (to the east). The shallow portion of the local study area contains a zone of mixing 

of waters that originated from these sources, with older waters 

simulated near the middle and southern parts of the local-scale 

model domain. 

The simulated initial carbon-14 concentrations from 

regional model layer 5 (fig. 13), which corresponds to localscale model layers 35–39 and simulates the aquifer interval 

from about 125 to 246 m deep, are much lower than the simulated initial 14C concentrations in the top layer of the model, 

reflecting longer simulated groundwater residence times at 

greater depths. Younger water that is simulated as recharging 

along the eastern mountains also enters the eastern side of the 

local-scale model domain at these greater depths, but younger 

water from the Rio Grande does not appear in layer 5.

Model Calibration

The local-scale flow and solute-transport models were 

simultaneously calibrated to obtain the best overall agreement 

between measured heads and age-tracer concentrations and 

their corresponding values simulated with MODFLOW and 

MT3DMS, respectively. This calibration was accomplished 

by a non-linear regression procedure that adjusted the modelparameter values to minimize the overall differences between 

the observed quantities and their simulated equivalents; the 

sum of those differences is quantified by the objective function 

(eq. 2). More specifically, the parameter-estimation code PEST 

(Doherty, 2004) adjusted the local-scale flow and transportmodel parameters in table 3 in order to minimize the objective 

function:

∑∑ ( ) ω ω− + − ∑ ( ) ω ω = = = i i C Ci i' ' H H

i

N

j

k k k k

11 k

5 2

1

172 2

 , (2)

where

H is the measured water level for observation k,

H′ is the simulated-equivalent water level for 

observation k, 

C is the concentration measured for observation 

i of chemical species j, 

C′

i is the simulated-equivalent concentration for 

observation i of chemical species j,

ωi is the weight applied to concentration 

observation i and its simulated equivalent, 

	 ωk is the weight applied to head observation k, 

N is the number of observations for each of 

the five chemical species simulated with 

MT3DMS, and 

172 is the number of water-level observations.

Concentration Observations

Concentrations of the five chemical species (tracers of 

groundwater age) measured in samples from wells (fig. 14) 

were used for model calibration. Bexfield and others (2012) 

categorized the sampled wells into four categories based on 

well type (monitoring or public supply) and the well-screened 

interval depths (table 4). The monitoring-well screen lengths 

are all less than 21.3 m, and most are less than 12.2 m. The

shallow monitoring wells are screened across the water table 

or have a screen midpoint within 18.3 m of the water level in 

the well. The 13 intermediate monitoring wells have a screen 

midpoint between 27.1 and 79.5 m below the water level in the 

well. The two deep monitoring wells have a screen midpoint 

185 m or more below the water level in the well. The three 

public-supply wells have screened intervals at least 190 m in 

length that include the depth intervals characterized by the 

intermediate and deep monitoring wells. The 25 wells sampled 

for these concentration observations compose the majority of 

the 28 wells from which water-level observations were used in 
Methods for Simulation of Groundwater Flow and Solute Concentrations 21

EXPLANATION

Simulated carbon-14 concentration–

in percent modern carbon

24 - 30

31 - 40

41 - 50

51 - 79

Rio Grande

35°06'

35°05'30"

35°05'

35°04'30"

35°04'

35°03'30"

35°03'

35°02'30"

106°39'30" 106°39' 106°38'30" 106°38' 106°37'30" 106°37'

0 1 MILE

0 1 KILOMETER

Figure 12. Regional and local-scale finite difference cells, with carbon-14 concentration interpolated from the regional transport 

simulation and specified as initial concentration for the top layer of the local-scale model, Albuquerque, New Mexico.
22 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

EXPLANATION

Simulated carbon-14 concentration–

in percent modern carbon

8.4 - 20.0

20.1 - 26.6

35°06'

35°05'30"

35°05'

35°04'30"

35°04'

35°03'30"

35°03'

35°02'30"

106°39'30" 106°39' 106°38'30" 106°38' 106°37'30" 106°37'

0 1 MILE

0 1 KILOMETER

Figure 13. Carbon-14 concentration interpolated from layer 5 of the regional transport simulation and specified as initial concentration 

for layers 35–39 of the local-scale model, Albuquerque, New Mexico.
Methods for Simulation of Groundwater Flow and Solute Concentrations 23

Table 3. Summary of parameters representing hydraulic property values in the local scale model in Albuquerque, New Mexico.

[HK, horizontal hydraulic conductivity; VK, vertical hydraulic conductivity; HANI, horizontal anisotropy (ratio of HK along columns to HK along rows); 

VANI, vertical anisotropy (ratio of VK to specified HK); SS, specific storage; SY, specific yield; n, effective porosity; Fm, Formation; m/d, meters per day; 

m-1, per meter]

Parameter name Type Description Initial value Calibrated value

K_Qr HK River alluvium in layers 1, 2 2.4 m/d 8.5 m/d

K_QTsa HK Sierra Ladrones Fm. in layers 1, 2 13.7 m/d 22.7 m/d

K_fine HK Fine facies in layers 3–34 0.3 m/d 0.23 m/d

K_moderate HK Intermediate facies in layers 3–34 7 m/d 16.9 m/d

K_coarse HK Coarse facies in layers 3–34 46 m/d 121 m/d

K_Tcrp HK Ceja Fm: Rio Puerco member in layers 35, 36, 38, 39 2.4 m/d 1.22 m/d

K_Tcrp2 HK Ceja Fm: Rio Puerco member in layers 37, 40 2.4 m/d 3.35 m/d

Ksdm HK Ceja Fm: Atrisco member in layers 41–44 0.46 m/d 0.46 m/d

Ksdfm HK Fine Sand in layer 45 0.015 m/d 0.015 m/d

Haniyes HANI Layers 1, 2, 35–45 2:1 2.66 : 1

Hanino HANI Layers 3–34 1:1 1:1

Vani1 VANI Layers 1, 2 1:1 39:1

Vani2 VANI Layers 35–45 15:1 13:1

VK_coarse VK Coarse facies in Layers 3–34 9.1 m/d 34.3 m/d

VK_moderate VK Moderate facies in Layers 3–34 0.3 m/d 0.76 m/d

VK_fine VK Fine facies in Layers 3–34 0.03 m/d 0.09 m/d

SSall SS All layers 6.6 x 10-6 m-1 5.25 x 10-6 m-1

SYall SY All layers 0.2 0.12

porosity n All layers 0.215 0.08

model calibration. Details of the sampling and analysis of the 

chemical species are documented in the companion report by 

Bexfield and others (2012).

Observations of carbon-14 and tritium concentrations 

from all of the 25 sampled wells were used. CFC concentration observations denoted as “not determined” in table 4 were 

not used for model calibration because either (1) the measured concentration was higher than what is possible from 

atmospheric input, indicating contamination from other CFC 

sources, (2) the ratio of a CFC to other CFC species indicated 

that it likely was contaminated, although its concentration 

was not higher than possible from atmospheric input, or (3) 

the measured CFC concentration was very low compared to 

measurements of the other CFCs in the same well, indicating 

degradation of that species.

The estimated errors associated with each concentration 

observation were used to assign the weights (ωi

) in the objective function (eq. 2) used for the model calibration. For each 

chemical species, various error estimates were converted to a 

measurement variance, the inverse of which defined the initial 

measurement weight. The weights within a group of measurements of each chemical species were then multiplied by a factor to correct for different measurement units. Thus, concentration observations with greater measurement or laboratory 
24 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

SFMW-46

SSW

MW7

MW2

FP4

FP3

FP2

FP1

PSW3

PSW1

MNW-4-D1 MW8

1,512

1,511

1,510

1,509

1,508

1,507

1,506

1,505

1,504

1,512

1,511

1,510

1,509

1,508

1,507

1,506

1,505

1,514

1,513

1,512

1,511

1,510

1,509

1,508

1,507

1,506

1,505

1,503

P83-19

0 1 MILE

0 1 KILOMETER

N

P83-19

1,504

EXPLANATION

Contour of simulated predevelopment water-table 

altitude–In meters

Age-tracer observation well location and number 

Rio Grande

Figure 14. Boundary of the local-scale model, locations of age-tracer observation wells, and predevelopment water-table altitudes 

simulated with the local-scale flow model, and with the coupled regional-scale flow model, Albuquerque, New Mexico. (See table 4 for 

observation well information.)
Methods for Simulation of Groundwater Flow and Solute Concentrations 25

Table 4. Concentration observations used to calibrate the local-scale flow and transport model in Albuquerque, New Mexico.[14C, carbon-14; pmc, percent modern carbon; TU, tritium units; CFC, chlorofluorocarbon; pptv, parts per trillion by volume; DD, depth-dependent sample, followed by depth of collection; WH, wellhead; 

sqrwt, Square root of weight; nd, not determined; m, meters]

Well name

(fig. 14)

Well 

category

Sample 

date

(mm/dd/

yyyy)

Sample time 

sample type

14C activity

(pmc)

14C 

(sqrwt)

Tritium

concentration

(TU)

Tritium(sqrwt)

CFC-11

concentration

(pptv)

CFC-11

(sqrwt)

CFC-12

concentration

(pptv)

CFC-12

(sqrwt)

CFC-113

concentration

(pptv)

CFC-113

(sqrwt)

FP1D Deep 6/10/2007 1400 Regular 11.71 1.3 0.02 2.75 2.9 0.37 nd nd 3.3 0.49FP1MD Deep 6/8/2007 1400 Regular 15.04 1.18 0.00 2.75 nd nd nd nd 3.0 0.29FP1MS Intermediate 6/6/2007 1500 Regular 51.22 0.75 0.52 2.75 70.1 0.02 nd nd 20.5 0.07FP1MS Intermediate 11/10/2008 1130 Regular 43.86 0.75 1.19 2.75 nd nd 113.2 0.02 3.8 0.12FP2D Intermediate 6/17/2007 1500 Regular 72.85 0.64 6.03 1.25 nd nd 67.0 0.03 0.0 0.36FP2M Intermediate 6/16/2007 1400 Regular 86.65 0.57 5.51 1.38 37.0 0.04 nd nd 7.8 0.21FP3D Intermediate 6/14/2007 1400 Regular 68.06 0.67 7.79 0.95 nd nd nd nd 19.4 0.08FP3D Intermediate 11/13/2008 1400 Regular 73.21 0.67 6.63 1.15 nd nd 159.8 0.01 4.5 0.36FP3M Intermediate 6/13/2007 1500 Regular 59.61 0.66 0.04 2.75 23.0 0.07 8.2 0.06 5.9 0.24FP4D Intermediate 11/24/2008 1215 Regular 67.73 0.79 7.12 1.08 46.1 0.04 nd nd 6.7 0.19FP4M Intermediate 11/25/2008 930 Regular 67.48 0.69 4.07 1.08 203.7 0.01 nd nd 46.8 0.04MW7 Intermediate 12/18/2007 1500 Regular 52.85 0.71 0.02 2.75 1.1 1.46 42.2 0.03 0.1 0.35MW8 Intermediate 12/18/2007 1045 Regular 57.73 0.68 0.17 2.75 nd nd nd nd 71.7 0.02MNW4-D1 Intermediate 2/20/2008 1630 Regular 56.29 0.79 1.40 2.75 nd nd nd nd 6.3 0.08SFMW-46 Intermediate 1/15/2008 1145 Regular 71.60 1.42 4.10 1.78 nd nd nd nd 8.4 0.12P83-19LR Intermediate 1/30/2008 1530 Regular 30.65 1.54 2.24 2.75 nd nd nd nd nd ndP83-19M Intermediate 1/31/2008 1300 Regular 66.35 0.57 4.90 1.58 nd nd nd nd nd ndFP1S Shallow 6/5/2007 1400 Regular 67.03 0.65 2.26 2.75 nd nd nd nd 36.8 0.05FP2S Shallow 6/15/2007 1500 Regular 75.06 0.98 5.73 1.3 116.1 0.01 nd nd 37.0 0.05FP3S Shallow 6/12/2007 1300 Regular 50.22 0.83 0.25 2.75 nd nd nd nd nd ndFP3S Shallow 11/7/2008 1500 Regular 48.59 0.75 0.13 2.75 nd nd nd nd nd ndFP4S Shallow 11/25/2008 1445 Regular 55.34 0.66 1.46 2.75 nd nd nd nd nd ndMW2 Shallow 12/19/2007 1100 Regular 68.26 0.94 1.65 2.75 nd nd nd nd 66.3 0.03MW9 Shallow 1/21/2008 1445 Regular 51.69 1.26 0.84 2.75 nd nd nd nd 35.5 0.05P83-19U Shallow 1/29/2008 1315 Regular 68.30 1.06 5.08 1.48 nd nd nd nd nd ndPSW1 Public supply 11/12/2008 1500 Regular 43.71 0.83 1.35 2.75 nd nd nd nd 38.5 0.04PSW3 Public supply 11/13/2008 930 Regular 10.12 1.4 0.12 2.75 4.3 0.03 10.6 0.04 1.7 0.33SSW Public supply 6/7/2007 1400 Regular 43.77 0.79 2.35 2.75 nd nd 192.2 0.01 5.4 0.14SSW Public supply 11/10/2008 930 Regular 31.20 0.88 0.96 2.75 nd nd 33.2 0.05 0.6 0.33SSW Public supply 12/4/2007 1200 DD, 115 m 21.99 1.05 0.54 2.75 nd nd 15.8 0.1 0.1 1.14SSW Public supply 12/4/2007 1400 DD, 133 m 21.51 1.03 0.64 2.75 nd nd 18.0 0.08 0.5 1.05SSW Public supply 12/4/2007 1600 DD, 151 m 25.23 0.94 0.77 2.75 nd nd 86.3 0.02 10.5 0.08SSW Public supply 12/4/2007 1700 DD, 176 m 11.65 1.42 0.14 2.75 nd nd 28.3 0.06 3.6 0.16SSW Public supply 12/5/2007 1300 DD, 241 m 9.69 1.54 0.06 2.75 nd nd 0.0 0.17 0.3 5.57SSW Public supply 12/5/2007 1500 DD, WH 24.49 1.06 0.70 2.75 nd nd 7.6 0.22 0.3 0.31
26 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

analytical error had smaller weights in the parameter-estimation regression. Tritium weights were calculated from the measurement uncertainties reported by Bexfield and others (2012, 

table 10), which are largely attributable to laboratory analytical error. Carbon-14 weights were calculated from variances 

associated with analytical error and the geochemical modeling 

by Bexfield and others (2012), which introduced the majority 

of the uncertainty in the “observed” carbon-14 values. The 

estimated variance for each modeled carbon-14 observation 

was based on two assumptions: (1) a normal distribution and 

(2) a 90-percent confidence that the true value lies between the 

measured value and the “best” modeled value. CFC weights 

were calculated from variances associated with analytical error 

and the uncertainty in the recharge temperature and altitude for 

calculation of the “observed” CFC atmospheric mixing ratios 

reported by Bexfield and others (2012). 

Water-Level Observations

Measurements of water levels from the public-supply, 

multiple-well nests (FP1, FP2, FP3, and FP4), and other 

wells within the local study area (fig. 15) were compiled for 

model calibration. Wells lacking screened-interval information or with only one water-level measurement were not used. 

Measurements from the final set of 28 observation wells were 

sampled to construct a calibration dataset with a maximumlength period of record for each well and no more than one 

water-level observation from each well per model stress 

period.

The uncertainties associated with measurement of wellhead altitudes and the depth to water in the observation well 

were converted to variances and summed to obtain an error 

variance associated with each water-level measurement. The 

inverse of these composite variances were used for the weights 

(ωk ) in the objective function (eq. 2) used for the model 

calibration. Several water-level observations from wells with 

long screened intervals were assigned a “penalty” to lower 

the measurement weight because of uncertainty in the effect 

of water levels at different aquifer depths on the composite 

measured water level.

For observation wells with screens that span multiple 

model layers, the Head Observation Package (HOB) of MODFLOW requires specification of the relative contribution of the 

simulated head in each model layer to the simulated equivalent 

water level for the observation well. Because this information 

was unknown, the relative contributions of each model-layer 

finite-difference cell connected to an observation well screen 

were assumed to be equal during model calibration. In order to 

test the effect of the initial simplifying assumption, the cell-towell conductance (CWC) for each cell connected to an observation well with a relatively long screened interval (PSW3) 

was computed by using the calibrated model parameters, and 

a revised model-layer apportionment was incorporated into a 

new HOB input package. For the tested observation well, the 

maximum difference between the composite water levels simulated by using these two approaches was 17 centimeters (cm). 

The model-calibration regression was subsequently rerun to 

obtain the final model parameter set.

Calibrated Model Parameter Values

The calibrated parameter values representing the horizontal hydraulic conductivities of the fine and intermediate 

TPROGS facies are lower and higher, respectively, than the 

horizontal hydraulic conductivities reported in the AquiferPumping Test section of this report. Because the pumping test 

withdrew water from all facies connected to the pumping-well 

screen and the measured water-level changes used for analysis 

of the test might depend upon the hydraulic conductivities of 

all these facies, hydraulic conductivities estimated from the 

test might be expected to have values that represent an aggregate of hydraulic conductivities of the constituent facies.

Sensitivity analysis is inherent in model calibration with 

a program such as PEST, which computes the sensitivity of 

each model parameter being estimated during each parameterestimation iteration. At the calibrated model parameter values, 

simulated-equivalent values to the observation dataset were 

most sensitive to parameters representing effective porosity 

and recharge.

Simulated Water Levels

The local-grid-refinement method ensures continuity of 

simulated heads across the interface between the local- and 

regional-scale models. Contours of predevelopment watertable altitudes simulated for steady-state conditions within 

the local-scale model align with contours simulated with the 

coupled regional-scale model (fig. 14). Prior to the onset of 

groundwater pumping, groundwater flowed from the northeast 

toward the southwest through the local-model area, and some 

groundwater discharged to the Rio Grande, located immediately west of the local study area. The direction of groundwater flow simulated in deeper model layers is similar to that at 

the water table for predevelopment time. 

Numerous public-supply and commercial wells are 

east of the local study area (Bexfield and others, 2012), and 

increased groundwater withdrawals in these areas since about 

1950 have changed the direction of groundwater flow in the 

local study area from the predevelopment direction. Water 

levels simulated for times after the early 1950s indicate that 

the direction of groundwater flow above an altitude of about 

1,200 m is generally from the west toward the east. Figure 15 

depicts water-table altitudes simulated by child-model layer 

37 and parent-model layer 5, which represent depths within 

the withdrawal-depth interval of Albuquerque public-supply 

wells. The finite-difference-cell centers, or nodes, in this layer 

of the local-scale model represent altitudes between 1,320 m 

and 1,329 m (near the southwest and northeast corners of the 

model grid, respectively). The simulated pressure head in the 

aquifer interval represented by this model layer varies from 

157 to 177 m.
Simulated Water Levels 27

CtyObs1

MW5

32.411

ESYale2

MW3

1,498

1,497

1,496

1,495

1,494

1,493

1,492

1,491

1,490

1,489

1,488

1,487

1,486

1,484

1,483

1,482

1,481

1,497

1,496

1,495

1,494

1,493

1,492

1,491

1,490

1,489

1,488

1,487

1,486

1,485

1,484

1,483

1,482

1,481

1,498

1,497

1,496

1,485

1,495

1,494

1,493

1,491

1,492

1,489

1,488

1,487

1,486

1,485

1,484

1,483

1,490

1,482

1,478

1,4801,479

1,497

SFMW-46

20.214

SSW

FP3

SJ9

FP2

FP1

PSW3

PSW1

MNW-4

P83-19

SJ3Obs

BEB

JM_2

0 1 MILE

0 1 KILOMETER

N

P83-19

1,504

EXPLANATION

Contour of simulated predevelopment water-level 

altitude–In meters

Age-tracer observation well location and number 

Rio Grande

Figure 15. Boundary of the local-scale model, locations of water-level observation and public-supply wells, water-table altitudes 

simulated for December 31, 2009, with layer 37 of the local-scale flow model, and with layer 5 of the coupled regional-scale flow model, 

Albuquerque, New Mexico.
28 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

Hydrographs of observed water levels and their simulated 

equivalents depict the quality of the calibrated model as well 

as the history of water-level decline at several depths for locations within the local-model area. Observation well CityObs1, 

located near the north boundary of the study area, has a screen 

open to the aquifer in the altitude interval from 1,467 to 

1,470 m, or about 43 m below land surface. Historical water 

levels measured in CityObs1 document water-table lowering at 

this location since about 1960 (fig. 16). The simulated-equivalent hydrograph for these observations, which depicts the 

average of water levels simulated in child model layers 8 and 

1,495

1,500

1,505

1,510

1960

CtyObs1

1,485

1,490

1,495

1,500

PSW3

1,485

1,490

1,495

1,500

ESYale2

1,485

1,490

1,495

1,500

SJ3Obs

1,485

1,490

1,495

1,500

Water-level altitude, in meters (NAVD 88)

SJ9

65 70 75 80 85 90 95 2000 05 2009

Year

EXPLANATION

Observed 

Simulated

Figure 16. Observed and simulated water levels for public-supply and observation wells in the local model, Albuquerque, New Mexico.

9 at the model location corresponding to City Obs1, depicts 

trends and magnitudes of changes similar to the observations 

from this well.

Observation well ESYale2, near the south boundary of 

the study area, has a screen open to the aquifer from 76 to 

about 91 m below land surface. Measured water levels during the relatively short period of record in the 1980s record a 

lowering of the water table in this area and are simulated by 

the model to within 1 m.

Observation well SJ3Obs, located north of the Albuquerque International Airport in the southern part of the study area, 
Simulated Intra-Borehole Flow Log 29

is screened over an interval from about 110 to 153 m below 

land surface. The simulated water-levels depicted in the hydrograph for well SJ3Obs are the average of the water levels 

simulated in the eight vertically adjacent child-model finitedifference cells corresponding to the observation well screened 

interval. Although the simulated hydrograph fits the observed 

water-level decline over the 13-year period of record, some 

of the observed shorter period (possibly seasonal) water-level 

variations are not simulated.

Observation well SJ9, in the southwest part of the study 

area, is screened over a relatively long interval from about 

55 to 233 m below land surface. The simulated water-levels 

depicted in the hydrograph for this well are the average of the 

water levels simulated in 27 vertically adjacent child-model 

finite-difference cells corresponding to the observation well 

screened interval. The simulated hydrograph is similar to that 

for observation well SJ3Obs in that it adequately represents 

the long-term water-level decline but does not simulate the 

observed shorter period water-level variations, which appear 

to be seasonal. It is possible that water levels in observation 

well SJ9 respond to seasonal variations in nearby groundwater 

pumpage that are not accurately specified in the model or to 

variations in the stage of the nearby Rio Grande that may not 

be accurately simulated in the regional model.

Well PSW3 is a public-supply well from which withdrawals are simulated in the local-scale model. As such, there 

are several additional considerations in the interpretation of 

water levels associated with this well. Although the observed 

water levels are 5 m higher than their simulated equivalents, 

these water levels were measured after the pump in the well 

had been turned off for several days to weeks, allowing water 

levels in the well to recover somewhat toward the ambient 

head conditions in the surrounding aquifer. In contrast, the 

model-simulated water levels are calculated for the time of 

the observation during a model stress period in which pumpage from the well is also simulated by the model. Because 

this water-level recovery time cannot be simulated with the 

seasonal stress periods employed during the observation times, 

the simulated water levels are somewhat lower than their 

observed counterparts. The solid line in figure 17 depicts the 

water level simulated within the wellbore of PSW3 by MNW2 

for a 34-year time period represented by 54 stress periods. 

This calculation of wellbore head is distinct from the “simulated equivalent” heads (depicted as square points in figure 17) 

that are calculated by the HOB by use of spatial and temporal 

interpolation between nodal heads for the location and time 

of the head observation, and the calculation is a more accurate representation of the head in the well while it is pumped. 

During stress periods in which a relatively small quantity 

of water is pumped, such as those for late 2008 to 2009, the 

MNW2-simulated wellbore head is close to that simulated by 

the HOB.

The fit of all simulated to observed water levels used for 

model calibration, including those from 23 wells not shown 

in the hydrographs (fig. 16), is depicted in figure 18. The 

reasonably random distribution of the scatterplot around the 

1:1 line suggests that there is no appreciable bias in the model. 

Simulated Intra-Borehole Flow Log

Depth-dependent data on borehole flow in the SSW collected on December 1 and 2, 2007, (Bexfield and others, 2012) 

provided an additional calibration constraint on the vertical 

distribution of horizontal hydraulic conductivity in the localscale flow model. Measurements of borehole flow at discrete 

depths were made under “ambient” conditions, when the SSW 

had not been pumped for the previous 47 days. Subsequent 

borehole-flow measurements made several minutes after 

activation of the submersible pump in the well and again after 

several hours are plotted separately as the measured fraction 

of the total pumped flow as a function of depth (fig. 19). These 

plots depict the cumulative flow distribution during the early 

transient hydraulic response of the aquifer to the pumping and 

during a quasi-steady flow regime that may exist after pumping has continued for several hours. The greatest difference 

between the “early observation” and “late observation” plots 

occurs at shallow depths because the logging tool was trolled 

downward and then upward; thus, the greatest difference in 

time between observations at specific depths occurs at shallower depths. Whereas the plot of the “early observation” log 

indicates that flow is entirely into the well (fraction of the total 

flow decreases with depth) above a depth of 145 m shortly 

after pumping commences, the plot of the “late observation” 

log indicates that some return flow out of the well (fraction 

of the total flow increases and decreases with depth) occurs 

within this depth interval after the flow field has had sufficient 

time to stabilize.

Model stress periods were designed to simulate the periods of non-pumping and the intra-borehole flow test from the 

SSW. Head-dependent flows simulated for the time of the test 

into each node representing the screened interval of the SSW 

were used to generate the “simulated” flow log for the SSW 

(fig. 19), which more closely resembles the “late observation” 

plot. The form of the simulated cumulative-flow plot was most 

sensitive to the parameters K_Tcrp and K_Tcrp2 (table 3), 

which represent the horizontal hydraulic conductivity of the 

Rio Puerco member of the Ceja Formation within local-model 

layers 35–40 that are connected to the simulated SSW below 

a depth of 177 m. Because simulated SSW nodal flows were 

not readily incorporable into the model-calibration regression, 

the K_Tcrp and K_Tcrp2 parameter values were manually 

calibrated and subsequently constrained during the final model 

calibration with PEST.
30 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

1975

1,475

1,485

Water-level altitude, in meters (NAVD 88)

80 85 90 95 2000 05 2008

Year

Water level simulated in SSW 

Observed water level

EXPLANATION

Water level simulated in PSW3 

Simulated equivalent to observation 

Observed water level

EXPLANATION

1,485

1,490

1,495

1,500

1,490

1,495

1,500

1,475

1,480

1,480

Figure 17. Water levels observed in the studied supply well (SSW) and PSW3, and simulated with the local model, Albuquerque, New 

Mexico.

1,480

1,490

1,500

1,510

Observed water-level altitude, in meters (NAVD 88)

1,480 1,490 1,500 1,510

Simulated water-level altitude, in meters

1:1 line

Figure 18. Relation between water levels observed and 

simulated with the local model, Albuquerque, New Mexico.

Simulated Concentrations 

of Carbon-14, Tritium, and 

Chlorofluorocarbons 

Simulated-equivalents to observed concentrations for 

each of the five chemical species were computed with the 

Transport Observation Package (TOB) of MT3DMS. The 

TOB is analogous to the HOB of MODFLOW; concentrations 

simulated at finite-difference-cell centers are spatially interpolated for the location of the observation well and temporally 

interpolated between transient transport-time steps for the time 

of the observation. Observation wells with screens that span 

multiple model layers require specification of the relative contribution of the simulated concentration in each screen-penetrating model layer to the simulated equivalent concentration 

for the observation well. Because the relative contributions 

from various depth intervals to the measured concentrations 

were unknown, the simulated concentration contributions of 

each transport model-layer finite-difference cell to the simulated observation concentration were assumed to be equal.
Simulated Concentrations of Carbon-14, Tritium, and Chlorofluorocarbons 31

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

100 150 200 250 300 350

Fraction of total flow

Depth, in meters

Simulated 

Early observation 

Late observation 

EXPLANATION

Figure 19. Simulated and observed flow logs for the studied supply well (SSW), Albuquerque, New Mexico.

Carbon-14

The graph of the relation between simulated carbon-14 

concentrations and observed carbon-14 values for each welldepth category (fig. 20) illustrates that the general trend of 

decreasing observed carbon-14 concentrations at greater aquifer depths is well simulated. In comparison to a similar plot of 

observed carbon-14 concentrations and carbon-14 concentrations simulated with a regional transport simulation (fig. 1-2), 

simulation of carbon-14 concentrations in shallow and intermediate depth observation wells, in particular, is improved 

in the local-scale simulation. This improvement likely results 

from incorporation of the heterogeneous hydraulic conductivity distribution and additional sources of recharge to the water 

table in the local-scale model.

Although the carbon-14 residual pattern (fig. 20) appears 

random, individual depth classes of wells appear to show 

some bias in their residual distributions, possibly indicating a 

systematic model error. For example, the observed carbon-14 

is typically greater than that simulated for the intermediatedepth observation wells. This bias may result from error in 

the carbon-14 initial condition at intermediate depths, or the 

existence of faster transport pathways to the intermediatedepth observation wells than are simulated by the model. 

(The Model Uncertainty and Limitations section discusses 

such pathways, which include transport through the wellbores 

and the gravel pack in the annular volumes surrounding the 

wellbores.) In contrast, the observed carbon-14 is typically 

less than that simulated for the public-supply wells, which 

may result, in part, from error in the specification of the relative contributions of model layers (assumed to be equal) to the 

simulated concentration for the well.

The transport code MT3DMS can simulate flux-weighted 

concentrations within wells with long screened intervals, such 

as the SSW, that were simulated as multi-node wells with 

the corresponding MODFLOW simulation. These concentrations simulated for multi-node wells with MNW2-simulated 

wellbore head differ from the TOB simulated-equivalent 
32 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

0

20

40

60

80

100

Observed carbon-14 concentration, in percent modern carbon

Simulated carbon-14 concentration, in percent modern carbon

0 20 40 60 80 100

Shallow 

Intermediate 

Deep 

Public supply

Well category

EXPLANATION

1:1 line

Figure 20. Relation between observed carbon-14 concentrations 

and concentrations simulated with the model of the local study 

area in Albuquerque, New Mexico.

concentrations for reasons analogous to the differences 

between MNW2-simulated water levels and those calculated 

by the HOB package of MODFLOW that were mentioned in 

the Simulated Water Levels section of this report. The simulated carbon-14 concentration within the SSW as a function 

of time is depicted with two observed carbon-14 concentrations in figure 21. Differences between seasonal groundwater 

withdrawal rates were simulated for periods after 1990, resulting in noticeable simulated-concentration fluctuations after 

that time. Carbon-14 concentrations were higher during the 

summer, when more water is withdrawn from the SSW. The 

peak simulated carbon-14 concentration appears in the SSW 

during 1985, which is about 21 years after the 1964 peak of 

atmospheric carbon-14 concentration and about 11 years after 

the peak concentration of the transient carbon-14 boundary 

condition specified for a 10-year transport-delay through the 

vadose zone (fig. 10). 

Tritium

The fit of simulated 3H concentrations to their observed 

values for each well-depth category is depicted in figure 22. 

Although there is a reasonable correspondence of simulated to 

observed concentrations for the deep and some of the shallow- and intermediate-depth observation wells, other tritium 

observations are relatively poorly simulated. In particular, some intermediate and most public-supply wells with 

observed 3H concentrations greater than 0.4 tritium units (TU) 

have simulated concentrations near zero, suggesting that some 

preferential pathways for young-water transport to deeper-well 

screened intervals may not be represented in the model.

The simulated 3

H concentration within the SSW is plotted 

with time and with three observed 3H concentration measurements in figure 23. The peak in the simulated 3

H concentration 

appears in the SSW during 1981, which is about 18 years after 

the 1963 peak atmospheric 3H concentration and 8 years after 

the peak concentration of the 3H transient boundary condition 

specified for a 10-year transport delay through the vadose zone 

(fig. 11). The apparent 3-year difference in arrival times of 

the peak concentrations for 3H and carbon-14 is an artifact of 

the flow and transport model temporal discretization; 5-year 

stress periods were employed prior to 1975, and specified 

boundary concentrations were averaged over the length of the 

stress period. Thus, the simulated peak concentration for 3H 

was specified beginning in 1970, and the simulated time for 

arrival of peak 3H concentration in the SSW is about 11 years. 

Because the actual time required for transport through the 

vadose zone is unknown and likely spatially and temporally 

variable, little additional uncertainty was introduced by this 

temporal discretization artifact. 

Chlorofluorocarbons

A plot of the relation between simulated and observed 

CFC-113 concentrations (fig. 24) illustrates that although the 

model cannot precisely simulate most observed CFC concentrations, there is reasonable correspondence for many shallow 

and intermediate observation wells. Some observation and 

public-supply wells with observed CFC-113 concentrations 

greater than 3 pptv have simulated-equivalent concentrations 

near zero. As discussed previously for a similar mismatch of 

simulated to observed 3H concentrations, the discrepancy may 

be due to preferential pathways for young-water transport 

to deeper-well screened intervals that are not represented in 

the model. For the same reason, the plot showing simulated 

and observed CFC-12 concentrations (fig. 25) also illustrates 

that the simulated-equivalent concentrations to many CFC12 observations in public-supply wells are near zero. Several 

CFC-12 measurements from intermediate-depth observation 

wells are reasonably well simulated, however. Although fewer 

CFC-11 concentrations were deemed valid for analysis, the fit 

of simulated and observed CFC-11 concentrations (fig. 26) is 

similar to that for CFC-113 (fig. 24). A geochemical/age-dating analysis (Bexfield and others, 2012) found that although 

CFCs were useful in indicating the depths to which young 

water had been transported, the potential for CFC contamination limited the use of the CFCs in determining individual 

interpreted ages. 

The maximum concentrations simulated in the SSW for 

all three CFC species appear during 1994 to 1995. Unlike 

the atmospheric concentrations of carbon-14 and 3

H, which 

exhibit distinct bomb-induced temporal variability (figs. 10 

and 11), the specified boundary concentrations of the three 

CFC species vary more gradually and attain near-maximum 
Simulated Binary Age Mixtures in and Surrounding the SSW 33

December 5, 2007

24.49 percent 

modern carbon

November 10, 2008

31.2 percent 

modern carbon

December 5, 2007

24.49 percent 

modern carbon

Simulated carbon-14 concentration 

Observed carbon-14 concentration 

with date of sample 

EXPLANATION

20

1975 80 85 90 95 2000 05 2008

25

30

35

40

Percent modern carbon

Year

Figure 21. Carbon-14 concentrations observed and simulated in the studied supply well in Albuquerque, New Mexico.

values by about 1991 (fig. 9). Plots of the CFC species through 

time (fig. 27) have similar forms because of their identical 

source locations and transport pathways but differ in magnitude because of their respective source concentrations (fig. 9). 

The faster apparent transport times of simulated peak CFC 

concentrations during the 1990s, as compared to those for 

3

H and carbon-14 during the 1970s, may be due to the larger 

groundwater withdrawal rates (fig. 8) and resulting larger 

hydraulic gradients in the vicinity of the SSW during the 

1990s.

Simulated Binary Age Mixtures in and

Surrounding the SSW

The relative proportions of young and old waters in 

supply wells are of interest because of the different types of 

contamination that may be present in water that originated 

from different sources or that has resided in the aquifer for 

different lengths of time. In the case of the SSW, the young 

water fraction may contain VOCs of anthropogenic origin, 

whereas the older fraction is more likely to be associated with 

the natural contaminant arsenic. Although public-supply wells 

with deeper screened intervals may withdraw groundwater 

with a smaller fraction of young water, and therefore be less 

susceptible to anthropogenic contaminants (such as VOCs), 

these wells may contain higher concentrations of natural 

contaminants if they are associated with older water. In the 

local study area, arsenic concentrations generally increase with 

depth (Bexfield and others, 2012), implying association with 

older, deeper groundwater. The fractions of “young,” post1950 groundwater, which is transported from the water table 

downward into the SSW screened interval, and of distinctly 

older groundwater that is transported upward from beneath the 

SSW screened interval may vary seasonally as pumping rates 

and consequent vertical hydraulic gradients change. Because 

groundwater of intermediate age surrounds the SSW, the time 

variation in the fractions of “young” and “old” groundwater 

with respect to this “intermediate” mixture were simulated, 

and are discussed separately.
34 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

0

1

3

5

7

8

Observed tritium concentration, in tritium units

Simulated tritium concentration, in tritium units

0 1 2 3 4 5 6 7 8

Shallow 

Intermediate 

Deep 

Public supply

Well category

EXPLANATION

2

4

6

1:1 line

Figure 22. Relation between observed tritium concentrations 

and concentrations simulated with the model of the local study 

area in Albuquerque, New Mexico.

Simulated Fraction of Young Groundwater in 

and Surrounding the SSW

 The time-dependent fraction of “young water” throughout the local-scale model was simulated with the transport 

code MT3DMS by considering “young water” as a conservative tracer that recharges the groundwater system after 1950. 

For this simulation, an initial zero-concentration condition 

was specified to represent water that resided in the saturated 

zone prior to 1950. The percentage of “young” water in watersupply wells that were simulated as multi-node wells, such as 

the SSW, can be depicted in a similar fashion as done for the 

five chemical tracers (figs. 21, 23, and 27). The percentage 

of simulated “post-1950” water in the SSW over the 38-year 

interval ending in 2008 (fig. 28) attained maximum values of 

about 12 percent between about 1985 and 1995, which is when 

groundwater withdrawals in the local study area were larger 

than in subsequent years (fig. 8). 

As groundwater withdrawals from the SSW decreased 

somewhat from the late 1990s onward, greater differences in 

the simulated percentages of “young” water between seasons result from greater differences in seasonal withdrawals. 

Simulated percentages of young water typically vary from a 

low of about 4 percent during the lower-pumping wintertime 

periods to about 8 percent during the higher-pumping summertime periods. This seasonal difference results from greater 

simulated downward flow from sediments above the SSW 

screen during the summer pumping season. During the winter, 

water-level observations from the FP1 nest of piezometers, 

located slightly east of the SSW (fig. 2) indicate either a 

smaller downward or an upward hydraulic gradient, both of 

which imply less flow of younger water to the SSW during the 

winter season. The simulated fractions of younger water generally agree favorably with those from the independent mixing 

analysis (Bexfield and others, 2012) that did not directly incorporate pumping rates and ranged between 3 percent and 

6 percent in the winter to 11 percent in the summer. 

The similarity between the simulated “post-1950” 

percentages (fig. 28) and the carbon-14 concentrations in the 

SSW (fig. 21) results from the similarity of their source boundary condition and transport pathways. A noticeable difference 

after about 1995 is that the relative carbon-14 concentration 

does not decrease as much as the relative decrease in the percentage of “post-1950” water after groundwater-withdrawal 

rates declined. This is due to the contribution of carbon-14 

from the deeper aquifer intervals where a finite initial concentration was specified.

The simulated percentage of young, or “post-1950,” 

water in the SSW borehole and a sampling of the model cells 

penetrated by the SSW is depicted over a shorter, 2.8-year 

interval in figure 29. The percentage of young water generally 

decreases with the increasing aquifer depth simulated in higher 

numbered model layers. The amplitude of the time-variation 

of the simulated “post-1950” percentage primarily depends on 

the hydraulic properties controlling flow (hydraulic conductivity and storage), transport (effective porosity), and, to a lesser 

extent, the simulated depth represented by the cell. At the 

location of the SSW, model cells in layers 5, 6, 7, and 30 are 

simulated by the highest hydraulic-conductivity, coarse facies 

(table 3) that is most effective in transmitting the younger 

fraction from the water table, resulting in larger variations 

of simulated “post 1950” water between stress periods with 

differing pumping rates. The model cells in layers 12 and 22 

are simulated with the “intermediate” hydraulic-conductivity 

facies (table 3), and the simulated variation of the fraction 

of young water in those cells is smaller. Although layers 21 

and 34 are simulated with the lowest hydraulic-conductivity 

facies (table 3), layer 21 nevertheless exhibits subdued variations in the simulated fraction of younger water. There is no 

substantial variation in the young-water fraction in layer 34 

or deeper model layers because little of the younger water is 

simulated to advect to the deeper aquifer intervals. Figure 29 

also illustrates the temporal correspondence between the variations in percentage of young water in the borehole of the SSW 

(figs. 28 and 29) and the variations within the model cells 

contributing water to the simulated SSW. 

A borehole-flow log of the SSW (Bexfield and others, 

2012) indicates that flow through the borehole occurs under 

ambient (non-pumping) conditions. Interpreted variations in 

the age distribution of waters sampled from the SSW may 

be due, in part, to preferential flow through the borehole or 

gravel-filled annular space of the SSW during periods of low 

or no pumping. Because substantial withdrawals occurred 

from the SSW during each of the seasonal stress periods simulated by the transient simulation documented in this report, no 
Simulated Binary Age Mixtures in and Surrounding the SSW 35

December 5, 2007, 0.70 Tritium units

June 7, 2007

2.35 Tritium units

November 10, 2008

0.96 Tritium units

December 5, 2007

0.70 Tritium units

Simulated tritium concentration 

Observed tritium concentration 

with date of sample 

EXPLANATION

0

1975 80 85 90 95 2000 05 2008

5

10

15

25

20

Tritium concentration, in tritium units

Year

Figure 23. Tritium concentrations observed and simulated in the studied supply well in Albuquerque, New Mexico.

such period of low or no pumping from the SSW was simulated, and consequently no substantial flow was simulated out 

of the SSW into any model layer. The simulated variations 

in the young-water fractions among various model layers 

and the SSW borehole, such as those depicted and discussed 

for figure 29, therefore result from differing proportions of 

relatively young water migrating through the simulated aquifer 

material during different stress periods. 

The borehole-flow logs (fig. 19), which were measured 

following a period of no pumping and water-level recovery, 

depict flow out of the SSW into the aquifer in discrete aquifer intervals. Although the details of these flows could not be 

simulated with the seasonal time discretization employed for 

this simulation, more detailed temporal discretization would 

enable more accurate simulation of periods of low pumping and no pumping, which could improve the simulation of 

mixing of borehole water returned to the aquifer with that 

in the surrounding aquifer, thereby enabling further analysis 

of the effects of borehole-return flow on the simulated age 

distribution of water surrounding the borehole. Quantification of the relative effects of borehole-return flow versus 

aquifer-advective flow on the age-distribution variations could 

also help assess their relative importance as contaminanttransport pathways.

Simulated Fraction of Old Groundwater in the 

SSW

The seasonal variation in the fraction of deeper, “old” 

groundwater was simulated by a similar procedure to that 

previously described for the “young,” post-1950 fraction. In 

the vicinity of the SSW, relatively high arsenic concentrations are associated with groundwater below a depth of 250 m 

(Laura Bexfield, USGS, written commun., 2012). To simulate the transport of this deeper fraction into the SSW, initial 

concentrations for the MT3DMS simulation were specified as 

zero within the upper model layers 1 through 37 that represent 

depths above about 250 m, and as 1 within the deeper model 

layers 38 through 45.

The simulated fraction of water originating below 250 m 

in the SSW (fig. 30) is a function of the simulated withdrawal 
36 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

0

10

30

50

70

0 10 20 30 40 50 60 70

20

40

60

Shallow 

Intermediate 

Deep 

Public supply

Well category

EXPLANATION

Observed chlorofluorocarbon-113 concentration, 

in parts per trillion

Simulated chlorofluorocarbon-113 concentration, 

in parts per trillion

1:1 line

Figure 24. Relation between simulated and observed CFC-113 

concentrations, Albuquerque, New Mexico.

0 20 40 60 100 80 120 160140 180 200

20

40

60

80

100

120

140

160

180

200

Intermediate 

Public supply

Well category

EXPLANATION

0

Observed chlorofluorocarbon-12 concentration, in parts per trillion

Simulated chlorofluorocarbon-12 concentration, in parts per trillion

1:1 line

Figure 25. Relation between simulated and observed CFC-12 

concentrations, Albuquerque, New Mexico.

Observed chlorofluorocarbon-11 concentration, in parts per trillion

40

60

80

100

140

180

200

220

0

20

120

160

Simulated chlorofluorocarbon-11 concentration, in parts per trillion

0 20 40 80 60 100 120 140 180 160 200 220

Shallow 

Intermediate 

Deep 

Public supply

Well category

EXPLANATION

1:1 line

Figure 26. Relation between simulated and observed CFC-11 

concentrations, Albuquerque, New Mexico.

rate from the SSW, which varies between the seasonal stress 

periods. The larger simulated variability in 2007 is caused 

by the finer temporal discretization used to simulate the 

period before and during an aquifer test and flow log in the 

SSW. During the summer seasons, when withdrawal rates 

are typically larger, a larger volume of the water pumped 

from the SSW is withdrawn from the upper, generally higher 

hydraulic-conductivity layers, resulting in smaller simulated 

fractions of “old” water during summer seasons. In contrast, 

smaller volumes withdrawn from the upper, generally higher 

hydraulic-conductivity layers during the winter result in a 

higher fraction of “old” water simulated in the SSW during the 

winter seasons. These simulated variations are consistent with 

the seasonal variability of observed arsenic concentrations in 

water samples from the SSW (Bexfield and others, 2012).

Simulated VOC Plume

The chlorinated solvents trichloroethylene (TCE) and 

cis-1,2-dichloroethylene (cis-1,2-DCE) and the gasoline oxygenate methyl tert-butyl ether (MTBE) were detected in low 

concentrations in groundwater produced by the SSW (Bexfield 

and others, 2012, table 8). Of the three known contaminantsource locations within the local study area (fig. 2), a Superfund site (U.S. Environmental Protection Agency, 2011) to 

the northwest of the SSW is upgradient along simulated flow 

paths and, therefore, is a possible source of contaminants to 
Simulated VOC Plume 37

December 5, 2007,

7.6 parts per trillion

June 7, 2007

5.4 parts per trillion

November 10, 2008

33.2 parts per trillion

June 7, 2007

December 5, 2007

Simulated CFC-12 

Simulated CFC-11 

Simulated CFC-113 

Observed CFC-12 with date of sample

Observed CFC-113 with date of sample 

EXPLANATION

0

1975 80 85 90 95 2000 05 2008

5

10

15

65

25

20

30

35

40

45

55

50

60

Atmospheric mixing ratio, in parts per trillion

Year

Figure 27. Simulated and observed CFC concentrations in the studied supply well in Albuquerque, New Mexico.

the SSW. The chlorinated solvents tetrachloroethene (PCE), 

TCE, and cis-1,2-DCE were detected in groundwater wells 

near this site and in soils on this site in 1989 (U.S. Environmental Protection Agency, 2001). The solvents likely leaked 

from onsite storage tanks associated with a laundry established 

in 1924 and (or) a dry-cleaning facility operated from 1940 

through 1972. Chlorinated solvents are dense, non-aqueous 

phase liquids (DNAPLs) which generally sink through fresh 

groundwater until they dissolve into the groundwater and (or) 

adsorb onto the sedimentary aquifer material. Groundwater 

wells at and near the site may have provided preferential conduits for the chlorinated solvents to migrate to deeper aquifer 

intervals (Landin, 1999).

Flow paths from this possible contaminant-source site to 

the SSW were studied by using the particle tracking program 

MODPATH (Pollock, 1994), and the transient flow field was 

computed with the calibrated local-scale groundwater-flow 

model documented in this report. For these simulations, 

particles representing parcels of groundwater containing dissolved contaminants were distributed across the downgradient 

faces of the top seven model cells that contain the relatively 

shallow groundwater well at the contaminant-source site. The 

particles were tracked forward in time along the simulated 

advective groundwater-flow paths until the simulated end 

date (Dec. 31, 2008), unless they either stopped at the perimeter boundary of the local-scale model or were stopped at a 

simulated well. Because the origination date and the duration 

of contaminant leakage were unknown, many simulation scenarios with different contaminant-release dates and simulated 

effective porosities were explored. Because the simulated 

rates of pumping from withdrawal wells and directions of 

groundwater flow changed during this transient simulation, 

the flow paths and fate of the particles were sensitive to the 

particle release date and the effective porosity specified in 

the particle-tracking algorithm, which controls the particle 

velocities. Particles released prior to about 1963 generally 

track the groundwater flow toward the south-southwest and 

sometimes toward withdrawal wells to the north-northeast. 

Subsequently, the drawdown of groundwater levels caused by 

withdrawals from public-supply wells inside and to the east of 
38 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

0

1975 80 85 90 95 2000 05 2008

1

2

3

13

5

4

6

7

8

9

11

10

12

Percent of post-1950 water

Year

Figure 28. Simulated percentages of post-1950 water in the SSW, Albuquerque, New Mexico, 1975–2005.

the local-scale model area direct the advective-transport flow 

paths toward the southeast. Because water-quality samples 

from locations south of the source were not obtained until after 

1989, by which time the changed groundwater-flow direction 

might have flushed this area with clean water, the possibility 

of earlier release times cannot be ruled out. The preponderance 

of contaminated samples between the source and the SSW 

suggests that scenarios with relatively direct flow paths to 

the SSW reasonably represent the actual contaminant plume. 

Actual contaminant flow paths, however, are likely affected by 

“on and off” supply well pumping cycles that are not simulated with the seasonal time discretization of the flow simulation. For simplicity, two example simulations with different 

constant effective porosities will be discussed.

For the first example, the leakage was simulated by 

“releasing” particles from the source area once a year for 

14 years (1975 until 1989) with the effective porosity (8 percent) that was estimated from calibration of the solute-transport model. A plot of the horizontal component of all particle 

flow paths in this scenario (fig. 31) illustrates that particles 

either exit the local-scale model domain in the easternmost 

model column or are withdrawn through the well PSW1, and 

none arrive at the SSW. A groundwater “capture zone” created 

by withdrawals from PSW1, in particular, prevents the simulated flow paths of the contaminants from reaching the SSW in 

this example.

In the second example, particles that were released in 

1976 were simulated with an effective porosity of 1.1 percent 

(fig. 32). In this case, some particles have sufficient velocity to 

avoid the capture zones of the intervening public-supply wells 

(PSW1 and PSW3) and are drawn into the SSW. Particle paths 

that terminate in the SSW have shallow flow paths near PSW3 

and pass over the cells that contain the simulated screened 

interval of PSW3. Interestingly, the VOCs that were observed 

in the SSW and PSW1 were not observed in PSW3 (Bexfield 

and others, 2012, table 8). It is possible that the screened interval of PSW3 is isolated from shallower contaminant pathways. 

If that is the case, the pathways that terminate at the cell containing PSW3 in figures 31 and 32 may not be representative 

of the actual paths of VOCs from the depicted source.

Although the relatively small simulated effective porosity of 1.1 percent is likely less than the predominant effective 
Simulated VOC Plume 39

Model layer 5

Model layer 7

Model layer 6

Model layer 21

Model layer 12

Model layer 30

Model layer 22

Borehole

Model layer 34

Percent post-1950 water

2006

M A J A M J S NO D J F M A J A M J S NO D J F M A J A M J S NO D

10

20

30

40

50

60

70

80

90

0

2007 2008

Figure 29. Simulated percentages of post-1950 water in the aquifer surrounding the SSW, Albuquerque, New Mexico.
40 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

0.3

2000 01 02 03 04 05 06 07 2008

0.4

0.5

0.6

0.8

0.7

0.9

Simulated fraction of water originating 

more than 250 meters below land surface

Year

Figure 30. Simulated fraction of water originating more than 

250 meters below land surface in the studied supply well in 

Albuquerque, New Mexico.

porosity in the aquifer, it may be representative for some 

preferential pathways, such as along sedimentary structures, 

that cannot be represented at a scale of spatial discretization in 

the model. In reality, an ensemble range of effective porosities 

likely exists in the aquifer. 

Model Uncertainties and Limitations

In addition to the specification of boundary conditions, 

the simulated groundwater levels and transport of dissolved 

chemical tracers depend upon the specified spatial distribution 

of hydraulic properties controlling flow (hydraulic conductivity and storage) and transport (effective porosity). In this 

model, the spatial distribution of hydraulic conductivity has 

been simulated by one specific realization from a geostatistical 

hydrogeologic-facies model. Geostatistical properties defining characteristic spatial extents of the three hydrogeologic 

facies that compose the hydrogeologic-facies model, and the 

hydraulic properties associated with each of them, are uncertain. Although the specified distribution of the coarse, intermediate, and fine facies in model layers 3–34 does conform to 

the lithologies observed at the borehole-control locations, it 

is only intended to represent their relative abundance and the 

probable frequency of facies transitions at locations between 

those borehole locations. The amount of interconnection 

between the coarse facies, which are simulated by model cells 

with high hydraulic conductivities, in particular, likely has a 

substantial influence on the effectiveness of chemical-tracer 

and contaminant transport. Although the thickness and frequency of transition between facies were characterized in the 

vertical dimension at the borehole control points, the length, 

width, and interconnectedness of the coarse facies are not well 

constrained. Comparison of the longitudinal extents of the 

interconnected coarse-facies model cells with the longitudinal 

extents of channel bottoms in the present-day Rio Grande and 

Rio Puerco suggests that the longitudinal coarse-facies interconnectedness is under-represented in the model. Future use 

of a depositional-process-based stratigraphic simulator could 

enable an improved representation of hydrostratigraphy in a 

similarly scaled model.

A regression calibration using a second hydrogeologicfacies realization resulted in slightly different parameter 

values. Because of the long transient-model runtimes, particularly for the multi-species transport model, it was not practical 

to calibrate the hydraulic-model parameters for each of the 

ten hydrogeologic facies (TPROGS) distributions. Forward 

groundwater-flow model runs and subsequent particle-tracking 

runs were made for all 10 realizations with an identical set 

of parameter values to ascertain if substantial differences 

in either simulated heads or transport pathways might be 

simulated with different realizations. Differences in simulated 

water levels between realizations were similar in magnitude to 

the residuals between the simulated and observed water levels 

for the calibrated realization.

Although the seasonal (230-day or 135-day) stress 

periods adequately simulate the different pumping rates 

which result in changing horizontal and vertical hydraulic 

gradients at the wellfield to a 6-km scale in the model, finer 

borehole-scale flows due to changing pumping rates over 

shorter periods are not well represented. The inter-day variability of withdrawals from the SSW and other public-supply 

wells, such as depicted in figure 7, likely causes differences 

in the hydraulic gradients and consequent flow into different 

vertical sections along the wellbore. The intra-borehole flow 

log (fig. 19) provided one measure of the vertical variability 

of flow into and out of the SSW following a transition from a 

non-pumping to pumping condition. Model testing using daily 

stress periods with different pumping rates has indicated that 

intra-borehole flows, such as those measured by the intraborehole flow log, and the associated solute transport between 

model layers with different hydraulic heads can be simulated 

with the MNW2 package. Further simulations using daily 

stress period data could aid in discerning the relative importance of intra-borehole flow versus flow through the aquifer 

matrix in causing observed seasonal differences in chemicaltracer concentrations in the SSW.

The positive residuals for tritium and CFCs, with simulated values near zero and less than their observed counterparts, may indicate that some preferential transport pathways 

are not adequately simulated in the flow model. Although the 

TPROGs realizations may represent reasonable spatial variations in hydraulic properties, it is likely that some conductive 

vertical transport pathways exist in the aquifer at a finer spatial 

scale than that of the model discretization, and (or) should be 

represented with smaller effective porosity than the uniform 

value employed throughout the model domain. Additional well 

boreholes not represented in the model may also be shortcircuiting flow though the aquifer matrix in some areas. Well 

boreholes that are simulated in the model may not adequately 
Model Uncertainties and Limitations 41

!

PSW 3

SSW

W 3

PSW 1

106°39' 106°38'30" 106°38' 106°37'30" 106°37'

35°05'30"

35°05'

35°04'30"

35°04'

35°03'30"

35°03'

0 1 MILE

0 1 KILOMETER

Simulated flow path 

Simulated contaminant source 

Public-supply well 

Well with chlorinated solvents 

EXPLANATION

Albuquerque Int lbuquerque International Airport ational Airport

Lomas Blvd

PSW 1

Figure 31. Flow paths of particles released from the contaminant source from 1975 until 1989 simulated with 8 percent effective 

porosity, public-supply wells, and monitoring wells with observed chlorinated solvents, Albuquerque, New Mexico.
42 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

PSW 3

SSW

W 3

PSW 1

106°39' 106°38'30" 106°38' 106°37'30" 106°37'

35°05'30"

35°05'

35°04'30"

35°04'

35°03'30"

35°03'

0 1 MILE

0 1 KILOMETER

Simulated flow path 

Simulated contaminant source 

Public-supply well 

Well with chlorinated solvents 

EXPLANATION

Albuquerque Int lbuquerque International Airport ational Airport

Lomas Blvd

PSW 1

Figure 32. Flow paths of particles released from the contaminant source in 1976 simulated with 1.1 percent effective porosity, 

public-supply wells, and monitoring wells with observed chlorinated solvents, Albuquerque, New Mexico.
Summary and Conclusions 43

simulate the short-circuiting flow for a reason similar to that 

previously discussed for the SSW: the flow which may occur 

during short time intervals of non-pumping is not simulated by 

the seasonal stress periods, for which a higher pumping rate 

represents the average over the length of the stress period. A 

daily stress-period simulation is needed to further evaluate this 

possibility. Flow from the water table to the well screen may 

also occur through the borehole annular space around the casing of the SSW or other wells if they were imperfectly grouted 

or sealed, or if fractures in cement have developed over time. 

The possible effects of transport through this potential preferential flow path were not simulated. 

Summary and Conclusions

This report documents the development and calibration 

of a model of transient groundwater flow and tracer-transport 

and selected particle-tracking simulations of a contaminant 

plume within the TANC local study area in Albuquerque, 

New Mexico. A 108-year transient groundwater-flow model 

of the 24-km2

 TANC study area was constructed and coupled 

to a modified version of the regional MODFLOW model of 

the MRGB with the local-grid refinement code MODFLOWLGR2. Short-normal resistivity and lithologic logs from 

14 wellbores and borehole cuttings from 4 wellbores were 

used to categorize the Tertiary alluvium that composes much 

of the zone of contribution to the public-supply wells into 

three hydrofacies. Ten hydrofacies-distribution realizations, 

each of which honored the borehole control data, were generated with the geostatistical software package TPROGS and 

were used to simulate a more complex hydraulic-conductivity 

distribution within 32 of the model layers that represented 

a 97-m-thick interval of the Tertiary Rio Puerco member of 

the Ceja Formation. Although all of these realizations were 

tested in various model runs, one realization was used for the 

final model calibration. Areas receiving recharge from sewer 

and water-distribution pipelines, infiltration from an unlined 

stormwater-diversion channel, and infiltration beneath ponds 

and irrigated vegetated areas at different times during the transient simulation were specified and parameterized so that the 

magnitude of recharge from each source could be estimated 

during model calibration. 

The transport of five isotopic and chemical tracers (carbon-14, tritium, and three chlorofluorocarbon species) was 

simulated with the solute-transport code MT3DMS, which 

used the transient flow field computed by the local-scale 

MODFLOW simulation. The initial concentration condition 

for carbon-14 was interpolated from the regional transport 

simulation documented in the appendix. The concentrations specified for carbon-14 and tritium in recharge at the 

water table were delayed by 10 years with respect to their 

atmospheric concentrations in order to simulate 10 years of 

transport through the vadose zone. Measurements of water 

levels and tracer concentrations in the public-supply and

other wells were used to calibrate parameters representing 

recharge, hydraulic conductivity, and effective porosity with 

the parameter-estimation code PEST. The uncertainty associated with each water level or concentration measurement 

was quantified and used to assign observation weights in the 

model-calibration regression. Values of some hydraulic-conductivity parameters were also constrained by measurements 

of borehole flow within the SSW.

The fit of simulated water levels to their measured 

values is graphically depicted with time series for wells with 

six or more observations and with a scatter plot of the relation between all water-level observations used in the model 

calibration and their simulated counterparts. Graphs of the 

relations between simulated and observed concentrations by 

observation-well-depth category for each of the five simulated species enable identification of possible model bias. The 

local-scale transport model simulates the set of carbon-14 

observations within the local study area with greater accuracy 

and smaller bias than the regional-scale transport simulation 

documented in the appendix. The simulated positive residuals of tritium and CFCs, with simulated values near zero, 

may indicate that preferential-transport pathways are not 

adequately simulated in the flow model. 

The simultaneous simulation of the concentrations of 

carbon-14 and the young-groundwater tracers (tritium and 

chlorofluorocarbons), which have different initial conditions, 

temporally changing input histories, and decay characteristics, 

constrained the simulation of the mixing of relatively recently 

recharged (post-1950) water with an older mixture containing 

groundwater that may have resided in the flow system for up 

to tens of thousands of years. The transient groundwater flow 

model simulates the effects of seasonal changes in pumping 

rates on the hydraulic gradients, and consequent transport 

rates, between different aquifer intervals and the screened 

intervals of the simulated withdrawal wells. Time-series plots 

illustrate the seasonal variability of simulated water levels 

and concentrations in the SSW, which are a consequence of 

increased groundwater withdrawals that typically occur during 

the summer stress periods. The relative fraction of groundwater originating in aquifer intervals above and below the 

studied supply well screened interval changes as a function of 

pumping rates and consequent hydraulic gradients and flow 

rates. Because these different aquifer intervals are more likely 

associated with either anthropogenic or natural contaminants 

in the local study area, the model is capable of simulating the 

change in susceptibility to contamination from either source as 

a function of withdrawal rates. The larger tracer concentrations 

simulated in the SSW during the summer seasons result from 

greater flow from the shallow aquifer intervals that contain 

relatively younger water, which generally contains higher 

concentrations of the tracers. The simulated percentage of 

post-1950 water is similarly greater during periods of greater 

groundwater withdrawal when larger hydraulic gradients 

exist between the water table and the SSW screened interval. 

These simulated results agree well with the observed seasonal 
44 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

tracer-concentration variability of water samples from the 

SSW and the independent mixing-model analysis in a companion report.

Seasonal variability in the fraction of water originating 

from deeper aquifer intervals near and below the bottom of the 

SSW screened interval, which is more likely associated with 

elevated arsenic concentrations, was also simulated for the 

SSW. The simulated fraction of this deeper-sourced, presumably older water is greater during periods of lower groundwater withdrawals, such as typically exist during the winter 

season. This pattern of simulated seasonal variability is in concordance with observations of higher arsenic concentrations in 

water samples from the SSW during the winter low-pumping 

season.

The observed temporal variability of measured concentrations may also be due, in part, to mixing of water in 

the aquifer resulting from the direct movement of water 

through the borehole (from intervals of higher head to lower 

head) during times when the well is not pumped. Because 

this borehole-flow mechanism was not adequately simulated 

with the seasonal stress periods, it remains uncertain whether 

flow through the aquifer or flow through the borehole is the 

dominant cause of vertical mixing in the vicinity of the SSW. 

Either mechanism likely results in the existence of zones of 

younger water, which are more likely to contain anthropogenic 

contaminants at greater depths than might exist without deep 

public-supply wells.

Particle tracking with MODPATH was employed to 

simulate the advective transport of dissolved contaminants 

from a known source located northwest of the SSW. Simulations using effective porosities of several percent, including 

that estimated by calibration of the solute-transport simulation 

(8 percent), and a wide range of contaminant-release times 

were characterized by capture of the simulated contaminated 

water by other public-supply wells located between the source 

and the SSW. A simulation with a smaller effective porosity 

of 1.1 percent, which may represent a particularly preferential 

pathway, simulated purely advective transport directly from 

the source into the SSW. Alternative scenarios with an early 

contaminant release time (around 1963) when groundwater 

flow was directed more southerly also simulate transport of 

contaminants into the SSW. Because water-quality samples 

from locations south of the source were not obtained until after 

1989, by which time the changed groundwater-flow direction 

might have flushed the sampled area with clean water, this 

alternative scenario cannot be ruled out. Nevertheless, the 

preponderance of contaminated samples between the source 

and the SSW favors the scenario with a more direct path (and 

smaller simulated effective porosity) to the SSW.

Acknowledgments

The author acknowledges the assistance of people affiliated with the Albuquerque Bernalillo County Water Utility Authority (ABCWUA), the University of New Mexico 

(UNM), the Central New Mexico Community College (CNM), 

and the New Mexico Environment Department (NMED). Sean 

Connell of the New Mexico Bureau of Geology and Mineral 

Resources shared his extensive knowledge of the hydrogeology of the area and provided input on data collected as part 

of this study. Modeling of aquifer heterogeneity with the 

TPROGS software was performed by Nick Engdahl while 

attending UNM and working for the USGS. Nicole Thomas 

of the USGS described borehole cuttings, and Gordon Rattray and Eric Scherff analyzed borehole electric logs. Amjad 

Umari and Jesse Dickinson provided technical reviews, and 

Laura Bexfield and other members of the national TANC team 

provided advice.

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Appendix

Appendix Simulation of the regional distribution of carbon-14 concentration.
48 Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells

The radiocarbon age of dissolved inorganic carbon in 

groundwater can be used to estimate the aquifer-residence 

time, or the time since recharge to the aquifer system, of a 

groundwater sample. The carbon-14 concentrations in numerous groundwater samples from the Middle Rio Grande Basin 

(MRGB) were measured for the NAWQA TANC study (Bexfield and others, 2011) as well as previous studies (Plummer 

and others, 2004). Observations of carbon-14 concentration 

in the local-scale model area (table 4) were used to estimate 

a value of regional effective porosity in a regional-scale 

carbon-14 transport simulation and to calibrate hydraulic 

conductivities and effective porosity in the local-scale groundwater-flow and solute-transport simulations. The objective of 

the regional carbon-14 transport simulation was to provide a 

reasonable representation of the predevelopment (steady-state) 

carbon-14 distribution in the area of the local model, which, in 

turn, was used to specify the initial carbon-14 condition for the 

local-scale transient carbon-14 transport simulation described 

in this report.

The transport code MT3DMS numerically solves the 

transient advection-dispersion equation:

( ) ( ) 











θ

θ

θ

θ

∂

∂ = ∂∂ ∂ − ∂∂

+ +

C

t x

D

C

x x

v C q C R

k

i

ij

k

j i

i

k

s s

k

n , (1-1)

where 

θ	 is the effective porosity,

Ck is the concentration of the dissolved chemical 

species k, 

t is time, 

x is distance,

i and j are indices referencing orthogonal directions 

in a Cartesian coordinate system,

D is the hydrodynamic dispersion tensor, 

vi is the seepage velocity, 

qs is the volumetric flow rate of fluid sources 

and sinks,

Cs

k is the concentration of dissolved chemical 

species k in the fluid sources or sink, and

Rn is a chemical reaction term.

The radioactive decay of dissolved carbon-14 (represented by the term Rn

 in equation 1-1) was simulated as a firstorder irreversible reaction without sorption by using a decay 

constant representing the 5,730-year half-life in the Chemical 

Reaction Package of MT3D.

The regional transport model simulates the regional distribution of carbon-14 concentration, which primarily depends 

upon the initial carbon-14 concentration of water when it 

recharges the groundwater system and the elapsed time of 

transport between recharge and the sampling event. Because 

some numerical dispersion always occurs in a numerical solution of the solute transport equation (eq. 1-1) and adequate 

criteria for specification of aquifer system dispersivities were 

not available, additional dispersive mixing was not simulated. 

Therefore, the regional transport model primarily simulates 

advective transport, with some numerical dispersion.

The spatial and temporal discretizations of the regional 

MT3D transport simulation are identical to the revised 

regional flow model, which enables use of the MODFLOW-2005 computed inter-cellular flows to define the 

advective term of the transport equation (eq. 1-1). Corresponding to the MODFLOW simulation, the MT3DMS transport 

simulation consists of steady-state and transient stress periods. 

The predevelopment carbon-14-concentration distribution was 

simulated in an initial steady-state stress period solved with 

the fully implicit finite-difference solution option of MT3DMS 

with upstream weighting. 

Concentrations were specified at model boundaries that 

simulate recharge to the groundwater system along rivers 

(such as the Rio Grande and Rio Puerco), mountain-front 

areas, and, in the transient simulation only, beneath leaky 

water and sewer pipes in the Albuquerque area. The carbon-14 

concentration of recharge water in the MRGB is likely near 

100 percent modern carbon (pmc); accordingly, this value was 

assigned to model cells containing river or recharge boundaries with the Sink & Source Mixing Package of MT3DMS. 

However, because vadose-zone geochemical reactions can 

lower the initial carbon-14 concentration of recharge water, 

these concentrations were parameterized to enable their 

estimation during model calibration. Because groundwater 

underflow into the regional model area is thought to be old, 

a carbon-14 concentration of zero was specified for such 

boundaries.

Calibration of the Regional-Scale Carbon-14 

Transport Simulation

The values of parameters representing a uniform effective porosity and initial carbon-14 concentration of water 

in rivers and other recharge sources were estimated with 

UCODE (Poeter and others, 2005) by minimizing the objective function:

∑ ω ( ) − =

C C ' i

i

N

i i

1

2

, (1-2)

where 

Ci is the measured carbon-14 concentration for 

observation i, 

C′i is the simulated-equivalent carbon-14 

concentration observation for i,

ωi is the weight applied to the squared 

difference of observation i and its 

simulated equivalent, and

N was 35, which is the number of carbon-14 

observations in the local-scale study area 

that were used in the regression.

The weight (ωi

) assigned to each carbon-14 observation 

was equal to the inverse of the calculated total error variance 
Appendix 49

for that observation. The total observation error variance was 

the sum of the laboratory, or “measurement,” error variance 

and the error variance introduced in the geochemical modeling 

by Bexfield and others (2012),which introduced the majority 

of the uncertainty. The estimated variance for each modeled 

carbon-14 observation was based on two assumptions: (1) a 

normal distribution and (2) 90-percent confidence that the true 

value lies between the measured value and the “best” geo-

chemically modeled value. Stated differently, the magnitude 

of the uncertainty interval introduced from the geochemical 

modeling was estimated with 90-percent confidence to be the 

difference of the corrected and measured values.

In each iteration of the UCODE regression, the param-

eter values for a combined steady-state and transient regional 

MT3D transport simulation were updated. A uniform effec-

tive porosity of 18 percent and a recharge-source carbon-14 

concentration of 100 pmc produced the optimal fit between 

observed carbon-14 concentrations and their corresponding 

values simulated with the transient transport model. 

Simulated Regional Carbon-14 Concentrations

The steady-state regional distribution of carbon-14 

concentrations simulated within the top layer of the trans-

port model, which represents water at depths from zero to 

14 meters below the simulated steady-state water table, is 

depicted in figure 1-1. Areas of high simulated carbon-14 

concentration result from the shorter times of groundwater 

transport since recharge at the base of the mountains along 

the eastern basin margin, the Rio Puerco, and losing reaches 

of the Rio Grande. The simulated distribution is comparable 

to that mapped by Plummer and others (2004, fig. 77) from 

measured carbon-14 concentrations. Simulated carbon-14 

concentrations in deeper model layers are smaller, represent-

ing generally increasing groundwater age with depth, and 

have similar spatial variability. The area encompassed by the 

local-scale model domain described in the main report is also 

depicted in figure 1-1. The steady-state carbon-14 concentra-

tions simulated with the regional transport model described 

in this appendix were used to specify the initial carbon-14 

concentrations for the local-scale model described in the 

main report. The non-random fit of carbon-14 concentrations 

simulated with the regional-scale transport model to concen-

trations observed in the local study area (fig. 1-2) suggests 

that the regional model over-predicts the age (under-predicts 

the carbon-14 concentration) of relatively young groundwater 

samples and under-predicts the age (over-predicts the car-

bon-14 concentration) of older groundwater samples in this 

area.
50 Report Title

45’ 30’ 15’ 106°00’

45’

15’

107°15’ 107°00’

35°00’

34°15’

30’

35°30’

Simulated carbon-14 concentration, 

in percent modern carbon

0 - 20

21 - 40

41 - 60

61 - 80

81 - 100

Local model area

EXPLANATION

0 20 MILES

0 20 KILOMETERS

Base modified from U.S. Geological Survey digital data, 1:24,000, 1999

Universal Transverse Mercator, Zone 13N, North American Datum of 1983.

Figure 1-1. Carbon-14 concentrations simulated in the top layer of the Albuquerque Basin regional transport model.
Appendix 51

0

20

40

60

80

100

0 20 40 60 80 100

Observed carbon-14 concentration, in percent modern carbon

Simulated carbon-14 concentration, in percent modern carbon

Shallow 

Intermediate 

Deep 

Public supply

Well category

EXPLANATION

1:1 line

Figure 1-2.Relation between observed carbon-14 concentrations 

and concentrations simulated with the Albuquerque Basin regional 

transport model.

Heywood—Simulations of Groundwater Flow, Transport, and Age in Albuquerque, New Mexico, for TANC to Public-Supply Wells—Scientific Investigations Report 2012–5242