A Department of Energy
Environmental Cleanup Program
LA-UR-00-5923
December 2000
ER2000-XXXX
Modeling Transport in Los Alamos Canyon:
Effects of Hypothetical Increased Infiltration
after the Cerro Grande Fire
Los Alamos
NATIONAL
LABORATORY
Los Alamos, NM 87545
Los Alamos National Laboratory, an affirmative action/equal opportunity
employer, is operated by the University of California for the United States
Department of Energy under contract W-7405-ENG-36.
Produced by EES-5, Geoanalysis
Authors: P. Stauffer, B. Robinson, K. Birdsell
Illustrators: P. Stauffer, M. Witkowski, FIMAD
Grid Generation: C. Gable, M. Witkowski
This report was prepared as an account of work sponsored by an agency of the United States Government.
Neither the Regents of the University of California, the United States Government nor any agency thereof,
nor any of their employees make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represent that its use would not infringe privately owned rights. Reference herein to any specific
commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not
necessarily constitute or imply its endorsement, recommendation, or favoring by the Regents of the University of California, the United States Government, or any agency thereof.
Los Alamos National Laboratory strongly supports academic freedom and a researcher's right to publish; as
an institution, however, the Laboratory does not endorse the viewpoint of a publication or guarantee its tech-
Table of Contents
1.0
Introduction
2.0
Site Description
2.1
Location
2.2
Stratigraphy
2.3
Contaminants of Concern
2.4
Conceptual model
2.5
Hydrogeologic data
2.6
Transport properties
3.0
Numerical Model: Groundwater flow in Los Alamos Canyon
3.1
FEHM
3.2
Model domain and computational grid
3.3
Boundary and initial conditions
3.4
Numerical formulation used to simulate perched water
3.5
Hypothetical region of contamination: Initial tracer distribution
4.0
Results
4.1
Summary of the Base simulation
4.1.1 Dispersive effects
4.2
Increased infiltration scenarios
4.2.1 Changes to saturation cause by increased infiltration
4.2.2 Transport of a conservative tracer
4.2.3 Transport of a non-conservative tracer
5.0
Conclusions
6.0
Acknowledgements
6.0
References
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List of Figures
1-1
1-2
Cerro Grande Fire intensity map.
High intensity fire damage displayed in Los Alamos Canyon
2-1
Location of Los Alamos Canyon with respect to the Laboratory and the towns of
Los Alamos and White Rock
Geographical information for Los Alamos Canyon and the surrounding area.
Geologic framework model for the Los Alamos Canyon model study area
Simplified site stratigraphy.
Schematic diagram of the conceptual model for flow and transport in the vadose
zone of the Pajarito Plateau.
2-2
2-3
2-4
2-5
3-1
3-2
3-3
Map view of the computational grid.
Cross-section of model stratigraphy.
Location of hypothetical tracer input.
4-1
4-2
4-3
4-4
4-5
4-6
4-7
4-8
4-9
4-10
4-11
4-12
4-13
4-14
Base simulation saturation profile on two cross-sections.
Base simulation conservative tracer concentration after 100 years.
Base simulation conservative tracer concentration as a function of time.
Base simulation non-conservative tracer concentration as a function of time.
Effects of dispersion on tracer transport to the water table.
Location and size of A) The small pond and B) The medium pond
Total mass flow rate to the water table for infiltration Cases 1-9.
Saturation as a function of time at 50 m depth for infiltration Cases 1-9.
Conservative tracer concentration as a function of time at the source, Cases 1-9.
Conservative tracer concentration as a function of time at 30 m, Cases 1-9.
Conservative tracer movement to the water table as a function of time, Cases 1-9.
Non-conservative tracer concentration with time at the surface.
Non-conservative tracer concentration with time, 30 m below the surface.
Non-conservative tracer concentration with time, 50 m below the surface.
List of Tables
1
2
3
4
Stratigraphic nomenclature of the Pajarito Plateau.
Physical parameters used in the site model (permeability and porosity).
Physical parameters used in the site model (van Genuchten parameters).
Summary of values used in the 12 infiltration scenarios.
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1.0 - Introduction
The Cerro Grande fire swept through the steep canyons and over the tree covered mesas of
Los Alamos county during May 2000 with devastating effects. Large portions of the watersheds
above the town of Los Alamos were radically altered by the fire. Figure 1-1 shows fire intensity,
N
Figure 1-1.
High intensity
Medium intensity
Low intensity
5x vertical
Unburned
Cerro Grande Fire intensity map. Los Alamos National Laboratory lies within the closed black line. Diamond Drive is the unclosed black line to the north of the Laboratory boundary. Los Alamos Canyon is
highlighted by the light blue based line.
a measure based on the size of the fire and the degree of burning in the layer of storage in the forest
canopy. The fire intensity was high in the upper reaches of Los Alamos Canyon. Fire severity, a
measure of how much heat goes into the ground, was extreme in many locations, causing the soil
to vitrify and organic material to disintegrate into a fine, waxy ash (Figure 1-2).
The changes to the vegetation and soil have profound implications for both surface water
and groundwater. Burned watersheds shed more water faster because the soils can no longer soak
up rain. For example, estimates from surface water modeling show hundred-fold increases or more
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N
Figure 1-2.
High intensity fire damage on the south side of Los Alamos Canyon. The view is looking west toward
the Los Alamos reservoir, seen as the green patch near the center [Photo from BAER Team, 2000].
in maximum canyon flow rates during the summer monsoons [BAER Team, 2000; Reneau, pers.
com.]. Measured canyon flow rates during July 2000 thundershowers have matched or exceeded
the preliminary surface flow model results, requiring updated estimates for the 50 and 100 year
rainfall events [Reneau, pers. com.]. Increased flow of water to the unburned lower portions of the
canyons is expected to cause more water to infiltrate into the subsurface and subsequently toward
the water table. Such increased infiltration is a concern in Los Alamos county because of the
historical releases of laboratory generated contaminants. Some of these contaminants are
concentrated in the canyon bottom sediments, while some are found in overbank deposits created
in previous flooding events [Reneau et al, 1998]. Increased run-off may cause stream channels to
widen and reactivate these overbank deposits. When water is flushed through the sediments,
contaminants can be transported with the groundwater toward the water table as dissolved species
or bound onto colloids. This is of concern because the regional aquifer is the source for the local
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Post Cerro Grande Transport Modeling in Los Alamos Canyon
municipal water-supply wells, which are relied upon heavily for residential and industrial
applications.
Another concern is the potential for natural or man-made dams to generate large-scale
ponding in the canyons. A good example of this is the Diamond Drive fill near the Pueblo complex,
which historically has caused ponding to depths of 10 m or more [Reneau, pers. com.]. A
substantial dam (36 m) is being constructed in Pajarito canyon to protect TA-18 from potential
flooding, and state law requires that this dam be drained within 4 days [Reneau, pers. com.]. The
potential effects of such dams on groundwater flow and transport of contamination are not well
understood.
This report is designed to explore several scenarios involving increased infiltration and
ponding in the canyons of Los Alamos County. We present results from a numeric model of central
Los Alamos Canyon. The modeling results should be useful in helping to create sampling strategies
to better characterize the true behavior of infiltration during higher surface flows. Finally, we make
suggestions on ways to limit the effects of ponding on subsurface transport.
2.0 - SITE DESCRIPTION
2.1
Location
Los Alamos county is located in northern New Mexico on the eastern flank of the Jemez
mountains. Los Alamos National Laboratory is bounded by Bandelier National Monument, the
towns of White Rock (east) and Los Alamos (north), Pueblo lands, and Santa Fe National Forest
to the west (Figure 2-1). This study focuses attention on a small subset of the Laboratory, the
confluence of Los Alamos and DP canyons (Figure 2-2). This area was specifically chosen because
potential releases from the Omega West Reactor could combine with waste from TA-21, resulting
in measurable alluvial concentrations of several contaminants of concern (COC’s). This location
was also chosen because a pre-existing computational grid (Figure 2-3) designed to model flow
and transport in Los Alamos Canyon [Robinson et al., 2000] could be readily adapted for the
current study. Furthermore, Los Alamos Canyon is one of four canyons to receive the highest risk
rating from the Burned Area Emergency Rehabilitation Team [BAER, 2000]. The risk factors
examined by the BAER Team include: potential for waste migration, severity of fire in the upper
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n
anyo
Pueblo Cany
on
Los
Alam
os C
anyo
n
Bayo Canyon
Los A
lamos
Canyo
n
Pajarito Canyon
Cañ
on d
e
Sandia Canyon
Val
le
Cedro
Canyo
n
M
ort
an
da
dC
an
yo
n
Cañad
a del B
uey
Wate
r Can
yon
Po
tril
lo C
any
on
Fri
jol
es
Ca
ny
on
An
ch
oC
an
yo
n
n
nyo
Ca
lo
tril
Po
Frijo
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anyo
n
W
ate
rC
an
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n
on
any
iC
ehu
aqu
Ch
n
nyo
Ca
mo
Ala
yon
Can
mis
Lum
Ancho
Canyo
n
e
nd
ra
G
o
Ri
n
nyo
Ca
in
pul
Ca
Figure 2-1.
Location of Los Alamos Canyon with respect to the Laboratory and the towns of Los Alamos and White
Rock.
watershed, potential for destruction of infrastructure, and potential for damage to stakeholders (i.e.
communities downstream).
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Figure 2-2.
Topography, well locations, and TA boundaries near the confluence of Los Alamos and DP canyons.
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Within the area of interest, Los Alamos Canyon is bounded to the north by Los Alamos
Mesa, and to the south by the LANCE facility mesa. The area of interest receives run-off from the
upper two segments of Los Alamos Canyon (LA1, LA2), a watershed of nearly 18 square
kilometers. Los Alamos Canyon is one of the main canyons draining the Pajarito Plateau, and the
Cerro Grande fire is estimated to have burned more than 60% of the upper two segments of the
watershed, with at least 30% assigned a high burn severity. Burn severity is defined as a relative
measure of the degree of change in a watershed that relates to the severity of the effects of fire on
the watershed [BAER, 2000]. Figure 2-2 shows the site topography and GIS information on roads,
wells, and buildings that lie within the area of interest.
2.2
STRATIGRAPHY
The geology of the Pajarito Plateau is quite complex, with many episodes of volcanic
activity resurfacing the region over the last few million years. The mesas and cliffs in the area of
interest are composed of nonwelded to moderately welded rhyolitic ash-flows and ash-fall tuffs
interbedded with thin pumice beds. The rhyolitic units are underlain by a thick fanglomerate
formation [Krier, et al., 1997]. The tuff layers were deposited during violent eruptions of volcanic
ash from the Valles caldera between 1.2 and 1.6 million years ago [Smith and Bailey, 1966;
Gardner et al., 1986]. The tuff units have eroded to leave a system of alternating finger-shaped
mesas and narrow canyons.
The complexity of the Pajarito Plateau geology requires use of a simplified geologic model
based on borehole logs, outcrops, and geologic inferences. Los Alamos geologists have created a
Qbt5
Qbt4
Qbt3t
Qbt3
Qbt2
Qbt1v
Qbt1g
Qbtt
Qct
Qbof
Qbog
Tt2
Tt1
Tb4
Tpf
Tpt
Tb2
Tsfuv
Figure 2-3.
Geologic framework model for the full-scale Los Alamos Canyon study area. The blue box shows the
subset used for the current study.
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Post Cerro Grande Transport Modeling in Los Alamos Canyon
three-dimensional geologic framework model for Los Alamos Canyon consisting of 20 distinct
units [Carey et. al, 1999]. Figure 2-3 shows the portion of the geologic model used for the Los
Alamos Canyon flow and transport model of Robinson et al. [1999] with colors on this figure
representing different stratigraphic units. The blue box on Figure 2-3 outlines the area of interest
for this study. The geologic model was developed by EES-1 (Geology Group) of LANL’s Earth and
Environmental Sciences Division and is the product of a continuous process of model development
and improvement in support of the LANL Environmental Restortation Project and related
hydrogeologic workplan activities. The current list of defined stratigraphic units and their accepted
designators is presented in Table 1.
Not all units in Table 1 are represented at our study location, and in addition to the well
defined rock units, there are deposits of alluvium lining many of the canyon bottoms. The alluvium
is composed of coarse channel sediments and finer grained overbank deposits [Reneau et al, 1998].
Figure 2-4 shows a simplified stratigraphic column of the rocks underlying Los Alamos Canyon in
the area of interest. The uppermost unit is the Otowi member of the Bandelier Tuff. The Otowi
member is nonwelded to poorly welded and contains only minor fractures. The Otowi is
subdivided into an ash-flow component (Qbof) and a pumice component (Qbog) [Vaniman et al.,
1996; Krier et al., 1997]. Below the Otowi lies the Puye Formation, a Tertiary (4.0 - 1.6 Ma)
amalgamation of alluvial fan, river, and lake deposits containing cobbles and boulders of both
volcanic and plutonic origin in a matrix of silts, clays, and sands. Interbedded basalt flows, dacite
flows, and pumice lenses are also common [Vaniman et al., 1996]. The regional water-table is
located approximately 850 ft. below the canyon bottom in the Puye Formation. Perched aquifers
may exist as suggested by observations of saturated conditions above the regional water-table in
wells near the area of interest [Robinson et al., 1999]. Below the Puye Formation lies the Tertiary
Santa Fe Group of sedimentary rocks (28.0 - 4.0 Ma) which are considered to be the primary
aquifer unit for Los Alamos county. The deepest unit shown on Figure 2-4 is the Cerros del Rio
basalt, which displays wide variability [Turin, 1995], ranging from extremely dense with no
effective porosity, to highly fractured, to so vesicular as to appear foamy.
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Stratigraphy of the Los Alamos Canyon model [Carey et al., 1999].
TABLE 1.
Group/Formation
Bandelier Tuff (Tshirege Member)
Unit Name
Symbol
Unit 5
Qbt5
Unit 4
Qbt4
Unit 3
Qbt3
Unit 2
Qbt2
Vapor-phase altered member of unit 1
Qbt1v
Glassy member of unit 1
Qbt1g
Tsankawi Pumice
Qbtt
Cerro Toledo Rhyolite
Cerro Toledo
Qct
Bandelier Tuff (Otowi Member)
Otowi Member ash flow
Qbof
Guaje Pumice Bed
Qbog
Puye fanglomerate
Tpf
Totavi Lentil
Tpt
Basalt 4
Tb4
Basalt 3
Tb3
Basalt 2
Tb2
Basalt 1
Tb1
Tschicoma dacite
Tt2
Tschicoma dacite
Tt1
Chaquehui (volcaniclastic) aquifer unit
Tsfuv
Santa Fe Group undifferentiated
Tsfu
Puye Formation
Cerros del Rio basalts
Tschicoma Formation
Santa Fe Group
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6600
6600
6400
6400
Otowi (Qbof)
Guaje Pumice Bed (Qbog)
6200
6200
6000
6000
Puye Formation, fanglomerate
(Tpf)
Tschicoma dacite (Tt1, Tt2)
Elevation
(feet)
Puye Formation, fanglomerate
(Tpf)
5800
5800
Puye, Totavi Lentil
(Tpt)
5600
5600
Santa Fe Group, aquifer unit
(Tsfuv)
Figure 2-4.
2.3
5400
5400
5200
5200
5000
2
5000
8 1 0 12
4
6
Cerros del Rio basalt
(Tb2)
Simplified site stratigraphy. Extracted from the numeric representation of the Sitewide Geologic Model.
CONTAMINANT SOURCES IN LOS ALAMOS CANYON
There are a host of possible contaminant source sites for Los Alamos and DP Canyons
resulting from past and present Laboratory operations. Although the current study uses an
hypothetical tracer released from an hypothetical release site, we include a review of Potential
Release Sites (PRSs) and Contaminants of Concern (COCs) relevant to Los Alamos Canyon. The
following list of PRSs and COCs in Los Alamos Canyon is from Robinson et al. [1999], and is
meant to give the reader insight into the range of transport parameters used later in this paper.
TA-1 (Townsite). A variety of septic systems, storm drains, and outfalls have introduced
contaminants into Los Alamos Canyon at the old TA-1 site. Quantities of effluents and
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concentrations of contaminants are generally unknown. Many of these sites have undergone
remediation and cleanup. Suspected contaminants include actinides, fission products, metals, and
solvents.
TA-41 (Weapons Development Facility). This site was used, starting in the early 1940's,
for nuclear weapons development and long-term studies on weapon subsystems. Storm drainages,
a sump pit, an abandoned septic tank, and a sewage treatment plant were operational and possibly
introduced contaminants such as actinides and tritium into Los Alamos Canyon.
TA-2 (Omega West Reactor Site). This site, located in Los Alamos Canyon, was used since
1943 to house and operate a series of research reactors. Early reactors were fueled by aqueous
uranyl solutions, whereas other reactors were fueled by solid fuel elements. A variety of
contaminants (mostly radionuclides) are suspected to have been released into the canyon. Most
relevant to the present study is tritium, which resulted from a leak in the primary cooling water
system at the reactor. The leak occurred from a break in a weld seam in a section of the delay line
running from building TA-2-1 to the surge tank. This leak was discovered in 1993, and tritium was
detected within the Guaje Mountain fault zone. Typical concentrations in the cooling water ranged
from 15.7 x 106 to 20.2 x 106 pCi/L. The duration of the leak is not documented, but measurements
of tritium concentrations in alluvial aquifer well LAO-1 (located at the eastern boundary of TA-2)
suggest that the leak may have begun between November 1969 and January 1970. This reactor was
permanently shut down in 1994.
TA-21 (DP Site). A variety of outfalls from treatment facilities and releases from absorption
beds have released contaminants from this nuclear materials research and processing facility since
the early 1940's. Major release sources include:
The 21-011(k) outfall, a discharge line that carried treated waste water from industrial
waste treatment plants to a discharge point on the south slope of DP canyon from approximately
1952 to 1985. A significant input of tritium is introduced to the Los Alamos/DP canyon system from
this source.
MDA T, four absorption beds that served as seepage pits for the disposal of liquid wastes
from plutonium processing operations. Contaminants include, among others, plutonium and
ammonium citrate.
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MDA V, an area that comprises three absorption beds used for liquid waste disposal from
a laundry operation. These pits were in continuous operation from 1945 to 1961.
TA-53 (LANSCE). Operations from LANSCE have introduced contaminants such as
tritium, metals, and variety of radionuclides including plutonium. The primary release sources are
identified as PRS 53-002(a and b), a series of three surface impoundments. Two of these, the
northern impoundments, were operated from the early 1970's to 1993. Both were designed as claylined retention ponds, but they frequently filled to capacity and had to be discharged.
2.4
CONCEPTUAL MODEL [from Robinson et al., 1999]
The conceptual model for vadose zone flow and transport at this site is based on the model
outlined qualitatively in the Hydrogeologic Workplan (LANL, 1996) and shown schematically in
Figure 2-5. The Pajarito Plateau, on which the Laboratory is located, can be viewed as a relatively
Figure 2-5.
Schematic diagram of the conceptual model for flow and transport in the vadose zone (reproduced from
the Hydrogeologic Workplan, LANL, 1996)
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Post Cerro Grande Transport Modeling in Los Alamos Canyon
dry site with low recharge of fluids over most of the study area. However, there are known to be
locations where focused recharge takes place. Most notably, canyons such as Los Alamos Canyon
are watersheds that provide focused flow paths for surface water flow; perennial or ephemeral
streams are common in the canyons that cut through the Plateau. Therefore, in its simplest form,
the conceptual model divides the Plateau into dry mesas (and in some cases, dry canyons) and
wetter canyons. Contaminant travel times are anticipated to be long for sources emitted on mesas,
but may be significantly shorter for contaminants in canyons.
Another commonly occurring observation is the presence of small bodies of subsurface
water at the contact between canyon-bottom alluvial deposits and the underlying bedrock. These
water bodies, which we call shallow alluvial groundwater, are not sufficiently extensive to be
suitable for mining of the water for domestic use. Nevertheless, this water is an important
component of the subsurface hydrologic system because recharge occurs from this system to the
underlying rock strata. Furthermore, Laboratory emissions into the canyons can easily move into
this shallow groundwater, thereby providing a means for contamination to migrate to greater
depths. Data from the shallow alluvial aquifer of Los Alamos Canyon were used to develop the
boundary condition input to the base flow model used here, which starts at the alluvium/bedrock
contact.
The base flow model used in the present study is the product of a comprehensive integration
of a wide variety of data sources and studies, including:
•
•
•
•
•
•
•
stratigraphy of the vadose zone beneath Los Alamos Canyon
rock hydrologic properties
water budget and recharge measurements
rock water content determinations in core samples from characterization wells
observations of perched water in characterization wells
records of tritium concentrations in recharge fluid
subsurface determinations of tritium concentrations in vadose zone fluids
[from Robinson et al., 1999]
The observations of perched water in the borehole data have led to a revised conceptual
model in which thin paleo-soil horizons between units lead to reductions in permeability.
Permeability reduction at these interfaces can potentially cause water to perch, forming lenticular
bodies of saturation with unknown lateral extent, well above the water table. These perched regions
of saturation probably have profound effects on flow and transport due to their ability to divert
water and contaminants laterally at the saturated permeability of the rock in the region of perching.
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A well documented interface thought to control perching is found at the base of the Guaje Pumice
Bed of the Otowi member of the Bandelier Tuff. This boundary is marked by a low permeability
clay-rich paleo-soil on the order of several inches thick. Other documented perched bodies occur
at similar interfaces within the Cerros del Rio basalts and Puye Formation. The presence of inch
thick interfaces that strongly influence subsurface flow and transport required the development of
new numerical techniques, which are described briefly in the Numerical Model section of this
report and in full detain in Robinson et al. [1999].
The current study uses the base flow model from Robinson et al. [1999] as a reference case
to which flow and transport caused by variations in infiltration rate and location can be compared.
Although no model can perfectly synthesize and match all available data, the base flow model used
in the present study is consistent with the majority of the observations, and, where appropriate,
matches the available data adequately. The resulting series of model simulations is therefore our
best available numerical representation of the available information.
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2.5
HYDROGEOLOGIC DATA
Tables 2 and 3 list the hydrologic properties used in the model of Los Alamos Canyon.
Geologic designation, permeability, and porosity for the hydrogeologic units used
in the model.
TABLE 2.
Permeability,
m2
Geologic
Designation
Hydrogeologic Unit
Porosity
Unit 5, Tshirege member
Qbt5
1.43e-14
0.349
Unit 4, Tshirege member
Qbt4
1.01e-14
0.478
Unit 3t, Tshirege member
Qbt3t
5.10e-13
0.466
Unit 3, Tshirege member
Qbt3
1.01e-13
0.469
Unit 2, Tshirege member
Qbt2
7.48e-13
0.479
Vitric unit, Tshirege member
Qbt1v
1.96e-13
0.528
Glassy unit, Tshirege member
Qbt1g
3.68e-13
0.509
Basal Pumice unit, Tshirege member
Qbtt
1.01e-12
0.473
Cerro Toledo Interval
Qct
8.82e-13
0.473
Otowi Member
Qbof
7.25e-13
0.469
Guaje Pumice Bed
Qbog
1.53e-13
0.667
Tschicoma dacite, in Puye
Tt2
2.96e-13
0.3
Tschicoma dacite, in Puye
Tt1
2.96e-13
0.3
Cerros del Rio Basalt, in Puye
Tb4
2.96e-13
0.3
Puye Formation, fanglomerate
Tpt
4.73e-12
0.25
Puye Formation, Totavi Lentil
Tpf
4.73e-12
0.25
Cerros del Rio Basalt, in Santa Fe Group
Tb3
2.96e-13
0.3
Santa Fe Group
Tsfuv
2.65e-13
0.25
TABLE 3.
Parameters in the van Genuchten model for unsaturated characteristic curve for
each unit [from Robinson et al., 1999]
Hydrogeologic Unit
Geologic
Designation
van Genuchten
α parameter,
Residual
saturation
van Genuchten
n parameter,
dimensionless
(m-1)
Unit 5, Tshirege member
Qbt5
0.17
0.00
1.602
Unit 4, Tshirege member
Qbt4
0.667
0.0037
1.685
Unit 3t, Tshirege member
Qbt3t
2.57
0.00
1.332
Unit 3, Tshirege member
Qbt3
0.29
0.045
1.884
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TABLE 3.
Parameters in the van Genuchten model for unsaturated characteristic curve for
each unit [from Robinson et al., 1999]
Hydrogeologic Unit
Geologic
Designation
van Genuchten
α parameter,
Residual
saturation
van Genuchten
n parameter,
dimensionless
(m-1)
Unit 2, Tshirege member
Qbt2
0.660
0.032
2.090
Vitric unit, Tshirege member
Qbt1v
0.440
0.009
1.660
Glassy unit, Tshirege member
Qbt1g
2.220
0.018
1.592
Basal Pumice unit, Tshirege member
Qbtt
1.520
0.010
1.506
Cerro Toledo Interval
Qct
1.520
0.010
1.506
Otowi Member
Qbof
0.660
0.026
1.711
Guaje Pumice Bed
Qbog
0.081
0.010
4.026
Tschicoma dacite
Tt2
0.100
0.066
2.000
Tschicoma dacite
Tt1
0.100
0.066
2.000
Cerros del Rio Basalt, Puye
Tb4
0.100
0.066
2.000
Puye Formation, Fanglomerate
Tpf
5.000
0.010
2.680
Puye Formation, Totavi Lentil
Tpt
5.000
0.010
2.680
Cerros del Rio Basalt, Santa Fe Group
Tb3
0.100
0.066
2.000
Santa Fe Group
Tsfuv
5.000
0.010
2.680
The process for setting or estimating the hydrologic parameters in these tables followed a series of
steps, as listed in Robinson et al., [1999]. The first table contains permeability and porosity values
used for each unit, and the second table lists the unsaturated hydrologic parameters for the van
Genuchten (1980) formulation used in the present study for the characteristic curves.
The
approach for assigning the hydrologic properties for these units is similar to that of Dander (1997)
in his model of Mortendad canyon, and the MDA G model of Birdsell et al. (1999). Parameter
values assumed in the present study are in most cases exactly the same as or very close to
parameters listed in those reports [from Robinson et al., 1999]. Although 20 individual units are
listed, some have identical properties and could be listed as a single hydrogeologic unit. However,
in the future, as more data become available, we hope to be able to differentiate the material
properties of these units. For example, the Tschicoma dacites (Tt1 and Tt2) and the Cerros del Rio
Basalts (Tb3 and Tb4) are all given the same van Genuchten parameters in the current simulations.
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2.6
TRANSPORT PROPERTIES
Transport of contaminants from the alluvial sediments to the regional aquifer depends on
many variables. Some contaminants can move freely with the water and are referred to as
conservative species. By definition, conservative species do not adsorb onto rocks, decay to other
species, or become involved in chemical reactions and consequently travel at the same rate as a
water molecule in the groundwater. Although conservative species travel with the groundwater,
they are affected by dispersive processes. Subsurface dispersion is a function of both molecular
diffusion and a velocity dependent dispersivity [Fetter, 1999]. Water diffusion is quite slow for
many chemicals and variations in transport caused by this parameter will be overwhelmed by
advective/dispersive effects, thus we fix the water diffusion coefficient to 1x10-9 m2/s. For the
initial simulations presented, a longitudinal dispersivity of 0.1 m is used while transverse
dispersivity is set to 0.01 m. Because the grid spacing is much greater than these values, the results
will be controlled by numerical dispersion. Increasing dispersivity in the model will eventually
result in dispersion greater than the minimum numerical dispersion. The size and shape of the
plume through time depends strongly on the assigned dispersivities, and we present results
showing model sensitivity to these parameters as well as an estimate for the numerical dispersion
associated with the computational grid.
Non-conservative species interact with materials in the subsurface and generally move
more slowly than the groundwater. The ability of a contaminant to adsorb is often described using
the distribution coefficient (Kd). The simplest conceptual model for a non-conservative species
assumes a linear relationship between A) the concentration of the contaminant in water (C g/cm3)
and B) the concentration adsorbed onto the rocks (C* g/g) as: C* = Kd C. Depending on the choice
of units chosen to express the concentrations in water and rock, the units for Kd can change and
must be carefully noted. For the modeling presented, units for Kd are cm3/g. We test the sensitivity
of the system for a range of distribution coefficients, choosing Kd = 1,10,100, and 1000 cm3/gm.
Nearly all distribution coefficients for many common contaminants of concern measured in
Bandelier tuff lie within this range [TA-54 RFI report, 1999]. More complex distribution
relationships can be defined, such as the Freundlich and Langmuir schemes which involve nonlinear relationships between C and C*, however for this study we address only simple linear
adsorbtion.
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3.0 - NUMERICAL MODEL
The Los Alamos Canyon site model is a three-dimensional representation of the
hydrogeologic system, including the surface topography. The numeric modeling is limited to
isothermal water flow and associated transport of a range of chemicals. Transport in the vapor
phase is not included. These simplifications are suggested by the Vadose Zone conceptual model
[LANL, 1996].
3.1
FEHM
The simulations are run with FEHM, a three-dimensional finite-volume heat and mass
transfer code suitable for simulating systems with complex geometries [Zyvoloski et al., 1997].
The governing equations arise from the principles of conservation of water mass, air mass,
contaminant mass, and energy. Darcy's law is assumed to be valid for the liquid phase. The
advection-dispersion equation governs solute transport [Fetter, 1999; Zyvoloski et al., 1997; Jury
et al., 1991] in these analyses.
3.2
Model Domain and Computational Grid
The model domain covers a rectangular map area with the southwest corner at SP(feet)
coordinate (1630492, 1770263) and the northeast corner at SP(feet) coordinate (1638367,
1776825). The model physics are calculated in SI units and the SP data (feet) were converted to
meters for the simulation. The land surface in the model domain is based on Digital Elevation
Model (DEM) data which allow accurate representation of the major features of the mesa/canyon
system.
For the purposes of this numerical model, the horizontal resolution on the mesas and
upstream of contaminant release sites was chosen to be coarse, whereas greater node resolution
was applied in the canyon bottoms. An initial grid resolution of 100 meters is used for the
horizontal and 40 meters for the vertical. Once an initial point distribution for the grid is
established, increased resolution is applied along the canyons. A technique called Octree Mesh
Refinement (OMR) is used to refine elements to four times the initial resolution in all dimensions.
OMR allows for the refinement of the node resolution within the canyon bottoms while leaving the
initial node distribution in other areas. After applying the OMR technique the highest X and Y cell
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resolution is 25 meters and the highest Z cell resolution is 10 meters [from Robinson et al., 1999].
The model surface shown in Figure 3-1 compares favorably to the site topography seen in Figure
2-2. Model geometries of the subsurface hydrogeologic units are based on interpolated data from
existing boreholes and outcrops, and as such, are inherently of a lower resolution than surface
geometry
Northing (SP meters)
DP Canyon
Los Alamos
Canyon
Easting (SP meters)
Qbt5
Qbt4
Qbt3t
Qbt3
Qbt2
Qbt1v
Qbt1g
Qbtt
Qct
Qbof
Qbog
Tt2
Tt1
Tb4
Tpf
Tpt
Tb2
Tsfuv
Figure 3-1.
Map-view of the computational grid. The surface expression of the geologic model is represented at the
resolution of the grid. Los Alamos and DP canyons are readily visible as the higher resolution sections
running through the center of the figure.
The model domain extends vertically from the highest mesa-top (2210 m = 7251 ft.) to
below the water table (1610 m = 5282 ft.) and represents a volume of over 2.55 cubic kilometers.
The stratigraphic configuration used for the model (Figure 3-2) is derived from the LANL sitewide geologic model [Carey et al., 1999]. The geologic model data set is interpolated with the
Stratigraphic Geocellular Modeling (SGM) Software [Stratamodel, Inc., Copyright 1994] to
generate the geologic framework model of continuous surfaces. Surfaces and interfaces are loaded
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into the LaGrit grid generation software [Trease et al., 1996; George, 1997] and a computational
grid is formulated. The grid maintains positive definite coupling coefficients at all volume
interfaces. The final grid contains 136,535 nodes and 766,163 tetrahedral volume elements.
DP Canyon
Los Alamos
Canyon
Easting
498,950
N
Northing 540,450
Figure 3-2.
3.3
Cross-sections of model stratigraphy. Easting 498,950 (SP meters) and Northing, 540,450 (SP meters).
The view is a perspective at a slight angle with the SP coordinate grid shown below the model domain.
Boundary and Initial Conditions
Simulations are isothermal (10o C) and isobaric with no airflow permitted (Pair = 0.1
MPa). Compared to a full treatment of the thermodynamics of compressible gas and water vapor,
the simulations we present have much faster computer runtimes with virtually no loss in the
accuracy of the physics The bottom boundary of the domain was chosen to provide a horizontal
bottom, lying below the water table. The presence of the water table within the model domain
allows us to estimate travel times to this important horizon. For all simulations, the water table is
implemented as a sink for water, thus we examine only transport from the land surface to the water
table. No flow of water or vapor is permitted across the bottom boundary of the domain, and in fact
this boundary is moot because all nodes below the water table are fixed to be fully saturated with
a tracer concentration of zero. As specified, nodes below the water table do not participate in the
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solution algorithm. The side boundaries allow no flow of water mass or chemical mass out of the
domain.
All of the contaminant transport simulations are started from the 3-D steady-state base
simulation of Robinson et al. [1999]. This is done to ensure that the transport simulations are not
affected by transient behavior associated with model initiation. The steady-state initial condition
is meant to represent our best estimate of the system before the hypothesized increase in infiltration
from the Cerro Grande fire. Because flooding and standing water will affect the canyon bottoms
more than the mesa tops or cliff sides, increased infiltration is only applied along Los Alamos
Canyon within the high resolution section of the numeric model grid. DP Canyon is relatively
small and we do not examine perturbations to infiltration in this canyon.
3.4
Numerical formulation used to simulate perched water
The observed thicknesses (i.e. cm-scale) of the paleo-soils controlling perching is such that
direct simulation of these very thin horizons is not possible. An exciting new numerical
implementation of the conceptual model for perched water was developed and added to the FEHM
code for the initial Los Alamos Canyon modeling study of Robinson et al. [1999]. In most finite
difference or finite element codes, including FEHM, when any two connected nodes in the model
connected to one another have a different permeability, a harmonic average permeability is
applied for that connection. The new feature added to the code is to allow the user to specify a
constant multiplier called the permeability reduction factor to any connection on an interface
between two hydrostratigraphic units where this effect is present. In this way, the permeabilities
within each unit are their original values, but the permeability applied for water passing through
the interface is reduced. When the reduction factor makes the permeability at the interface small
enough, lateral diversion or perching can occur, depending on the dip of the interface and the local
recharge rate [from Robinson et al., 1999]. The simulations presented use a permeability reduction
factor of 0.001 at the interface between the Guaje Pumice and the Puye Formation. Sensitivity to
this parameter shows that for 2-D simulations, simulated perched water begins to reproduce the
observations when the permeability reduction factor reaches 0.001.
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3.5
Hypothetical region of contamination: Initial tracer distribution
The distribution of contamination in Los Alamos Canyon is complicated by the long history
of releases from many sites. To simplify the current modeling study, we choose an hypothetical
contaminant release site within our model domain. The hypothetical release site is seeded with
DP Canyon
Los Alamos
Canyon
N
Tracer
input
location
Figure 3-3.
Location of the tracer input. Map colors represent geologic unit and are the same as shown in Figure 3-1
various tracers that are chosen to display a range of geochemical behavior and represent a variety
of contaminants of concern (COCs).
The hypothetical release site includes only the volume of
earth associated with the surface nodes lying between (497,793 < x< 498,007) and (540,430 < y<
540,520), an area of approximately 20,000 m2 containing a volume of approximately 121,000 m3
(Figure 3-3). The hypothetical tracer release area lies in the bottom of Los Alamos Canyon
immediately south of the main facilities at TA-21, and just east of well BH-1134 shown on Figure
2-2. All transport simulations are initiated with 24,792 moles of tracer, an arbitrary amount
resulting from our decision to use an initial conservative tracer concentration of 0.001 moles of
tracer per kilogram of water. For a common conservative LANL COC such as perchlorate with a
molecular weight of 100 g/mole, this is equal to 0.1 g/L or 100 ppm (mass).
Simulations involving non-conservative tracers use the partitioning coefficient (Kd) which
describes the equilibrium ratio of tracer concentration found on the rock to that found in the water.
The initial state for the non-conservative simulations has the total mass in the water plus the total
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amount on the rock equal to 24,792 moles. As Kd is increased for a given simulation, the
concentration found in the water at the source region will drop for a fixed total tracer mass
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4.0 4.1
RESULTS
Summary of the Base Case simulation
The increased infiltration simulations we present are compared to the Base Case simulation
(Case 1, Table 4), which uses best estimates for the in-situ hydrogeologic parameters, infiltration
rates, stratigraphy, and topography from Robinson et al., [1999]. Initially generated as part of the
site-scale Los Alamos Canyon modeling project, the Base Case simulation incorporates thin
interface zones that allow water to perch above the water table as seen in Figure 4-1. The water
(A)
LA canyon
LA canyon
Perched water
Water table
W
E
499350 m
LA canyon
DP
(B)
Perched water
S
N
Water table
m
0.0
Figure 4-1.
1.0
Saturation
Saturation profile on: (A) The E-W cross-section (Northing, 540,450) and (B) The N-S cross-section
(Easting = 497800) for the Base Case. The Base Case is at steady state with respect to saturation. Vertical exaggeration = 1x.
table is the top of the continuous saturated region at the bottom of the figure, while perched regions
are seen as blue (saturated) areas above the water table. Details on the techniques used to bring the
Base Case simulation to a steady-state flow condition are described in Robinson et al. [1999].
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Figure 4-2 shows how a conservative tracer spreads through the Base Case simulation over 100
(A)
Los Alamos Canyon
E
W
m
LA canyon
DP
S
(B)
N
m
Log10(C) = [moles/kg]
Figure 4-2.
Concentration of a conservative tracer at time=100 years after release in Los Alamos Canyon for the
Base Case simulation overlaid on the cross-section of model stratigraphy (same color scheme as Figure
3-2) as seen on Northing, 540,450 (A) and Easting 497800 (B). Vertical exaggeration = 1x.
years. The influence of the perched water on transport can be seen where the plume is diverted
south at the base of the Otowi member (Qbof) in Figure 4-2 (B). This corresponds to the thin blue
region labeled ‘perched water’ on Figure 4-1 (B). Figure 4-3 shows concentration versus time for
a series of nodes with increasing depth below the simulated hypothetical source region beginning
at the land surface and progressing at 10 m intervals to a depth of 50 m. At the surface where the
tracer is introduced, concentration is reduced by a factor of 1000, from 1x10-3 moles/kg to 1x10-6
moles/kg, in less than twenty years (7300 days) as the tracer is flushed to depth by the infiltrating
water. At each increasing depth below the source, the maximum concentration is reduced as the
plume spreads, and the peak concentration is shifted to later times.
Non-conservative tracers move more slowly than conservative tracers, as shown in Figure
4-4 for the Base Case combined with a tracer having Kd = 10 cm3/g. The initial water concentration
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Concentration (moles/kg)
0.0003
0.00025
surface
10m
20m
30m
40m
50m
0.0002
0.00015
0.0001
5 10-5
0
0
10
20
30
40
50
60
Time (years)
Figure 4-3.
Conservative tracer concentration as a function of time for the Base Case simulation. The surface is
initially at a concentration of 0.001 moles/kg.
1 0-4
Concentration (moles/kg)
1 0-6
1 0-8
1 0-10
surface
10m
20m
30m
40m
50m
1 0-12
1 0-14
Figure 4-4.
Kd = 10 cm3/g
0
20
40
60
Time (years)
80
100
Non-conservative tracer (Kd = 10 cm3/g), concentration as a function of time for the Base Case simulation. The surface is initially at a concentration of 1.38 x 10-5 moles/kg.
in the source region for this simulation is 1.38 x 10-5 moles/kg. Concentration at the source drops
by only a factor of two in 100 years to 7x10-6 moles/kg, while nodes deeper in the profile show
very small changes in concentration with time. At the end of the simulation period (100 years), the
tracer plume has barely reached 50 m (Figure 4-4), and no tracer mass has reached the water table.
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4.1.1
Dispersive effects
We next present results from a sensitivity analysis of dispersion on the scale of the
Moles reaching the water table
contaminant plume for the Base Case. Figure 4-5 shows tracer mass transfer to the water table as
1000
Longitudinal
dispersivity
(αL)
0.0_m
0.1_m
1.0_m
5.0_m(500d)
5.0_m(10d)
10.0_m
20.0_m
50_m(500d)
50_m(10d)
100
10
30
40
50
60
70
80
90
100
Time (years)
Figure 4-5.
Effects of dispersion on tracer transport to the water table. Simulations marked with 10d use a 10 day
timestep for the transport solution, whereas the rest use a 500 day timesteps for the transport solution.
The input mass is 24,792 moles of tracer.
a function of time for input model longitudinal dispersivities (αL) of between 0.1 m and 50 m.
Transverse dispersivity is set to 1/10 longitudinal for all cases presented, as suggested by the field
scale studies of Lallemeand-Barnes and Peaudecerf [1978]. Because intrinsic numerical
dispersion associated with the grid has a finite value, there is a point where reducing the model
input dispersion coefficient will not change the results of the plume development. On Figure 4-5,
this point appears to occur between 5 m and 10 m. This value is not surprising given the grid
spacing of approximately 10 m in the region of the plume, however the complex 3D nature of the
simulations required investigation of this important parameter. Initial attempts to differentiate the
model sensitivity to dispersion based on summing local differences in plume concentration were
not successful because the local differences overwhelmed the statistics. By stepping back from the
local scale and watching only the total mass, the scheme shown in Figure 4-5 provides a clear
indication of the effects of different dispersion coefficients as well as a defensible estimate of
numerical dispersion at the scale of the simulated plume. Thus, the transport simulations have a
minimum effective dispersivity of approximately 5 to 10 m. Finally, the minimum numerical
dispersivity of the system is quite important for understanding sensitivity analyses of this
parameter.
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4.2
Increased infiltration scenarios
Increased infiltration can be applied to the simulation domain in a variety of ways. Two
end-members, which bracket the spatial dimensions of the system, are distributed and focused
infiltration. The distributed end-member represents a relatively homogeneous increase throughout
the bottom of Los Alamos Canyon, whereas the spatially focused scenario could represent standing
water such as a pond. The temporal aspect of the system is dealt with by allowing simulated ponds
to remain for short (3 days) and long (30 days) periods of time. Both a medium and small pond are
simulated, with the spatial limits of these ponds shown in Figure 4-6. The ponds are situated near
(B)
(A)
N
Figure 4-6.
Location and size of (A) The small pond, and (B) the medium pond.
the center of the domain to ensure that boundary effects are minimized.
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The distributed infiltration scenarios assume that the system remains disturbed for the first
5 years post-fire, at which time the system returns to its pre-fire infiltration levels. Table 4
summarizes the 9 cases of infiltration (including the Base Case) which we examine . The steadyTABLE 4.
Summary of 12 infiltration scenarios
Simulation
ID
(Case)
Simulation
Name
Distributed (D)
or
Focused (F)
Time of
perturbation
LA Canyon
Infiltration
Rate
m/yr
1
Base
D
NONE
0.4
2
2x Base
D
5 years
0.8
3
5x Base
D
5 year
2.0
4
10x Base
D
5 years
4.0
5
20x Base
D
5 years
8.0
6
small pond
F
30 days
240.
7
small pond
F
3 days
240.
8
med. pond
F
30 days
240.
9
med. pond
F
3 days
240.
state mass flow rate for the Base simulation is Qin = Qout = 16.5 kg/s. The different infiltration
scenarios each have a unique water mass breakthrough curve at the water table as shown in Figure
4-7. The total mass introduced to the system is the integration of the area between the curves shown
and the steady state Base flux of 16.5 kg/s. The greatest increase to the Base Case is Case 5, with
8x Base infiltration for 5 years, although not shown on the figure below, the maximum mass flow
rate reaching the water table for Case 5 is about 32 kg/s. The 30-day medium pond (Case 8) results
in peak outflow of nearly the same magnitude as the Case 3 (5x Base flow for 5 years), however
the total mass input for Case 3 is much higher.
4.2.1
Changes to saturation
The addition of water to the steady-state Base simulation causes a pulse of water to move
through the system. This pulse of water raises saturations in the subsurface as it passes (Figure 48). The different infiltration scenarios have a wide range of saturation responses. For example, in
the high-flow distributed cases (Cases 4 and 5) at 50 m depth, the saturations rise to near steady for
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22
(kg/s)1
(kg/s)2
(kg/s)3
(kg/s)4
(kg/s)5
(kg/s)6
(kg/s)7
(kg/s)8
(kg/s)9
Mass flow rate (kg/s)
21
20
19
18
17
0
5
10
15
20
Time (years)
Figure 4-7.
Total water mass flow rate to the water table as a function of time for simulation scenarios 1-9. The total
amount of water input for Case 5 (20 x Base flow for 5 years) is much greater than either of the 30-day
pond scenarios and the peak flow rate for Case 5 (not shown) is 32.8 kg/s at 5.3 years. The 9 cases return
to the steady state Base Case mass flow rate (16.5 kg/s) by approximately 100 years.
approximately 3-4 years. The lower flow distributed cases (Cases 2 and 3) lead to smoothed
profiles at 50 m depth. Similarly, the 3-day focused infiltration cases (Cases 7 and 9) result in a
smoothed saturation front moving past the 50 m depth mark. Finally, the 30-day focused
infiltration cases (Cases 6 and 8) yield sharp saturation fronts that move quickly past the 50 m
depth. Because the medium and small ponds both lie above the column of nodes being examined,
the medium and small 30-day ponds (Cases 6 and 8) have virtually the same response, as do the
two 3-day pond simulations (Cases 7 and 9).
4.2.2
Transport of a conservative tracer
Figure 4-9 shows the conservative tracer concentration at the surface as a function of time
for Cases 1- 9. The combination of high infiltration rate (240 m/year) and long duration (30 days)
used for Cases 6 and 8 cause nearly all 24,792 moles of input tracer to be flushed from the surface
region during the lifetime of the simulated pond.
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1
sat1
sat2
sat3
sat4
sat5
sat6
sat7
sat8
sat9
0.9
Saturation
0.8
0.7
0.6
0.5
0.4
0
5
10
15
Time (years)
Figure 4-8.
Saturation at a point 50 m below the surface as a function of time for infiltration cases 1-9.
conc1
conc2
conc3
conc4
conc5
conc6
conc7
conc8
conc9
Concentration (moles/kg)
0.001
0.0001
1 0-5
1 0-6
1 0-7
1 0-8
0
5
10
15
Time (years)
Figure 4-9.
Conservative tracer concentration at the source as a function of time. Numbers in the Legend refer to
the 9 Cases presented in Table 4. Both the small and medium 30-day ponds (Cases 6 and 8) result in
extremely rapid removal of conservative tracers in the top few meters of the domain. The 3-day ponds
(Cases 7and 9) results in long term behavior similar to the 2x background simulation (Case 2).
Migration of the hypothetical conservative tracer plume to 30 m depth can be seen in Figure
4-10. The most rapid transport is for the small and medium 30-day ponds (Cases 6 and 8), while
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Concentration (moles/kg)
0.0001
conc1
conc2
conc3
conc4
conc5
conc6
conc7
conc8
conc9
1 0-5
0
5
10
15
20
Time (years)
Figure 4-10. Conservative tracer concentration at 30 m below the surface as a function of time for Cases 1-9. Simulation numbers (2,7 and 9) are nearly the same after 5 years.
the 3-day ponds (Cases 7 and 9) nearly match the 2xBase infiltration simulation (Case 2). The
maximum concentration seen for each case in Figure 4-10 is related to the total amount of water
introduced to the system (Figure 4-7), as well as the timing of the infiltration. For example, Case 5
adds the largest volume of water to the system, but because this volume is added over a relatively
long time, the dilution of the tracer front at 30 m is less than occurs for Cases 6 and 8 where a
smaller total flux is input during a much shorter period of time. Integration of the weighted areas
under the curves in Figure 4-10 yields the total mass which has passed through the node at 30 m
depth. The areas must be weighted for the integration to work on Figure 4-10 because this figure
is presented in log10 scale for concentration. This mental integration exercise shows that for the
Base Case, dilution and lateral spreading are the most limited, while Cases 6 and 8 have the most
dilution and lateral spreading. Figure 4-11 shows the cumulative transport after 100 years of a
conservative tracer to the water table for Cases 1 through 9.
4.2.3
Transport of a non-conservative tracer
We next present a series of simulations that explore variations involving the chemistry of
the contaminant. Table 4 lists the parameters used for the infiltration scenarios that were run with
Kd=1 cm3/g. Kd =1 was chosen because Kd = 10 cm3/g yielded very limited transport in the Base
Case. For Kd > 1. cm3/g, transport is extremely limited during the 100-year time-frame examined
and we do not present the results of these simulations. Figure 4-12 shows the concentration as a
function of time at the source for simulation Cases 1, 4, 6, and 7. The initial concentration in the
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3000
Moles1
Moles2
Moles3
Moles4
Moles5
Moles6
Moles7
Moles8
Moles9
Moles reaching the water table
2500
2000
1500
1000
500
0
0
20
40
60
Time (years)
80
100
Figure 4-11. Conservative tracer movement to the water table for the infiltration Cases 1-9. The initial mass of tracer
at the source region is 24,792 moles corresponding to an initial source concentration of 100 ppm (mass).
water at the source is approximately 1.2 x10-4 moles/kg, and in all cases nearly 99.9% is removed
by 100 years.
Although removal from the source region occurs in about 100 years, deeper
migration is limited by the non-conservative nature of this hypothetical tracer. Figure 4-13 shows
the arrival of the tracer front at 30 m below the source, while Figure 4-14 shows the change in
concentration at a node 50 m below the source for the same four simulations. The non-conservative
tracer effects become very apparent at 50 m, where the peak concentration for the highest-flow
simulations (Cases 4 and 6) has not reached this node at 100 years (Figure 4-14), while the
conservative tracer used with the Base Case shows peak concentrations reaching this depth after
only approximately 22 years (Figure 4-3). Additionally, none of the Kd = 1.0 cm3/g simulations
show any amount of tracer reaching the water table in the 100 year period examined.
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Concentration (moles/kg)
0.0001
Base_case
10x_Base
30_day_pond
3_day_pond
10-5
10-6
10-7
0
20
40
60
Time (years)
80
100
Figure 4-12. Non-conservative tracer concentration as a function of time at the surface. Kd = 1.0 cm3/g. The simulations shown are Cases 1, 4, 6, and 7 from Table 4.
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Concentration (moles/kg)
2 10-5
10-5
9 10-6
Base_case
10x_Base
30_day_pond
3_day_pond
8 10-6
7 10-6
6 10-6
0
20
40
60
Time (years)
80
100
Figure 4-13. Non-conservative tracer concentration as a function of time at 30 m below the surface. Kd = 1.0 cm3/g.
The simulations shown are Cases 1, 4, 6, and 7 from Table 4.
Concentration (moles/kg)
10-5
10-6
Base_case
10x_Base
30_day_pond
3_day_pond
10-7
0
20
40
60
Time (years)
80
100
Figure 4-14. Non-conservative tracer concentration as a function of time at 50 m below the surface. Kd = 1.0 cm3/g.
The simulations listed (top to bottom) are Cases 1, 4, 6, and 7 from Table 4.
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5.0 -
Conclusions
This paper examines the interplay between infiltration and subsurface tracer transport
beneath Los Alamos Canyon. The results are relatively general and can be used to make informed
decisions about transport in other canyons within the Pajarito Plateau. Results from the Base Case
show that tracer transport in the system is heavily influenced by lateral flow within perched
regions. The Base simulation is also used to show that with respect to tracer transport to the water
table, the currently used numerical grid has an effective longitudinal dispersivity of between 5 and
10 m.
Of the 5 distributed infiltration scenarios presented, Case 5 (20x Base infiltration for 5
years) causes the largest and most rapid flux of tracer to the water table, while Case 2 (2x Base
infiltration for 5 years) is nearly identical to the Base Case with respect to tracer transport to the
water table. Thus, for the distributed increased infiltration scenarios, a factor of 10 increase in
infiltration leads to large differences in travel times. The modeling suggests that because the
system is quite sensitive to increases in alluvial infiltration, measurements should be made to better
characterize this important parameter for future studies. Additionally, increased infiltration due to
the Cerro Grande Fire needs to be examined from the combined perspectives of experimenter and
modeler. Shallow monitoring stations in the canyon bottoms could help to define moisture flux
through the alluvium and continued moisture monitoring in existing boreholes may help to show
if the system is responding in a manner similar to any of the hypothetical infiltration scenarios.
The scenarios involving focused infiltration suggest that the system is also very sensitive
to the duration of ponding events. The 3-day pond of Case 7 is nearly identical to the Base Case
with respect to tracer transport to the water table, while the 30-day pond of Case 6 shows similar
transport characteristics to Case 4 (10x Base for 5 years). Therefore, we suggest that if ponds are
found to form in any of the canyon bottoms, they should be removed as quickly as possible (less
than a week) to limit perturbations to the system. This suggestion is of particular importance to the
water retention structure in Pajarito Canyon which has a drain height well above (3 ft.) the canyon
floor. The low-head weir, which has been constructed at the confluence of Pueblo and Los Alamos
Canyon, may be a good place to examine ponding effects on saturation profiles because a
monitoring network is currently being installed at this critical junction. Furthermore, monitoring
of conservative constituents of the post-fire chemical plume could also help to differentiate
between the different conceptual models of increased infiltration.
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Acknowledgements
This project was funded by the Los Alamos National Laboratory’s Environmental
Restoration Project. Much of the information used to build the Los Alamos Canyon model was
collected from the various RFI and CMS documents of the LANL Environmental Restoration
Project. Steve Reneau was particularly helpful in sharing his knowledge of local hydrological data
and current observations from the field. This report also benefited from data gathered by the Burn
Area Emergency Response Team during their effort to characterize the damage in the weeks
following the Cerro Grande Fire. Finally, we would like to thank Diana Hollis for her support and
insight during initiation of this project.
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