The multivariate statistical methods of Principal Component Factor Analysis (PCFA) and k-Means
Cluster Analysis (KMCA) are sequentially used on groundwater chemistry form the Amargosa Desert region to estimate possible hydrochemical processes and facies along with probable groundwater flow paths and evolution in the region. PCFA is applied to major ion data and results are rotated, thus reducing the number of variables describing the system and allowing for better interpretation of the system’s variation in terms of hydrochemical processes. KMCA is applied to factor scores derived from the rotated PCFA to allow the grouping of sampling locations with similar water chemistries into hydrochemical facies. The resulting rotated factor loadings and scores are presented as biplots, demonstrating relationships between and among variables and sampling-locations. Derived factor scores and hydrochemical facies overlaid on a digital elevation map of the region provide a visual picture of hydrochemical evolutions, localized recharge centers and potential groundwater interactions with geologic and topographic features in the region.
A Principal Component Factor Analysis (PCFA) of the data was performed Using
™ 7 [11] . The first four factors were extracted to reduce the number of variables from seven to four and to find relationships among the original variables [12] . Original variables are expressed as a linear combination of the underlying common factors,
[13,14] , a high loading indicates a high degree of correlation. A normalized varimax rotation of factors is performed and rotated factor loadings for the major ion chemistry are generated along with factor scores for each sampling location. Rotated factor loadings are presented on
Table 1.
A biplot is simultaneous bivariate (factor loadings and factor scores) scatter plots that provides a visual picture of the relationships between and among different ions and sampling locations, in addition, it shows objective sampling-location groupings, and thus provides more insight than Piper diagrams
[15,16] . Each factor, with a certain chemical composition, implies a dominating hydrochemical process, and a clustered group implies a hydrochemical facies with similar genesis, evolution and/or composition [17] indicated by the underlying factors.
The first four rotated PCFA scores were subjected to a
-
Means Cluster Analysis (KMCA) to group observations with a similar genesis into separate hydrochemical facies.
The KMCA is nonhierarchical supervised partitioning method that tries to group data that is similar and separate data that is not based on Euclidean distances of variables
[13] . From previous analysis it was determined to group data into seven (k) clusters.
Although the seven hydrochemical facies are derived independently of lithological data, they are found to be in good agreement with their respective lithology and thus these groups are validated as hydrochemical facies [17] .
Contour plots of each of the resulting factors were overlaid on a digital elevation model (DEM) of the region in order to reveal ground water signatures and potential flowpaths. A contour plot of a factor would be equivalent to a contour plot of a hydrochemical process indicating its direction of evolution and delineating areas influenced by that process [18] .
Table 1
Rotated Factor Loadings for Major Ions
Parameter Factor 1 Factor 2 Factor 3 Factor 4
Mg 2+
Ca 2+
SO
4
2-
Cl -
Na +
Alk
K +
0.922
0.866
0.541
0.202
-0.004
0.514
0.299
0.055
0.322
0.667
0.924
0.727
0.232
0.265
0.289
0.036
0.260
0.129
0.659
0.769
0.199
0.135
0.315
0.318
0.195
0.148
0.246
0.893
Variation
Percentage
2.287
32.6%
Alk = Alkalinity in (CaCO
3
)
2.059
29.4%
1.235
17.6%
1.137
16.2%
Static ground water elevation contours in meters above sea level based on 1,088 wells
[1] , (342 wells within the area are shown) overlaid on a satellite image of the Amargosa Desert region. Contour intervals are reduced from 100 to 20 meters (m) between the 800 and 660 m levels.
The Amargosa Desert is in the southern portion of Nye County in south central Nevada, within the Great
Basin, and is part of the Death Valley ground water basin. The ephemeral Amargosa River begins in the
Oasis Valley, turns southeast to cross through the length of the Amargosa Desert, then continues south until it bends west, and finally enters Death Valley from the southeast.
Yucca Mountain , north of the Amargosa
Desert, is a group of north-trending block-faulted ridges of volcanic rocks (ash-flow and ash-fall tuffs) [2] .
Yucca Mountain has been chosen as the site of a high-level nuclear waste repository expected to hold approximately 77,000 metric tons of radioactive waste.
Fortymile Wash is an ephemeral drainage that originates in the uplands between Timber Mountain and Shoshone Mountain, it flows southward along the east of Yucca Mountain, and fans out in the northern part of the Amargosa Desert before reaching the Amargosa
River.
Highway 95 fault is named so as it lies below and approximately along the highway. Furthermore, a deep carbonate aquifer that is locally up to 4600 meters (m) thick [3] and composed mainly of Paleozoic limestones and dolomites [4] , underlies most of the tuff volcanic rocks and alluvial fill [5] . The present climate in the Amargosa Desert region is considered arid to semiarid, with average annual precipitation ranging from less than 130 millimeters (mm) at lower elevations to more than 280 mm at higher elevations [6] . In contrast, the climate at the end of the Tioga glacial maximum of Wisconsin glaciation in North America, at approximately
11,500 years before present (yr BP), was wetter and colder than the present [7,8] . Contemporary potentiometric water levels of the region are presented on Figure 1 .
Ground water chemistry major ion data were obtained from the Nye County Nuclear Waste Repository Project
Office (NWRPO) website as of March 2003 [9] and a Los Alamos National Laboratory report [10] . Data were compiled into a single database, covering the Amargosa Desert region. Sampling locations are mostly wells, some of which have multiple screened depths, while the remaining are fresh springs; springs containing high levels of evaporites were excluded from analysis.
The biplots presented here have two scales: one for factor scores of sampling locations (i.e., bottom and left), and the other for factor loadings of ions (i.e., top and right). Each ion vector indicates the direction of increasing ion content in the samples, and their projection onto the factor axis is their correlation to that factor. This biplot is a diagram customized to the dominant hydrochemical processes (i.e. the factors), showing the hydrochemical facies and demonstrating the chemical composition of the processes and facies of the system.
Three common trends are observed from contours of the rotated factors: First, a large trough of more dilute waters follows along the path of Fortymile Wash and turns to the southeast, where the wash joins the Amargosa River. Second, the presence of “noise” in the contours, apparent along the Highway 95
Fault. Third, a gradual increase in Ca 2+ , Mg 2+ and Cl along the pathway of the Amargosa River coming out of the Oasis Valley. The geochemical data presented herein suggests that groundwater beneath
Fortymile Wash follows the surface of the wash until it appears to merge and mix with groundwater beneath the Amargosa River. The signature from Fortymile Wash is believed to represent the relic of focused infiltration of surface runoff along the course of the wash during past pluvial periods, when the climate was colder and wetter than the present and the amount of runoff in the wash was significantly greater. The results from the multivariate statistic analyses provide objective grouping of major ions, into hydrochemical processes; and sampling-locations, into hydrochemical facies. Specifically, evidence is presented of past-focused recharge around Fortymile Wash climate-induced changes surrounding the wash, and some potential interaction of ground water with Highway 95 fault. The work herein demonstrates how the use multivariate methods of statistical analyses of water chemistry provide further understanding of ground water flow and evolution in the Amargosa Desert region.
This work was funded by the Nye County, Nevada, Nuclear Waste Repository Project
Office through cooperative research grant DE-FC28-02RW12163 from the U.S.
Department of Energy, Office of Civilian Radioactive Waste Management
a) Rotated Factor 1 is dominated by Mg 2+ and Ca 2+ ions, which are typically associated with the dissolution of carbonates, and is interpreted as an indication of the degree of influence of, or mixing with, the carbonate aquifer. High values are found at Crater Flat, Amargosa Flat and Ash Meadows, which are down gradient of outcrops of the underlying carbonate aquifer (Bare Mountain Specter Range, Stripped Hills, and
Skeleton Hills).
b) Rotated Factor 2 is primarily composed of Cl , Na + , and SO
4
2; high levels of these ions are generally associated with elevated amounts of the water evaporation that caused their concentration, and is perceived as a measure of the degree of evolution through evaporation. Low values form a trough surrounding
Fortymile Wash.
c) Rotated Factor 3 is dominated by alkalinity and Na + , and is most likely related to the weathering of silicate minerals with the generation of alkalinity and the concomitant release of Na + . These values present a clear separation between groundwater west and east of Yucca Mountain.
d) Rotated Factor 4 is mostly composed of K + and suggests that silicate weathering is significant in this system. These values appear to create a faint pathway originating in the Oasis Valley and following the
Amargosa River.
1.
Buqo, T., Personal communication by email.
buqo@aol.com
, with attachment “Master
Regional Water Levels 2004.xls.
” August 27, 2004.
2.
Kelkar S., P. Tseng, T. Miller, R. Pawar, A. Meijer, B. Robinson, G. Zyvoloski, E.
Kwicklis, A.A. Eddebbarh, and B. Arnold. 2003. Site/subsite scale saturated-zone flowtransport models for Yucca Mountain.
IHLRWM 2003 , Las Vegas, NV: 114-121.
3.
Stetzenbach K.J., V.F. Hodge, C. Guoa, I.M. Farnhama, K.H. Johannessonc. 2001.
Geochemical and statistical evidence of deep carbonate ground water within overlying volcanic rock aquifers/aquitards of southern Nevada.
Journal of Hydrology 243: 254-
271.
4.
Flint A.L., L.E. Flint, E.M. Kwicklis, G.S. Bodvarsson, and J.T. Fabryka-Martin. 2001.
Hydrology of Yucca Mountain, Nevada.
Review of Geophysics, 39, no. 4: 447-470.
5.
Kwicklis E.M., A. Meijer, and J.T. Fabryka-Martin. 2003. Geochemical inverse models of ground water mixing and chemical evolution in the Yucca Mountain area.
IHLRWM
2003 , Las Vegas, NV: 328-339.
6.
Flint A.L., L.E. Flint, G.S. Bodvarsson, E.M. Kwicklis, and J.T. Fabryka-Martin. 2001.
Evolution of the conceptual model of unsaturated zone hydrology at Yucca Mountain,
Nevada.
Journal of Hydrology , 247: 1-30.
7.
Benson, L., M. Kashgarian, R. Rye, S. Lund, F. Paillet, J. Smoot, C. Kester, S.
Mensing, D. Meko, and S.
Lindström. 2002. Holocene multidecadal and multicentennial droughts affecting northern California and Nevada . Quaternary Science Reviews 21 :
659-682.
8.
Harvey, A.M., P.E. Wigand, and S.G. Wells. 1999. Response of alluvial fan systems to the late Pleistocene to Holocene climatic transitions: contrasts between the margins of pluvial Lakes Lahontan and Mojave, Nevada and California, USA.
Catena 36: 255-281.
9.
NWRPO (Nuclear Waste Repository Project Office). 2003. Geochemistry data files. Nye
County, Nevada website. Accessed March 2003.
http://www.nyecouty.com
)
10. LANL (Los Alamos National Laboratory), 2003. Regional ground water hydrochemical data in the Yucca Mountain are used as direct input to ANL-NBS-00021, Revision 01.
LA0309RR831233.001
, May, 2003.
11. StatSoft, Inc. 1984-2006.
SATISTICA Computer program manual for Windows . Tulsa,
OK. Kernel release 5.5. www.statsoft.com
12. Mellinger M., 1987. Multivariate data analysis: its methods.
Chemometrics and
Intelligent Laboratory Systems 2: 29-36. Rencher, A.C., Methods of Multivariate
Analysis , Second edition, Wiley-Interscience, USA, 2002.
13. Rencher, A.C., Methods of Multivariate Analysis , Second edition, Wiley-Interscience,
USA, 2002.
14. Grande J.A., A.
González, R. Beltrán, and D. Sánchez-Rodas. 1996. Application of factor analysis in the aquifer system of Ayamonte-Hueva (Spain).
Ground Water 34, no.
1: 155-161.
15.
Güler, C., G. Thyne, J.E. McCray, and A.K. Turner. 2002. Evaluation of graphical and multivariate statistical methods for classification of water chemistry data.
Hydrogeology
Journal 10: 455 –474.
16. Dalton, M.G., and S.B. Upchurch. 1978. Interpretation of hydrochemical facies by factor analysis.
Ground Water 16, no. 4: 228 –233.
17. Thyne, G., C.
Güler, and E. Poeter. 2004. Sequential analysis of hydrochemical data for watershed characterization.
Ground Water 42, no. 5: 711 –723.
18. Lawrence F.W., and S.B. Upchurch. 1982. Identification of recharge areas using geochemical factor analysis.
Ground Water 20, no. 6: 680-687.