Modeling Transport in Los Alamos Canyon: Effects of Hypothetical Increased Inﬁltration After the Cerro Grande Fire

LA-UR-00-5923 December 2000 ER2000-XXXX A Department of Energy Environmental Cleanup Program

Modeling Transport in Los Alamos Canyon: Effects of Hypothetical Increased Inﬁltration after the Cerro Grande Fire

Los Alamos Los Alamos National Laboratory, an affirmative action/equal opportunity employer, is operated by the University of California for the United States N A T I O N A L Department of Energy under contract W-7405-ENG-36. L A B O R A T O R Y Los Alamos, NM 87545 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 responsi- bility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process dis- closed, 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 Univer- sity 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

ER2000-XXXX iii DRAFT September 26, 2001 List of Figures

1-1 Cerro Grande Fire intensity map. 1-2 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 2-2 Geographical information for Los Alamos Canyon and the surrounding area. 2-3 Geologic framework model for the Los Alamos Canyon model study area 2-4 Simpliﬁed site stratigraphy. 2-5 Schematic diagram of the conceptual model for flow and transport in the vadose zone of the Pajarito Plateau.

3-1 Map view of the computational grid. 3-2 Cross-section of model stratigraphy. 3-3 Location of hypothetical tracer input.

4-1 Base simulation saturation profile on two cross-sections. 4-2 Base simulation conservative tracer concentration after 100 years. 4-3 Base simulation conservative tracer concentration as a function of time. 4-4 Base simulation non-conservative tracer concentration as a function of time. 4-5 Effects of dispersion on tracer transport to the water table. 4-6 Location and size of A) The small pond and B) The medium pond 4-7 Total mass flow rate to the water table for infiltration Cases 1-9. 4-8 Saturation as a function of time at 50 m depth for infiltration Cases 1-9. 4-9 Conservative tracer concentration as a function of time at the source, Cases 1-9. 4-10 Conservative tracer concentration as a function of time at 30 m, Cases 1-9. 4-11 Conservative tracer movement to the water table as a function of time, Cases 1-9. 4-12 Non-conservative tracer concentration with time at the surface. 4-13 Non-conservative tracer concentration with time, 30 m below the surface. 4-14 Non-conservative tracer concentration with time, 50 m below the surface.

List of Tables

1 Stratigraphic nomenclature of the Pajarito Plateau. 2 Physical parameters used in the site model (permeability and porosity). 3 Physical parameters used in the site model (van Genuchten parameters). 4 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 High intensity Medium intensity Low intensity Unburned 5x vertical Figure 1-1. Cerro Grande Fire intensity map. Los Alamos National Laboratory lies within the closed black line. Di- amond 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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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

ER2000-xxx 2 DRAFT September 26, 2001 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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anyon

Pueblo Canyon Los Alamos Canyon Bayo Canyon

Los Alamos Canyon

Pajarito Canyon

Ca Sandia Canyon ñon de Valle Cedro Canyon

Mortandad Canyon

Cañada del Buey Water Canyon Potrillo Canyon

Frijoles Canyon

Potrillo Canyon

Water Canyon Ancho Canyon

Frijoles Canyon

Chaquehui Canyon Lummis Canyon Ancho Canyon

Alamo Canyon

Rio Grande

Capulin Canyon

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 Qbtt Qbt1v Tpt Tt2 Qbt1g Qbt4 Tt1 Qbt3t Qct Qbt3 Qbof Qbt2 Qbt5 Tb2 Tb4 Tpf Qbog 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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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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TABLE 1. Stratigraphy of the Los Alamos Canyon model [Carey et al., 1999].

Group/Formation Unit Name Symbol Bandelier Tuff (Tshirege Member) 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 Formation Puye fanglomerate Tpf Totavi Lentil Tpt Cerros del Rio basalts Basalt 4 Tb4 Basalt 3 Tb3 Basalt 2 Tb2 Basalt 1 Tb1 Tschicoma Formation Tschicoma dacite Tt2 Tschicoma dacite Tt1 Santa Fe Group Chaquehui (volcaniclastic) aquifer unit Tsfuv Santa Fe Group undifferentiated Tsfu

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6600 6600

Otowi (Qbof) 6400 6400 Guaje Pumice Bed (Qbog) 6200 6200 Puye Formation, fanglomerate (Tpf) Tschicoma dacite (Tt1, Tt2) 6000 6000 Elevation Puye Formation, fanglomerate (feet) (Tpf) 5800 5800

Puye, Totavi Lentil 5600 5600 (Tpt) Santa Fe Group, aquifer unit (Tsfuv) 5400 5400

5200 5200 Cerros del Rio basalt (Tb2)

5000 5000 24681012

Figure 2-4. Simplified site stratigraphy. Extracted from the numeric representation of the Sitewide Geologic Model.

2.3 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

ER2000-xxx 9 DRAFT September 26, 2001 Post Cerro Grande Transport Modeling in Los Alamos Canyon 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 clay- lined 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)

ER2000-xxx 11 DRAFT September 26, 2001 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.

TABLE 2. Geologic designation, permeability, and porosity for the hydrogeologic units used in the model.

Geologic Permeability, Hydrogeologic Unit Porosity Designation m2

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]

vanGenuchten Residual vanGenuchten Geologic Hydrogeologic Unit α parameter, saturation n parameter, Designation (m-1) dimensionless

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]

vanGenuchten Residual vanGenuchten Geologic Hydrogeologic Unit α parameter, saturation n parameter, Designation (m-1) dimensionless

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 3 must be carefully noted. For the modeling presented, units for Kd are cm /g. We test the sensitivity 3 of the system for a range of distribution coefficients, choosing Kd = 1,10,100, and 1000 cm /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 non- linear 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

ER2000-xxx 17 DRAFT September 26, 2001 Post Cerro Grande Transport Modeling in Los Alamos Canyon 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

DP Canyon Northing (SP meters) Los Alamos Canyon

Easting (SP meters) Qbtt Qbt1v Tpt Tt2 Qbt1g Qbt4 Tt1 Qbt3t Qct Qbt3 Qbof Qbt2 Qbt5 Tb2 Tb4 Tpf Qbog 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.

Los Alamos DP Canyon Canyon

Easting 498,950

Northing 540,450

Figure 3-2. 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.

3.3 Boundary and Initial Conditions

o Simulations are isothermal (10 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

ER2000-xxx 19 DRAFT September 26, 2001 Post Cerro Grande Transport Modeling in Los Alamos Canyon 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

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 - RESULTS 4.1 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

WEWater table

499350 m

DP LA canyon (B) Perched water

S Water table N

0.0 Saturation 1.0

Figure 4-1. 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. Ver- tical 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

Los Alamos Canyon (A)

W E

DP LA canyon (B)

S N

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 3 4-4 for the Base Case combined with a tracer having Kd =10cm /g. The initial water concentration

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0.0003

0.00025 surface 0.0002 10m 20m 30m 0.00015 40m 50m 0.0001

5 10-5 Concentration (moles/kg) 0 0 102030405060 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.

10-4

10-6

10-8

-10 10 surface 10m 20m 10-12 30m

Concentration (moles/kg) 40m 3 50m Kd = 10 cm /g 10-14 0 20 40 60 80 100 Time (years) 3 Figure 4-4. Non-conservative tracer (Kd =10cm /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 contaminant plume for the Base Case. Figure 4-5 shows tracer mass transfer to the water table as

1000 Longitudinal dispersivity α ( L) 0.0_m 0.1_m 100 1.0_m 5.0_m(500d) 5.0_m(10d) 10.0_m 20.0_m 50_m(500d) 50_m(10d) 10

Moles reaching the water table 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.

α a function of time for input model longitudinal dispersivities ( L) 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

(A) (B)

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 steady-

TABLE 4. Summary of 12 inﬁltration scenarios

LA Canyon Simulation Distributed (D) Simulation Time of Inﬁltration ID or Name perturbation Rate (Case) Focused (F) 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 4- 8). 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 21 (kg/s)6 (kg/s)7 (kg/s)8 (kg/s)9 20

Mass flow rate (kg/s) 18

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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sat1 0.9 sat2 sat3 sat4 0.8 sat5 sat6 sat7 0.7 sat8 sat9 Saturation 0.6

0.5

0.4 051015 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 0.001 conc4 conc5 conc6 0.0001 conc7 conc8 conc9 10-5

10-6

10-7 Concentration (moles/kg)

10-8 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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0.0001

conc1 conc2 conc3 conc4 conc5 conc6 conc7 conc8 conc9 Concentration (moles/kg) 10-5 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. Sim- ulation 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 3 3 Kd=1 cm /g. Kd =1 was chosen because Kd =10cm /g yielded very limited transport in the Base 3 Case. For Kd >1.cm /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 2500 Moles4 Moles5 Moles6 Moles7 Moles8 2000 Moles9

1500

1000 Moles reaching the water table

500

0 0 20406080100 Time (years) 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 3 only approximately 22 years (Figure 4-3). Additionally, none of the Kd = 1.0 cm /g simulations show any amount of tracer reaching the water table in the 100 year period examined.

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0.0001 Base_case 10x_Base 30_day_pond 3_day_pond

10-5

10-6 Concentration (moles/kg)

10-7 0 20 40 60 80 100 Time (years)

3 Figure 4-12. Non-conservative tracer concentration as a function of time at the surface. Kd = 1.0 cm /g. The simulations shown are Cases 1, 4, 6, and 7 from Table 4.

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2 10-5

10-5 9 10-6 Base_case -6 8 10 10x_Base 30_day_pond -6 3_day_pond

Concentration (moles/kg) 7 10

6 10-6

0 20 40 60 80 100 Time (years) 3 Figure 4-13. Non-conservative tracer concentration as a function of time at 30 m below the surface. Kd = 1.0 cm /g. The simulations shown are Cases 1, 4, 6, and 7 from Table 4.

10-5

10-6

Base_case 10x_Base

Concentration (moles/kg) 30_day_pond 3_day_pond

10-7 0 20 40 60 80 100 Time (years) 3 Figure 4-14. Non-conservative tracer concentration as a function of time at 50 m below the surface. Kd = 1.0 cm /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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6.0 - REFERENCES

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Smith, J., Hardesty, B., McCranie, N. (1999a). “Quarterly Pore Gas Sampling at TA-54 MDA L and MDA G First Quarter FY1999”, PMC-LA-99-002. Smith, J., Hardesty, B., McCranie, N. (1999b). “Quarterly Pore Gas Sampling at TA-54 MDA L and MDA G Second Quarter FY1999”, PMC-LA-99-003. Smith, J., Crocker, J., McCranie, N. (1998). “Quarterly Pore Gas Sampling at TA-54 MDA L and MDA G Second Quarter FY1998”, PMC-LA-98-009. Trease, H., George D., Gable C. W., J. Fowler, A. Kuprat, A. Khamyaseh (1996). “The X3D Grid Generation System Numerical Grid Generation in Computational Fluid Dynamics and Related Fields”, B. K. Soni, J. F. Thompson, H. Hausser and P. R. Eiseman, Engineering Research Center, Mississippi State Univ. Press. Trujillo, V., Gilkeson, R., Morgenstern, M., Kreir, D. (1998). “Measurements of Surface Emission Flux Rates for Volatile Organic Compounds at TA-54”, LA-13329 van Genuchten, M.T., (1980). “A Closed-Form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils,” Soil Science Society of America Journal 44, 892-898 Vaniman, D., G. Cole, J. Gardner, J. Conaway, D. Broxton, S. Reneau, M. Rice, G. WoldeGabriel, J. Blossom, and F. Goff, (1996). “Development of a Site-Wide Geologic Model for Los Alamos National Laboratory,” Los Alamos National Laboratory unpublished document. Zyvoloski, G.A., B.A. Robinson, Z.V. Dash, and L.L. Trease, (1997). “Summary of the Models and Methods for the FEHM Application - A Finite Element Heat- and Mass-Transfer Code”, Los Alamos National Laboratory manuscript LA-13307-MS.

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