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EPA Comment

1-15-1 Water Level Fluctuations in SNL-13.

DOE/WIPP-12-3489 p. 143 states “SNL-13 was also excluded [from the Culebra groundwater analysis] 

due to a sudden rise and then sudden stabilization following the drilling of a new oil or gas well nearby.” 

Please address the following:

1. Identify and provide the location, depth, and purpose of the ‘new’ nearby wells.

2. What activities took place at the nearby wells during the time of the changes in SNL-13, such as 

fluid injection? Provide pressure histories, volumes and fluid types, fluid enhancements (e.g. 

fracking fluids or proppants).

3. Provide a chronological history of the activities in the nearby wells compared to the “sudden” 

changes in adjusted freshwater heads recorded in SNL-13.

4. Were water levels in other monitoring wells influenced by activities at nearby wells?

DOE Response

All four points raised in the comment are addressed by text in Appendix HYDRO (pages HYDRO-22 and 

23). The text and figure are also provided below:

SNL-13 has seen large fluctuations since a rise in 2010 attributed to oil and gas drilling 

in its vicinity. Figure HYDRO-15 shows both water levels and pressure transducer 

readings, along with spud dates obtained from the New Mexico Oil Conservation 

Division well search application (http://www.emnrd.state.nm.us/ocd/) for three sections 

surrounding SNL-13, T22S R30E Section 36, T23S R30E Section 1, and T23S R31E 

Section 6 (some wells in these sections did not have reported spud dates). In February 

2012 an oilfield truck struck and became stuck on the SNL-13 surface casing, requiring 

the surface casing to be repaired (see the drop in pressure just after last peak in pressure 

transducer data shown in Figure HYDRO-15). The water levels and pressure transducer 

observations at SNL-13 appear to show effects of a longer-term slow rise and decay, 

beginning in early 2010, along with shorter-term rapid rises and decays through 2011

and 2012, some of which correspond closely to reported spud dates of nearby oil and gas 

wells. Neither the short- or long-term rises are observable in nearby Culebra wells SNL17 (Figure HYDRO-14) or H-4bR (Figure HYDRO-13).

Data reported by the New Mexico Oil Conservation Division (NMOCD) do not include well coordinates, 

only township, range, and section. Not all wells in the NMOCD data include a spud date. Some of the 

vertical lines in Figure HYDRO-15 may be associated with wells more than a mile away from SNL-13, 

and some oil wells drilled close to SNL-13 may not have reported spud dates. These data limitations 

prevent more detailed correlation between drilling of oil wells and changes in water level at SNL-13.
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Figure HYDRO-15. Water Levels (Symbols) and Pressure Transducer Data (Continuous 

Line) for Culebra Well SNL-13, with Spud Dates of Nearby Oil Wells (Vertical Lines).

The information available on oil wells drilled in the vicinity of SNL-13 includes data from the New 

Mexico Oil Conservation District (OCD), which were used to prepare Figure HYDRO-15 and the text 

repeated above. Table 1 is a sample, illustrating the type of data available for wells in Township 22S, 

Range 30E, Section 36. The reported OCD data have been filtered to only show wells with reported spud 

dates since 1/1/2009 (many wells do not have a reported spud date).
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Table 1. Sample of data available from OCD for T22S R30E Section 36, in the vicinity of 

SNL-13 (used in Figure HYDRO-15)

API Well Name Well 

Number Section Township Range Spud Date Current Operator

30-015-

36722

JAMES RANCH 

UNIT #112H 36 22S 30E 1/12/2009 [260737] BOPCO, L.P.

30-015-

37046

JAMES RANCH 

UNIT #113H 36 22S 30E 5/5/2009 [260737] BOPCO, L.P.

30-015-

37062

JAMES RANCH 

UNIT #107H 36 22S 30E 5/18/2009 [260737] BOPCO, L.P.

30-015-

37064

JAMES RANCH 

UNIT #105H 36 22S 30E 6/14/2009 [260737] BOPCO, L.P.

30-015-

37063

JAMES RANCH 

UNIT #106H 36 22S 30E 9/9/2009 [260737] BOPCO, L.P.

30-015-

37175

JAMES RANCH 

UNIT #103H 36 22S 30E 10/7/2009 [260737] BOPCO, L.P.

30-015-

37271

JAMES RANCH 

UNIT #104H 36 22S 30E 10/31/2009 [260737] BOPCO, L.P.

30-015-

38288

JAMES RANCH 

UNIT #129H 36 22S 30E 2/27/2011 [260737] BOPCO, L.P.

30-015-

38308

JAMES RANCH 

UNIT #130H 36 22S 30E 6/8/2011 [260737] BOPCO, L.P.

No detailed location, depth, purpose (aside that they are oil wells), or information on activities in the 

wells is provided by OCD, but the observed effects are likely due to the drilling penetrating the Culebra 

Member of the Rustler Formation while drilling for hydrocarbons in deeper formations. No information 

on fluid injection, pressure histories, volumes, fluid types, or fluid enhancements is available. 

As indicated in the last sentence of text from Appendix HYDRO, these fluctuations are not observed in 

other nearby Culebra wells (e.g., see Figure HYDRO-14).
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EPA Comment

1-15-2 Shaft Extensometers Not Taking Recordings.

DOE is not replacing the failed monitoring instruments in the shaft. However, EPA Section 42, 

Monitoring requirements expects, “...extent of deformation...” and “...brittle deformation...” to be 

monitored. Please provide a justification to discontinue measuring these characteristics in the WIPP 

shafts as these measurements are used to calibrate numerical models and predict closure rates. 

Additionally, identify how this requested information will be addressed in the future.

DOE Response

Title 40 CFR Part 194.42(a) lists the minimum system monitoring parameters for analysis, which include:

(2) Stresses and extent of deformation of the surrounding roof, walls, and floor of 

the waste disposal room;

(3) Initiation or displacement of major brittle deformation features in the roof and 

surrounding rock;

The analyses of these two parameters, as indicated in 40 CFR 194.42, are intended for disposal units and 

not shafts. Since the entire lengths of the shafts are routinely inspected, unusual deformation can be 

readily observed. With respect to both safety and operational perspectives, visual observations in the 

shafts are actually more comprehensive than single-point shaft extensometer data, which are separated by 

almost 500 feet. Additionally, existing shaft closure plans are not affected by any reduction in shaft 

measurements.

The shaft monitoring data were used for operational considerations and were not used to validate 

modeling activities since submittal of the original compliance application. As stated above, "extent of 

deformation" and "brittle deformation" monitoring of the waste panels and shaft stations is performed to 

meet 40 CFR194.42 requirements under the Compliance Monitoring program (SNL 2013). Since the 

subject shaft data is not used to support long-term compliance activities, continuing to gather data from 

this instrumentation is unnecessary.

Reference:

Sandia National Laboratories (SNL). 2013. Sandia National Laboratories Compliance Monitoring 

Parameter Assessment for 2013 (November 2013). ERMS 561163. Sandia National Laboratories,

Carlsbad, NM.
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EPA Comment

1-15-3 Derivation of Annual Culebra Water Level Map.

CRA-2014 Section 42.8 Changes or New Information Since the CRA-2009 discusses changes to the 

process used to derive the Culebra groundwater flow parameters that is used to prepare the annual water 

level map. Please address the following:

1. For each yearly calculation (ERMS 558589, Section 2.3.2.2), if the monitored freshwater heads 

have changed, do the 100 calibrated T-Fields need to be recalculated? If not, why? 

2. ERMS 557633 Section 2.1 states, "The PA MODFLOW model T (transmissivity), A (anisotropy) 

and R (recharge) input fields are appropriately averaged across 100 realizations, producing a 

single average MODFLOW flow model." Provide information as to how averaging is done with 

examples.

3. For the averaged MODFLOW model, T (transmissivity), A (anisotropy) and R (recharge) are 

fixed while a subset of the boundary conditions is modified (ERMS 557633, Section 2.1). Please 

describe how the new boundary conditions are determined and implemented. If this simply 

involves raising or lowering the heads along the boundaries to best match the observed water 

levels within the modeled area, describe how well the assigned boundaries honor the water levels 

in the nearest monitoring wells both inside and outside the model area.

4. If the step-by-step creation of the annual Culebra water level map is the same as that provided

during 2012 and 2013 inspections, please denote as such. If generating the annual water level 

map differs from what has been provided during inspections, please provide these steps.

DOE Response

The numbered points in the EPA comment are addressed below in correspondingly numbered points.

1. The monitored freshwater heads in the Culebra do change slightly from year to year, but the TFields do not need to be re-calibrated because of these fluctuations. The calibration is based on a 

snapshot of water levels (approximating a steady-state), and the transient response of several 

large-scale pumping tests (see detailed description and references in CRA-2014 Appendix 

TFIELD pages TFIELD-66 through TFIELD-84). The water levels do change, but the calibration 

process which compares model-generated responses to large-scale pumping tests still defines the 

distribution of permeability in the model in the vicinity of the WIPP Land Withdrawal Boundary. 

The existing interpretation of pumping tests used in CRA-2014 Appendix TFIELD remain valid, 

and therefore so do the calibrated T-Fields.

2. The averaging process involves computing average MODFLOW properties at each model cell 

across all 100 realizations; it is described in detail in the body of the Sandia annual contour map 

reports (Kuhlman 2009 – 2012), with input/output files and examples in the Appendix: Section 5, 

“Run Control Narrative”. These reports discuss the details of the process used to create the 

contour map included in the Annual Site Environmental Report. The individual reports are 

referenced on page HYDRO-34 of Appendix HYDRO in CRA 2014; the references are repeated 

below. 

Kuhlman, K.L. 2009. Analysis Report for Preparation of 2008 Culebra Potentiometric Surface Contour 

Map. ERMS 552005. Sandia National Laboratories, Carlsbad, NM.

Kuhlman, K.L. 2010. Analysis Report for Preparation of 2009 Culebra Potentiometric Surface Contour 

Map. ERMS 553645. Sandia National Laboratories, Carlsbad, NM.

Kuhlman, K.L. 2011. Analysis Report for Preparation of 2010 Culebra Potentiometric Surface Contour 

Map. ERMS 555318. Sandia National Laboratories, Carlsbad, NM.
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Kuhlman, K.L. 2012. Analysis Report for Preparation of 2011 Culebra Potentiometric Surface Contour 

Map. ERMS 557633. Sandia National Laboratories, Carlsbad, NM.

Kuhlman, K.L. 2012. Analysis Report for Preparation of 2000-2004 Culebra Potentiometric Surface 

Contour Maps. ERMS 557660. Sandia National Laboratories, Carlsbad, NM.

Kuhlman, K.L. 2012. Analysis Report for Preparation of 2005-2007 Culebra Potentiometric Surface 

Contour Maps. ERMS 557494. Sandia National Laboratories, Carlsbad, NM.

3. The averaged model calibration process involves estimating the coefficients in the equation used 

to extrapolate the initial condition and boundary conditions to all cells in the model domain, 

finding those coefficients which result in the smallest difference between observed and predicted 

heads. The details of the calibration process for the averaged model (i.e., the adjustment of 

boundary conditions), the details of the averaging process, and the quality of the fit resulting from 

the calibration are given in the Sandia reports cited in point number 2.

4. The process for creating contour maps was defined in Sandia specific procedure SP9-9 (reference 

provided below), and has been referenced and used for all the reports listed above. The process is 

the same as EPA observed during their inspections in 2012 and 2013.

Kuhlman, K.L. 2009. Activity/Project Specific Procedure SP 9-9 Preparation of Culebra Potentiometric 

Surface Contour Map Revision 0. ERMS 552306. Sandia National Laboratories, Carlsbad, NM.
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EPA Comment

1-23-1 Continuing Validity of Kds.

CRA-2014 Appendix PA, Table PA-1 states that the Culebra Matrix Partition Coefficients (Kds) are, 

“Carried over from CRA-2009 PABC.” Please provide the rationale for the assumption that the CRA 

2014 Kds can be same as those used in the CRA-2009 despite the changes in the organic ligand content 

and the 2012 inventory since the last PA. Additionally, provide a discussion of the potential impacts of 

the organic kitty litter added to the LANL waste on the Culebra Kds.

DOE Response

Table 6-6 of the 2012 Performance Assessment Inventory Report (Van Soest 2012) indicates there has 

been a net decrease of 7,946 kg in total organic ligand content in WIPP between the PAIR-2008 and 

PAIR-2012. When organic ligand content increases, the Kd value decreases and there is less retardation of 

dissolved species (Kuhlman 2010; eq. 11 on p. 40). Since total organic ligand content has decreased by 

almost 14% and the increases in individual ligands are less than 7.3% , the Kd values will have increased 

leading to additional retardation. For conservatism the Kd values were not changed.

Table 6-6. Comparing Organic Ligands (kg)1

PAIR-2008 PAIR-2012 Net Change

Acetate 9.70E+03 9.96E+03 2.56E+02

Acetic Acid 1.32E+04 1.41E+04 9.60E+02

Citrate 2.55E+03 2.55E+03 6.11E+00

Citric Acid 5.68E+03 5.23E+03 -4.54E+02

EDTA 3.54E+02 3.76E+02 2.18E+01

Oxalate 6.46E+02 6.50E+02 4.02E+00

Oxalic Acid 2.66E+04 1.78E+04 -8.74E+03

TOTAL 5.87E+04 5.07E+04 -7.95E+03

A discussion on the potential impacts of organic kitty litter on the Culebra Kd values will be addressed in 

the response to EPA Comment 1-C-1.

References:

Kuhlman, K. L. 2010. Analysis Report for the CRA-2009 PABC Culebra Flow and Transport 

Calculations, AP-144. Sandia National Laboratories, Carlsbad, NM.

Van Soest, G. D. 2012. Performance Assessment Inventory Report, INV-PA-12, Revision 0. Los 

Alamos National Laboratory Carlsbad Operations, Carlsbad, NM.

 

1

From Van Soest 2012
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EPA Comment

1-23-2 Continuing Validity of T-Fields.

CRA-2014 Appendix PA, Table PA-l states that the Culebra Transmissivity Fields are, "Carried over 

from CRA-2009 PABC." It appears that the last update to the geologic well data analysis was performed 

in 2007 (Powers 2007a and Powers 2007b). Specific questions and requests the Agency has related to the 

T-Fields are listed below.

1. Have changes in the Culebra well data during the past seven years changed the T –Field 

derivation in any way?

2. Has any additional hydraulic testing been performed that could be used for additional calibration 

of the T-Fields?

3. Has the saturated thickness of the Culebra remained constant since the original derivation of the 

T-Fields?

4. Provide justification that new well information and water level changes since the 2009 PABC do 

not need to be included in the T-Field derivation and that the 2009 T-Fields are still valid for use 

in the CRA-2014 PA.

5. Provide justification for the continued use of the CRA-2009 T-fields.

DOE Response

The ensemble of calibrated MODFLOW models (i.e., the T-Fields) incorporates the Rustler halite 

margins mapped in Powers (2007a and 2007b) and is still valid and consistent with the limited amount of 

new information obtained since the CRA-2009 PABC calibration effort. The T-Field calibration process 

is given in detail in Appendix TFIELD in CRA-2014. The specific responses to EPA comments follow:

1. No new wells have been drilled to the Culebra since the CRA-2009 PABC (only replacement 

wells) and therefore no new geologic well data have been collected; see discussion in CRA-2014 

Appendix HYDRO on page HYDRO-11. Culebra water level data collected since CRA-2009 is 

consistent with existing conceptual models and T-Fields (see pages HYDRO-14 through 

HYDRO-25).

2. Limited hydrologic testing has been conducted at replacement wells to confirm their proper 

construction and validate prior interpretations of hydraulic conductivity from previous wells on 

the same well pads. No analyses of these tests have been reported which deviate from the 

reported values used in the CRA-2009 PABC calibration process.

3. There has been no observed change in saturated thickness of the Culebra.

4. Discussion about the lack of new wells and new geologic information is in sections HYDRO-4.0 

and HYDRO-5.0. New water level data are reported and discussed in section HYDRO-7.0. As 

stated in the response to EPA Comment 1-15-1, the observed fluctuations in water level in the 

Culebra at WIPP are due to local transient perturbations are not significant enough to invalidate 

the calibration of the T-Fields, which are primarily based on drawdown observations during 

large-scale pumping tests.

5. The points made above justify the continued use of the CRA-2009 PABC T-Fields in the CRA2014 PA.
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EPA Comment

1-23-5 Waste Shear Strength. Please address the following:

1. Provide horsetail plots of the remaining fraction of uncorroded iron in the repository throughout 

the 10,000-year regulatory time frame from the CRA-2009 PABC from each of the three 

replicates and each scenario.

2. Provide horsetail plots of the remaining fraction of undegraded CPR in the repository throughout 

the 10,000-year regulatory time frame from the CRA-2009 PABC from each of the three 

replicates and each scenario.

3. Provide and justify the criteria used in advancing the surrogate waste samples during the shear 

strength tests when the eroded sample face was not smooth but irregular.

4. Identify and justify the consequences of using the proposed uniform distribution rather than the 

currently approved log-uniform distribution for TAUFAIL.

5. Provide the quality control procedures used during the shear strength tests and provide evidence 

that the tests were performed consistent with those procedures.

DOE Response

1. Horsetail plots of the remaining fraction of uncorroded iron from both the CRA-2009 PABC and 

CRA-2014 are provided in Enclosure 2 (from SNL 2015a).

• Green dashed lines are the individual vector results

• Red solid lines represent the mean of all 100 vectors with time

Reference:

Sandia National Laboratories (SNL). 2015a. First set of EPA Completeness Comments (Dec 17, 

2014), 1-23-5 Waste Shear Strength, Comment 1. ERMS 563282. Sandia National Laboratories, 

Carlsbad, NM.

2. Horsetail plots of the remaining fraction of undegraded CPR from both the CRA-2009 PABC and 

CRA-2014 are provided in Enclosure 2 (from SNL 2015b).

• Green dashed lines are the individual vector results

• Red solid lines represent the mean of all 100 vectors with time

Reference:

Sandia National Laboratories (SNL). 2015b. First set of EPA Completeness Comments (Dec 17, 

2014), 1-23-5 Waste Shear Strength, Comment 2. ERMS 563286. Sandia National Laboratories, 

Carlsbad, NM.

3. The testing procedure followed during operation of the vertical flume is identical to that used for 

erosion tests conducted using SEDflume. SEDflume is considered the industry standard sediment 

erosion flume (Lick 2009). Descriptions of the vertical flume and its operation can be found in 

Roberts and Herrick (2009), Herrick et al. (2012), and Herrick and Schuhen (2014). The criteria 

used for advancing the waste sample are summarized below.

At the start of each test, the sample container and the surrogate waste sample it contains are 

attached to the test section on the side of the channel. The sample container is squared to the 

channel so the movement of the sample is perpendicular to the flow of the erosion fluid. Initially, 

the surface of the sample is positioned flush with the channel wall. Erosion fluid is forced upward 

through the enclosed channel across the surface of the sample. The flow-produced shear stress 
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may cause the sample to erode. Erosion is determined visually. If erosion is not observed, the 

shear stress is incrementally increased by increasing the fluid flow rate. This process is continued 

until erosion is observed or the limit of the pump is reached. If the surrogate waste sample erodes, 

additional material is advanced laterally by the operator until the surface of the specimen is once 

again flush with the channel wall. Lateral advancement of the sample is repeated in increments as 

small as 0.25 mm to as large as needed to keep pace with erosion of the sample. The erosion rate 

is determined from the lateral movement of the sample in the sample container over time.

During erosion, the surface of the sample may at times become irregular. There may be areas of 

the test specimen that experience more or less erosion based on the material makeup and cohesive 

nature of the sample. Practitioners often refer to this as “pitting” and “mounding,” respectively. In 

addition, the irregularity of the erosion may not be consistent over the entire face of the sample so 

there may be areas of pitting and other areas of mounding. Because of this tendency, erosion of 

the leading edge is used to drive the operator’s decision to move the sample forward. The leading 

edge is the lower, approximately segment-shaped area of the specimen that is first contacted by 

the erosion fluid flowing up the channel (Figure 1). A typical arc length for this segment-shaped 

area is 2-5 cm. The reason the leading edge is used to decide to advance the sample is that the 

shear stresses acting on this area are developed over the long, smooth-walled section of the flume 

channel below the sample. This allows the operator to analytically estimate the shear stresses in 

advance of the tests, with final corrections to the shear stress calculations being made post-test 

once all the testing conditions have been measured. 

Figure 1. Schematic diagram of the leading edge of a surrogate waste sample (dark, 

approximately segment-shaped area) in the channel of the vertical flume.

Eroding Fluid Flow (upward)

Leading Edge

Channel Wal

C

l

hannel Wall

Sample
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An additional advantage of establishing the leading edge as the specific point used by the 

operator to evaluate whether to advance the specimen is that it establishes a consistent 

methodology. The methodology is applicable from specimen to specimen and along the entire 

length of a specific sample. By using a specific point – the leading edge – the operator not only 

evaluates erosion under undisturbed flow conditions, but also removes possible random 

interpretations of the shear stress or determination of erosion if different positions over the face of 

sample were used. The idea is that every sample throughout its length is consistently advanced 

based on the observations at this specific point of erosion. This consistent method of evaluation 

minimizes randomness that would otherwise complicate the interpretation of the results.

References:

Herrick, C.G. and Schuhen, M. 2014. Flume Testing of Surrogate Waste Materials Leading to a 

Recommendation for the Lower Limit for TAUFAIL. Paper 14172. WM2014 Conference, March 

2-6, 2014, Phoenix, Arizona.*

Herrick, C.G., Schuhen, M.D., Chapin, D.M., and D.C. Kicker, D.C. 2012. Determining the 

Hydrodynamic Shear Strength of Surrogate Degraded TRU Waste Materials as an Estimate for 

the Lower Limit of the Performance Assessment Parameter TAUFAIL, Rev. 0. ERMS 558479. 

Sandia National Laboratories, Carlsbad, NM.

Lick, W. 2009. Sediment and Contaminant Transport in Surface Waters. CRC Press, Boca Raton, 

FL.*

Roberts, J. and Herrick, C.G. 2009. Waste Erodibility with Vertical and Horizontal Erosion 

Flumes. Test Plan TP 09-01, Rev. 0. Sandia National Laboratories, Carlsbad, NM.

4. The use of a uniform distribution for TAUFAIL is justified for three reasons: (1) the uniform 

distribution is the Maximum Entropy distribution when all that is known about a parameter is its 

range. (2) The original subject-matter expert and the developer of the cavings model used a 

uniform distribution on a range of parameter values that spanned more than two orders of 

magnitude. (3) Based on experimental results from erosion testing of surrogate materials 

representing the most degraded state conceivable for WIPP waste at the end of the 10,000-year 

regulatory period, the DOE proposed increasing the lower bound of sampled shear strengths from 

0.05 to 2.22 Pa. The proposed range of values for TAUFAIL would be 2.22 – 77 Pa, which spans 

less than two orders of magnitude.

Tierney (1990) described and rationalized a defensible and systematic five-step procedure to be 

used to construct probability distributions for uncertain variables in performance assessment 

models of WIPP. This approach has been used in all compliance certification performance 

assessments. According to Tierney (1990):

“This procedure provides a consistent approach to the construction of probability 

distributions in cases where empirical data concerning a variable are sparse or absent 

and minimizes the amount of spurious information that is often introduced into a 

distribution by assumptions of nonspecialists. The procedure gives first priority to the 

professional judgment of subject-matter experts and emphasizes the use of site-specific 

empirical data for the construction of the probability distributions when such data are 

available. In the absence of sufficient empirical data, the procedure employs the 

Maximum Entropy Formalism and the subject-matter experts’ subjective estimates of the 

 

*

Copyrighted reference not provided in Enclosure 2
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parameters of the distribution to construct a distribution that can be used in a 

performance simulation.”

Tierney’s procedure is guided by two axioms: (1) a probability distribution describing a variable 

should, to the maximum extent practicable, be constructed from empirical data and information 

that are site specific, and (2) if numerical data (i.e., sample values for the quantity) are few or 

nonexistent, probability distributions for that quantity should be constructed using only those 

subjective but quantified judgments about the quantity that are made by experts in the subject 

matter pertaining to the quantity.

Tierney’s (1990) Axiom 1 recognizes that empirical, system-specific data – combined with 

proven theoretical concepts and informed, professional interpretation of the data – provide the 

best possible connection between the real system and the mathematical models that are being 

used to study the system’s behavior. When data are lacking, professional judgment is all that 

remains. Tierney’s (1990) Axiom 2 ensures that subjective information provided by persons with 

specialized knowledge of the variable will be included in determining the form of the probability 

distribution. Adhering to this second axiom practically dictates the use of a particular method 

called the Maximum Entropy Formalism (MEF), also referred to as the Principle of Maximum 

Entropy. Tierney’s (1990) methodology forms the basis of the process by which later parameter 

probability distributions were assigned in WIPP Performance Assessments (e.g., Rechard and 

Tierney 2005). The emphasis of Tierney’s (1990) procedure is on the MEF and the subject-matter 

experts’ subjective judgment.

According to Tribus (1969) and Harr (1990), as cited in Rechard and Tierney (2005), Shannon in 

1948 quantitatively defined the connection between probability and uncertainty using a “measure 

of information” or an “informational entropy” functional, 

( ) U n

. In 1957, Jaynes described a 

MEF that states that of all the distributions that can be chosen based on the information at hand, 

the one distribution that maximizes 

( ) U n

is the least prejudiced or least biased selection. Hence, 

using the MEF, a unique probability distribution function (PDF) can be determined that 

maximizes 

( )

n

U 

while satisfying prescribed, quantitative constraints. The constraints may be 

empirical constraints (i.e., constraints based on sample values of the variable) or subjective 

constraints (based on professional judgment). Examples of quantitative constraints are the range, 

the mean, the variance, or the percentiles of the distribution. In the case of TAUFAIL, the 

quantitative constraint is the range [A, B] (that is, only the lower and upper limits are known). The 

MEF states that the probability distribution which best represents the current state of knowledge 

is the one with largest entropy. Again quoting Tierney (1990):

“The entropy functional has also been interpreted as a measure of the amount of 

uncertainty inherent in a PDF or as a measure of the amount of information that would 

be required to specify completely the value of a random variable X (for the idea that 

entropy is “missing” information, see Baierlein, 1971). Thus, finding an extremum of the 

entropy functional subject to prescribed constraints can be construed as finding the PDF, 

within the set of all PDFs that incorporate the information inherent in the constraints, 

which maximizes the amount of remaining information that must be supplied in order to 

completely specify the value of the uncertain variable X. Use of the MEF can minimize 

the amount of spurious information that often enters into the construction of a PDF from 

sparse data or limited quantitative information.”

If nothing is known about a distribution except the quantitative constraints, then the distribution 

with the largest entropy should be chosen as the default. Tierney (1990) and Cullen and Frey 

(1999) note that when only the range [A, B] of an uncertain variable X is all that is known, the 
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probability distribution function that maximizes 

( ) U n

is the uniform distribution. The only 

constraint they indicate is that 

B A   

, i.e., the range of the variable X is bounded (Iman and 

Shortencarier 1984, Tierney 1990, Sandia WIPP Project 1992).

In a log-uniform distribution the log-transformed random variable is assumed to be uniformly 

distributed (e.g., Cullen and Frey 1999, GoldSim User’s Guide). By its nature, a log-uniform 

distribution assigns considerable negative skewedness to the distribution of possible values of the 

uncertain variable. Thus its use implies that there exists more information about the distribution 

than just its minimum and maximum. In agreement with Tierney (1990), Cullen and Frey (1999) 

suggest that the subject-matter expert is the best person to assign a distribution type and, for a 

case similar to the parameter TAUFAIL, the default is the uniform distribution, “...the uniform is 

useful for representing subjective judgments about uncertainty when an expert is only 

willing/able to estimate an upper and lower bound for a quantity because it is the maximum 

entropy distribution in this case.” (Cullen and Frey 1999, Section 4.3.1.6).

Berglund (1992) developed the original cavings model and performed the first analyses. For the 

Compliance Certification Application (CCA) (DOE 1996), the DOE assumed a uniform 

distribution of the waste shear strength with a range of 0.05 to 10 Pa. This range was based on 

Berglund’s (1996a, b) review of soil erosion tests. The lower limit of the range was based on 

erosion tests of a bay mud. It was the lowest value of a clayey material found in the literature at 

that time. The upper limit was arbitrarily chosen, based on the same literature review, as a value 

less than the highest threshold value reported. It is important to note that the original subjectmatter expert, Jerry Berglund, assigned a uniform distribution for the parameter TAUFAIL at that 

time. This distribution was selected in spite of the fact that the range of possible values extended 

for more than two orders of magnitude:

10 log 2.30

0.05

    

 

Probability distribution functions are used in risk assessment work. WIPP Performance 

Assessment is a type of risk analysis of the predicted containment performance of a geologic 

disposal system. In the field of risk assessment a move was made to also include a log-uniform 

distribution, as well as a uniform distribution, to describe the variability of an uncertain variable 

X when only its range [A, B] is known. The challenge became when to apply which PDF, and the 

number of orders of magnitude that the range spans is often considered. In the table below is a 

review of guidelines recommended by various risk assessment practitioners for when to assign a 

log-uniform distribution. This list is not exhaustive, but is believed to be representative.

Reference Criterion for Using a Log-Uniform Distribution

Tierney (1996), 

CCA (DOE 1996), 

CRA-2004/CRA-2004 PABC 

(DOE 2004)

“B/A >> 10; that is, the range (A,B) spans many orders of 

magnitude”

CRA-2009/CRA-2009 PABC 

(Fox 2008)

“B/A > 100; that is, the range (A,B) spans more than two orders 

of magnitude”

GoldSim User’s Guide “...parameters that may range in value over two or more log-
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cycles...”

Cullen and Frey (1999) “... loguniform, useful in cases where inputs cover large ranges 

of values...”

Mishra (2002) “The log-uniform distribution ... inputs cover a large range (e.g. 

multiple orders of magnitude)...”

Ecolego “...log-uniform distribution is useful in cases where inputs cover 

large ranges of values...”

Peake (1997) “Since the range of possible shear stresses extends over two or 

three orders of magnitude, a log uniform distribution should be 

used...

Most practitioners do not define “many orders of magnitude,” “multiple orders of magnitude,” or 

“large ranges of values.” The consensus for those that do define a range is that it is at least two 

orders of magnitude, that is, B/A > 100. In no case has a statistical argument such as the MEF 

been made to justify the use of a log-uniform distribution.

The range of TAUFAIL values used by DOE in CRA-2014 is [2.22 Pa, 77.0 Pa]. This range 

spans less than two orders of magnitude:

77 log 1.54

2.22

    

 

According to the guidelines about when to use a uniform versus a log-uniform distribution just 

covered, a uniform distribution should be assigned to this range of values for TAUFAIL.

In summary, assigning a uniform distribution to the range of values of TAUFAIL is supported for 

several reasons: (1) the uniform distribution is the maximum entropy distribution when all that is 

known about a variable is its range, (2) the original subject-matter expert and model developer 

recommended a uniform distribution, and the suggested range at that time spanned more than two 

orders of magnitude, and (3) the currently-proposed range is less than two orders of magnitude.

References:

Berglund, J.W. 1992. Mechanisms Governing the Direct Removal of Wastes from the Waste 

Isolation Pilot Plant Repository Caused by Exploratory Drilling. SAND92-7295. Sandia National 

Laboratories, Albuquerque, NM.

Berglund, J.W. 1996a. Effective shear resistance to erosion TAUFAIL. Memorandum to B.M. 

Butcher, October 28, 1996. ERMS 247189. Sandia National Laboratories, Carlsbad, NM.

Berglund, J.W. 1996b. Parameters required for the CUTTINGS_S code for use in WIPP 

Performance Assessment. ERMS 236766. Sandia National Laboratories, Carlsbad, NM.

Cullen, A.C. and H.C. Frey. 1999. Probabilistic Techniques in Exposure Assessment: A 

Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Springer Science 

& Business Media.*

 

*

Copyrighted reference not provided in Enclosure 2
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Ecolego – Log-Uniform Distribution. http://ecolego.facilia.se/ecolego/show/LogUniform%20Distribution.

Fox, B. 2008. Parameter Summary Report for CRA-2009. ERMS 549747. Sandia National 

Laboratories, Carlsbad, NM.

GoldSim User’s Guide – Appendix B: Probabilistic Simulation Details. 

http://www.minem.gob.pe/minem/archivos/file/dgaam/publicaciones/curso_cierreminas/02_T%C

3%A9cnico/03_Calidad%20de%20Aguas/TecCalAg-L4_GoldSim_App%20A-B.pdf.

Harr, M.E. 1987. Reliability-Based Design in Civil Engineering. McGraw Hill, New York.*

Iman, R.L. and M.J. Shortencarier. 1984. A FORTRAN 77 Program and User’s Guide for the 

Generation of Latin Hypercube and Random Samples for Use with Computer Models. SAND83-

2365. Sandia National Laboratories, Albuquerque, NM.

Peake, T. 1997. Shear resistance to erosion (TAUFAIL). Facsimile to G. Basabilvazo, dated April 

28, 1997. ERMS 545768. Sandia National Laboratories, Carlsbad, NM.

Rechard, R.C. and M.S. Tierney. 2005. Assignment of probability distributions for parameters in 

the 1996 performance assessment for the Waste Isolation Pilot Plant. Part 1: description of 

process. Reliability Engineering and System Safety 88: 1–32.

*

Sandia WIPP Project. 1992. Preliminary Performance Assessment for the Waste Isolation Pilot 

Plant, December 1992 – Volume 3: Model Parameters. SAND92-0700/3. Sandia National 

Laboratories, Albuquerque, NM.

Tierney, M.S. 1990. Constructing Probability Distributions of Uncertain Variables in Models of 

the Performance of the Waste Isolation Pilot Plant: The 1990 Performance Simulations. 

SAND90-2510. Sandia National Laboratories, Albuquerque, NM.

Tierney, M.S. 1996. Distributions. ERMS 235268. Sandia National Laboratories, Carlsbad, NM.

Tribus, M. 1969. Rational Descriptions, Decisions, and Designs. Pergamon Press, New York.*

U.S. Department of Energy (DOE). 1996. Title 40 CFR Part 191 Compliance Certification 

Application for the Waste Isolation Pilot Plant. DOE/CAO-1996-2184. October 1996. Carlsbad 

Field Office, Carlsbad, NM.

U.S. Department of Energy (DOE). 2004. Title 40 CFR Part 191 Subparts B and C Compliance 

Recertification Application for the Waste Isolation Pilot Plant. DOE/WIPP 2004-3231. Carlsbad 

Field Office, Carlsbad, NM.

5. EPA has stated in its response to DOE clarification questions (EPA 2015) that it would review the 

associated shear strength test procedures and QA records in Carlsbad, so the procedures and 

records are not included in this response.

Reference:

U.S. Environmental Protection Agency (EPA). 2015. Email correspondence dated 1/9/15 from 

Kathy Economy, EPA, to Russ Patterson, CBFO, Subject: CRA-2014 Completeness Questions. 

ERMS 563280. Sandia National Laboratories, Carlsbad, NM.

 

*

Copyrighted reference not provided in Enclosure 2
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EPA Comment

1-23-7 Volume of Repository Operations and Experimental Areas. Please address the following:

1. Explain how DOE arrived at a volume of 148,011 m3for the underground.

2. In the diagram of repository ventilation during recovery (see attached, labeled Phase 2B 

Underground Map, dated March/17/2014) a large portion of the north experimental area is 

denoted as ‘backfilled’. Please provide information of the material properties of these backfilled 

areas and how they are modeled during the 10,000 year regulatory period in the CRA-2014 PA.

DOE Response

1. The volume referred to in the question is the value as of the data cut-off date of December 31, 

2012. The value of 148,011 m3for the WIPP experimental area was obtained after adding 

additional volume to that region to account for the Salt Disposal Investigation (SDI) excavation in 

the repository north end. From Section 6.1 of Camphouse et al. (2011):

The BRAGFLO software calculates the flow of brine and gas in the vicinity of the WIPP 

repository over the 10,000-year regulatory compliance period. The volume of the 

experimental region implemented in the PABC-2009 discretization is

2( (30.61m) x (361.65m) x (1.32m + 1.32m + 1.32m) ) = 87,675 m3.

The volume resulting from additional excavation in the experimental region for SDI is 

60,335 m3. As a result, the target volume of the experimental region implemented in the 

BRAGFLO computational grid is 87,675 m3 + 60,335 m3 = 148,010 m3. Elements 

corresponding to the experimental area are lengthened in the z-direction to account for 

the additional SDI excavation. Two element lengths of 30.61 meters in the z-direction 

were used in the PABC-2009 representation of the WIPP experimental region. These two 

lengths are increased to 51.67 meters and 51.68 meters, respectively, to represent the 

increased volume due to the SDI excavation. The resulting volume of the experimental 

region in the BRAGFLO numerical grid is

(51.67m + 51.68) x (361.65m) x (1.32m + 1.32m + 1.32m) = 148,011 m3,

one cubic meter greater than the target value.

Reference:

Camphouse, R.C., Kicker, D.C., Kirchner, T.B., Long, J.J., and Pasch, J.J. 2011. Impact 

Assessment of SDI Excavation on Long-Term WIPP Performance. ERMS 555824. Sandia 

National Laboratories, Carlsbad, NM. 

2. The areas that have been permanently backfilled include Room G, the N-1100 and N-1400 access 

drifts to SPDV Rooms 1 -3, SPDV Room 4, and alcoves L-1, L-2, L-3, L-4, J and I-2. Bulkheads 

installed on the north and south ends of SPDV Rooms 1-3 were left in place so those rooms were 

not backfilled. The salt backfill came from the mining of Panel 2 and its access drifts. The 

backfill was placed using load haul dumps (LHDs) and a ram car. The LHDs pushed the backfill 

to as close to the back as possible (about 1.5 to 2 feet). These areas were backfilled so that they 

would no longer need to be maintained and to remove them from ventilation.

The only other location that has been backfilled was a portion of W-30 between S-3310 and S3650. This backfill was placed as a construction demonstration for the run-of-mine salt panel 
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closure system. The length of the backfill is approximately 100 feet. The backfill was placed 

using an LHD and a scaler modified to push the backfill tight to the back. This backfill is 

temporary and will be used to provide salt for the interim closure of Panel 6, and could be used to 

provide salt for the installation of the WIPP Panel Closure System.

Parameters used to represent the repository experimental area in the CRA-2014 PA are 

unchanged from those used in the CRA-2009 PABC, as well as the CCA and CRA-2004. To 

remain consistent with previous, peer-reviewed conceptual models for the repository and for the 

Salado, performance assessment assigns constant porosity and permeability to the operations and 

experimental areas. Repository performance has been shown to be insensitive to dynamic closure 

in the experimental and operations areas (Vaughn 1996). This was confirmed by more recent 

calculations for the SDI impact assessment which did not show a change in the CCDFs 

(Camphouse et al. 2011). Constant values for porosity and permeability in the experimental and 

operations areas are based on the concept that it is conservative in terms of releases if these areas 

have high permeability and low porosity. High permeability ensures that these regions do not 

impede brine and gas flow that might contribute to a borehole release and low porosity minimizes 

the storage capacity for gas in these regions, thus resulting in higher pressures in the waste-filled 

regions of the repository. The experimental region in the CRA-2014 PA is assigned a 

permeability of 10-11 m

2

and a porosity of 0.18 for the duration of the 10,000-year regulatory 

period, the same values established and used in the CCA, as well as the CRA-2004 and CRA2009.

References:

Camphouse, R.C., Kicker, D.C., Kirchner, T.B., Long, J.J., and Pasch, J.J. 2011. Impact 

Assessment of SDI Excavation on Long-Term WIPP Performance. ERMS 555824. Sandia 

National Laboratories, Carlsbad, NM.

Vaughn, P. 1996. Phase I FEPS Composite FEP Records Screening Analysis. ERMS 418211. 

Sandia National Laboratories, Carlsbad, NM.
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EPA Comment

1-23-12 WIPP-Specific Organic Complexation Data.

Appendix SOTERM Section 3.8.2 provides a description and four graphs (Figure SOTERM-21) that 

relate to WIPP-specific experiments designed to evaluate the effects of organic chelating agents on +III 

and +IV actinide solubility in WIPP brines.

1. Please provide supporting documentation for these data, including a summary of the 

experimental approach, materials and analytical methods used to produce the data.

2. Please provide any available characterization data for the solid phases present in these 

experiments.

DOE Response

1. The data shown in Figure SOTERM-21 were from experiments performed according to Los 

Alamos Test Plan LCO-ACP-07 (LANL 2007) entitled “Effect of Acetate, Citrate, EDTA, 

Oxalate and Borate Ions on Neodymium Solubility in WIPP Brine.” The experiments specific to 

the effects of organic complexation are described in Task 1: The Effect of Acetate, Citrate, 

EDTA and Oxalate Complexation on Neodymium (III) Solubility in WIPP Brine.

The general scientific approach used (paraphrased from the Test Plan) was:

a) Start with prior-reacted GWB and ERDA-6 brine samples from the neodymium (Nd) 

solubility experiments that had been equilibrated with neodymium for over 300 days at a 

pCH+ of 8-9.

b) Re-measure neodymium concentration and pCH+ to establish a baseline prior to the addition 

of the organic ligand.

c) Perform triplicate experiments with two different concentrations of each ligand. The 

maximum concentration used was approximately 10 times the maximum organic ligand 

concentration estimated for the Performance Assessment Baseline Calculations that 

supported the CRA-2004 (Brush and Xiong 2003).

d) Monitor experiments (Nd analysis by inductively-coupled plasma-mass spectrometry and 

pCH+ measurement) until a steady state neodymium concentration was observed (multiple 

months).

The details of the experimental results are found in the Actinide Chemistry and Repository 

Science Program Scientific notebook designated ACP-07/1 and summarized in the data package 

(LANL 2013) which is provided as an attachment to this response.

2. In this experiment series, there are no data on solid phases specific to the organics present in the 

brine system.

References

Brush L.H. and Xiong Y. 2003. Calculation of Organic Ligand Concentrations for the WIPP 

Compliance Recertification Application and for Evaluating Assumptions of Homogeneity in WIPP 

PA. Unpublished analysis report. ERMS 531488. Sandia National Laboratories, Carlsbad, NM.

Los Alamos National Laboratory (LANL). 2007. Effect of Acetate, Citrate, EDTA, Oxalate and 

Borate Ions on Neodymium Solubility in WIPP Brine. Test Plan, LCO-ACP-07, Revision 0. ACP0702-03-03-01. Sandia National Laboratories, Carlsbad, NM.
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Los Alamos National Laboratories (LANL). 2013. Data Package: Figure SOTERM-21-Summary 

Data Package for Figure SOTERM-21 in 2014 SOTERM Document. ACP-1401-04-05-01. Sandia 

National Laboratories, Carlsbad, NM.
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EPA Comment

1-24-1 Shielded Container Lead Inventory

1. Please provide information as to how lead shielding on RH shielded containers is included in the 

performance assessment.

DOE Response

For the Performance Assessment Inventory Report (PAIR) - 2012 (12/31/2011 cutoff date), only one 

lead-shielded container is included in the inventory. It is included within the WIPP-bound waste stream 

AW-5410N from the Material and Fuels Complex (MFC) site. The waste stream is categorized as 

remote-handled, and thus its contribution to the performance assessment in terms of volume, radionuclide, 

and waste/packaging material inventory is under the “RH” category. The mass of lead contributed by 

this single container is 431 kg out of the total 8,280 kg reported in the PAIR-2012 in the lead packaging 

materials for RH, Table 5-5 Waste and Packaging Materials. The lead-shielded containers that are 

currently emplaced in the Waste Isolation Pilot Plant from Argonne National Laboratory were not 

packaged until 2013 and therefore are not included in the PAIR-2012. 
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EPA Comment

1-24-2 Inventory Report Text Unclear. Please address the following:

1. Provide information as to how the “projected-to-stored volume ratio” is derived for both RH and 

CH waste. Please provide an example of this derivation.

2. Provide information of the RH waste volume that has been and will be placed in the leaded 

containers on the waste panels floors.

3. Provide information that specifies how potential waste inventory listed in the inventory report 

estimate is used in the CRA-2014 performance assessment.

DOE Response

1. The projected-to-stored volume ratio is calculated on a waste-stream by waste-stream basis in the 

Comprehensive Inventory Database (CID) for both RH and CH waste by dividing the total final 

form projected volume by the total final form stored volume as reported for each waste stream 

containing stored waste. This ratio is then multiplied by the waste stream’s reported stored 

radionuclide and waste material parameter values in order to derive the projected values. The 

anticipated values to be reported in the Annual Transuranic Waste Inventory Report (ATWIR) 

can then be calculated as the sum of the stored and projected components. An example of this 

can be shown by using the data contained in the CID as reported by Oak Ridge National 

Laboratory for waste stream OR-REDC-CH-HET (numbers are rounded in example). This waste 

stream reported 1,561 stored and 120 projected 55-gal Drum Dir Ld w/o Liner containers. This 

calculates to a stored volume of (1,561)(0.208 m3) or 325 m3and a projected volume of 

(120)(0.208 m3) or 25 m3; therefore, the projected-to-stored volume ratio is 25 m3/325 m3, or 

roughly 0.077. This ratio is then used to derive the projected values from the stored radionuclide 

and waste material parameter values reported for this waste stream. For instance, 2,365 kg is 

reported for the waste material parameter, cellulosics stored mass; therefore, the projected amount 

of cellulosics is (2,365 kg)(0.077) or 182 kg. This is then used to calculate the anticipated (stored 

+ projected) amount of cellulosics by summing 2,365 kg and 182 kg, which equals 2,547 kg. 

2. Post Performance Assessment Inventory Report-2012 (PAIR-2012) (cutoff date of 12/31/2011), 

nine RH lead-shielded containers have been emplaced within the Waste Isolation Pilot Plant 

(WIPP) repository. All nine were emplaced in September 2013 under the waste stream 

AERHDM from Argonne National Laboratory, and are located in Panel 6 Room 2. These nine 

lead-shielded containers account for a total of 0.99 m3of RH emplacement volume as reported in 

the WIPP’s Waste Data System. As of 12/31/2013, no other lead-shielded containers had been 

identified by the sites to be shipped to WIPP. In the PAIR-2012, MFC originally reported 1 leadshielded container would be used for waste stream AW-5410N, but has since decided to use a RH 

canister for this waste.

3. Waste streams with the designation of “potential” are not included in the Performance 

Assessment calculations, as directed by the U.S. Department of Energy Carlsbad Field Office in 

a memorandum (Patterson 2010), which is provided in Appendix D of the ATWIR-2012 and also 

identified in the PAIR-2012, Section 2.
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EPA Comment

1-24-3 Emplaced Inventory Chemical Constituents

1. In the ATWIR 2012, Section 2.3, it is stated that, “Chemical constituents are not reported in the 

emplaced inventory”. In the PAIR 2012 report, Section 4.3, it is indicated that “two additional 

analysis” were performed for chemical and other important constituents. It is unclear how the 

chemical constituents of emplaced inventory, identified in the PAIR report, are derived and 

calculated for the CRA-2014 PA, provide clarification.

DOE Response

Chemical constituents of emplaced inventory, identified in the Performance Assessment Inventory 

Report-2012, are derived and calculated from data contained in the Comprehensive Inventory Database 

which is based on estimates provided by the sites. The Standard Analysis Report (SAR) uses scaled (full 

repository) mass for oxyanions, complexing agents, and cement on a waste stream basis, while accounting 

for waste streams that have been emplaced since the preceding SAR. Since the WIPP’s Waste Data 

System does not track these components, it is important for the SAR to account for the masses of the 

components that currently remain at the sites and those that have been emplaced at WIPP. For an indepth description of this process, see the Standard Analysis Report, Chemical and Cement Components 

2011 Inventory Estimates, INV-SAR-28, which was provided to EPA in DOE’s First Completeness 

Response (Franco 2015).

Reference:

Franco, J. 2015. Letter to Mr. Jonathan D. Edwards (Subject: Response to EPA Letter Dated December 

17, 2014 Regarding the 2014 Compliance Recertification Application). 28 January, 2015. U.S. 

Department of Energy, Carlsbad Field Office, Carlsbad, NM.