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CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
MODELING KARST DEVELOPMENT IN AN ALPINE LOCATION: MINERAL KING, SEQUOIA NATIONAL PARK, CALIFORNIA
A thesis submitted in partial fulfillment of the requirements
For the degree of Master of Arts in Geography
By
Patrick Joseph Kahn
August 2008
The thesis of Patrick Joseph Kahn is approved: Darrick Danta, Ph. D Date Shawna Dark, Ph. D Date Julie Laity, Ph. D, Chair Date
California State University, Northridge
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DEDICATION
This volume is dedicated in loving memory to my parents, Melodee (1953-2007) and Kevin (1952-2008) Kahn - who made many sacrifices and were the catalysts in my
success. They will be forever missed, but never forgotten.
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ACKNOWLEDGEMENTS
This project would not have been possible without the help of so many
individuals. Therefore, I would like to take this time to acknowledge those people who
devoted their time in helping me throughout this process.
I would like to thank Joel Despain, Ben Tobin, and Sequoia/Kings Canyon
National Parks for providing me with the opportunity to conduct this exciting project.
Additionally, I’d like to thank Joel and Ben for their invaluable assistance and
companionship in the field, and their willingness to help through email.
I had the pleasure of many field assistants, none more valuable than Chris Lima,
who made many early morning journeys with me to Mineral King and sustained copious
amounts of physical abuse. I also want to thank Rob O’Keefe, his wife Kolette, and
daughter Emily for their companionship on a long field weekend. Additionally, I want to
acknowledge Ted Riedell, Tyler Eaton, Crystal Cave Guide, and Heather Veerkamp-
Tobin, National Park Service employee, for their invaluable assistance in the field.
Many people have contributed their knowledge and assistance to various aspects
in the creation of this volume. I would like to thank Greg Stock, National Park Service
Geologist, Cathy Busby, UCSB, Dave Deis, Danielle Bram, Kris Tacsik, Stephanie
Rozek, CSUN. Additionally, I would like to thank Dr. Amalie Orme and Dr. Steve
Graves for their invaluable input. For her unconditional support and patience, I would
like to thank my girlfriend, Courtney. Also, much thanks to my friends and family for
their continued emotional support in what has been a truly difficult time; I could not have
finished without them.
Lastly, and most importantly, I would like to end my praises with the three
individuals on my committee who dedicated their time to guiding me through this
process. I would like to thank Dr. Darrick Danta, Dr. Shawna Dark, and Dr. Julie Laity,
for their fruitful knowledge and advice. I especially want to thank my chair, Dr. Julie
Laity, who dedicated a large amount of time reading, editing, and guiding me until the
end.
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TABLE OF CONTENTS Signature Page ii Dedication iii Acknowledgements iv List of Tables vii List of Figures viii List of Equations x Abstract xi 1. INTRODUCTION 1
1.1 Statement of Purpose 4 1.2 Research Questions and Hypothesis 4
2. PHYSICAL ENVIRONMENT OF MINERAL KING 6 2.1 Introduction 6 2.2 Climate and Vegetation 8 2.3 Geology 11 2.4 Glaciation 18 2.5 Karst in the Sierra Nevada 20
3. GEOSPATIAL TECHNOLOGIES AND GEOMORPHOLOGY 23 3.1 Introduction 23 3.2 GIS and Karst Geomorphology 24
4. DEVELOPMENT OF KARST SYSTEMS 27 4.1 Introduction 27 4.2 Structural and Stratigraphic Controls 27 4.3 Models of Formation 28
5. METHODS 31 5.1 Introduction 31 5.2 Methodology: fieldwork, digitization, and GIS analysis 31 5.3 Fieldwork 32 5.4 Database Management 36 5.5 Developing the Predictive Model 38
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5.6 Analysis 41
6. RESULTS 48 6.1 Predictive Model 48 6.1 Distribution, Density, Correlative Analysis, and Regression 54
6.3 Franklin Creek Drainage 55 6.4 Monarch Creek Drainage 64 6.5 Timber Gap 71 6.6 White Chief 78 7. DISCUSSION 88 7.1 Franklin Drainage 88 7.2 Monarch Drainage 92 7.3 Timber Gap 94 7.4 White Chief 96 8. CONCLUSIONS 102 WORKS CITED 109 APPENDIX A 115 APPENDIX B 118
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LIST OF TABLES Table 2.1 Glacial Chronology of the Sierra Nevada 19 Table 6.1 Regression Model Summary and Coefficients for Franklin Drainage 63 Table 6.2 Regression Model Summary and Coefficients for Monarch Creek Drainage 71 Table 6.2 (cont.) 72 Table 6.3 Regression Model Summary and Coefficients for Timber Gap 77 Table 6.3 (cont.) 78 Table 6.4 Regression Model Summary and Coefficients for White Chief Valley 87
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LIST OF FIGURES
Figure 2.1 Location map of Mineral King in Sequoia National Park 7 Figure 2.2 Shaded relief map of Mineral King Valley 9 Figure 2.3 Geologic Map of Mineral King Valley 12 Figure 2.4 Marble Map of Mineral King Valley 15 Figure 2.5 Sketch of vertical distribution of karst in Sequoia National Park 21 Figure 5.1 Photo of Mineral King Valley 33 Figure 5.2 Photo of a karst spring 34 Figure 5.3 Photo of a collapse sink 35 Figure 5.4 Photo of a cave entrance 35 Figure 5.5 Photo of a sinking stream 36 Figure 5.6 Flowchart illustrating steps of predictive model creation 41 Figure 5.7 Flowchart for steps of proximity analysis 44 Figure 5.8 Flowchart illustrating steps of slope analysis creation 45 Figure 6.1 Map of predicted karst development in Franklin drainage 49 Figure 6.2 Map of predicted karst development in Monarch Creek Drainage 50 Figure 6.3 Map of predicted karst development in Monarch Creek Drainage 51 Figure 6.4 Map of predicted karst development in Monarch Creek Drainage 52 Figure 6.5 Regressions comparing predictive model distribution to Expected distribution 54 Figure 6.5 (cont.) 55 Figure 6.6 Karst feature distribution map of Franklin drainage 57 Figure 6.7 Karst feature density map of Franklin drainage 58 Figure 6.8 Histograms showing karst feature distances to formative variables in Franklin drainage 61 Figure 6.9 Karst density vs. Stream distance in Franklin drainage 63 Figure 6.10 Karst feature distribution map of Monarch Creek
drainage 66 Figure 6.11 Karst feature density map of Monarch Creek drainage 67 Figure 6.12 Histograms showing karst feature distances to formative variables in Monarch Creek drainage 68 Figure 6.12 (cont.) 69 Figure 6.13 Karst density vs. Stream distance in Franklin drainage 70 Figure 6.14 Karst feature distribution map of Timber
Gap 73 Figure 6.15 Karst feature density map of Timber Gap 74 Figure 6.16 Histograms showing karst feature distances to formative variables in Timber Gap 76 Figure 6.17 Karst density vs. Stream distance in Timber Gap 77
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Figure 6.18a Karst feature distribution map of White Chief 81 Figure 6.18b Karst feature distribution map of White
Chief Valley 82 Figure 6.19 Karst feature density map of White Chief Valley 83 Figure 6.20 Histograms showing karst feature distances to formative variables in White Chief Valley 85 Figure 6.21 Karst density vs. Stream distance in White Chief Valley 87 Figure 7.1 Photo of marble outcrop in Franklin drainage 92 Figure 7.2 Photo of Monarch drainage 94 Figure 7.3 Photo of Timber Gap marble 96 Figure 7.4 Photo of White Chief Cave complex 101 Figure B.1 Franklin Karst 119 Figure B.2 Monarch Karst 120 Figure B.3 Timber Gap Karst 121 Figure B.4 White Chief Karst (North) 122 Figure B.5 White Chief Karst (South) 123
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LIST OF EQUATIONS
Equation 5.1 Predictive model 39 Equation 6.1 Franklin Regression 62 Equation 6.2 Monarch Regression 69 Equation 6.3 Timber Gap Regression 76 Equation 6.4 White Chief Regression 86
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ABSTRACT
MODELING KARST DEVELOPMENT IN AN ALPINE LOCATION: MINERAL
KING, SEQUOIA NATIONAL PARK, CALIFORNIA
By
Patrick Kahn
Master of Arts in Geography
The Sierra Nevada Mountains have been extensively studied, but the principle
focus has been its geologic and glaciologic history. Over the past 50 years, the discovery
of many new caves has positioned Sequoia and Kings Canyon National Parks firmly in
the karst community. Karst topographies supply much of the Earth’s population with
water, and caves represent one of the last unexplored frontiers. There lacks substantial
knowledge of karst genesis in alpine settings. This study had two purposes. The first was
to map and create an inventory of karst features in Mineral King Valley. The second used
the inventory to analyze the morphogenesis of karst features as it relates to lithology,
geology, and hydrography. Mineral King Valley is a glaciated, sub-alpine to alpine valley
at the southern extent of Sequoia National Park, and its karstified marble is part of a
submarine metamorphic complex overlaying the Sierra Nevada batholith. Fieldwork took
place between July and October of 2007, and consisted of mapping the marble units and
inventorying surficial karst features, such as caves, springs, sinks, and stream sinks.
Moreover, a predictive model was developed using ArcGIS 9.2 to project probable
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locations of karst features. Analysis employed ArcGIS and SPSS to perform distribution,
density, histogram, and regression analyses to model karst formation. The results indicate
preferential formation along carbonate/non-carbonate boundaries, streams, and north
aspects. Additionally, karst feature distributions tend to occur approximately parallel to
strike. Development was also apparent along faults and folds to a lesser extent. Fracturing
due to glaciation and glacial meltwater appear to have played the most significant role in
karst genesis, possibly previous to Tioga glaciation. Further alpine karst investigation is
necessary to validate these results and monitor the effects of climate change on karst
formation.
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1. INTRODUCTION
Karst landscapes underlie large areas of ice-free continental surfaces, occupying
10-20% of the Earth’s surface (Palmer, 1991). Approximately 20-25% of the global
population relies on water supplied by karst groundwater, and there is an increasing need
for sustainable management of these important resources. Understanding the distribution
and processes of karst drainage systems and the processes that form them is important for
their preservation. Major surface karst features owe their origin to internal drainage and
processes relating to subterranean cave development. In order to analyze such systems it
is essential to determine their areal and vertical extent, boundary conditions, and input
and output sites (Ford and Williams, 2007).
Cave systems are hydrologically dynamic. As a consequence, cave ecosystems
and local-scale karst processes are easily disrupted by humans. In the United States,
caves within National Parks including Mammoth Cave, Kentucky, Carlsbad Caverns,
New Mexico, and Crystal Cave in Sequoia National Park, California receive millions of
visitors each year, creating impact potential. Additionally, contaminated surface water
can enter caves via sinkholes (Forth et al., 1999) with springs serving as outlets and
conduits serving as intermediaries. Thus, comprehending local karst processes is
imperative for proper hydrologic management, and identifying surficial features is the
first step in achieving this goal.
The Sierra Nevada mountain range in California has been extensively studied, but
the principle focus has been its geologic and glaciologic history. Over the past 50 years,
the discovery of many new caves has positioned Sequoia and Kings Canyon National
Parks firmly in the karst community. The two parks currently have approximately 250
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known caves. These unique caves include Crystal Cave, a 3-mile long tourist cave;
Lilburn Cave, California’s longest cave; Hurricane Crawl Cave, a highly decorated
cavern; and the recently discovered Ursa Minor Cave, which contains a multitude of rare
and endemic insects. Caves also provide habitat for many wildlife species and represent
some of the unexplored areas on our planet. In order to conserve and understand these
unique speleological resources, the National Park Service (NPS) is interested in
developing a spatial database of cave and karst locations.
Recent advances in technology have yielded a useful spatial analysis program in
Geographic Information Systems (GIS). GIS contains a suite of tools applicable to
explaining density and distribution patterns and defining spatial relationships. Federal
government agencies such as the National Park Service and Bureau of Land Management
have adopted GIS to manage and protect cave resources since the passing of the Cave
Resources Protection Act of 1988 (Szukalski, 2002). GIS has been used to identify and
buffer sensitive mineralogical, paleontological, and biological resources in Hurricane
Crawl Cave for their protection from proposed travel routes (Despain and Fryer, 2002). It
also offers a reliable method of storing and mapping spatial data and related non-spatial
attributes. The application of GIS to karst topography facilitates efforts to help explain
spatial relationships between features, contributes to explanations of karst genesis, and
helps determine hydrogeological relationships in karst environments.
The full extent of karst locations and resources in the Sierra Nevada is unknown.
Veni (2002) published a map of general karst locations in the United States, but the map
provides little detail, contains error in scale and projection, and includes questionable
boundaries. In 2004, Tobin and Weary revised and digitized a karst map of the United
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States created by Davies (1984) and added detailed lithologic descriptions. Surveys have
also been performed for various caves in Sequoia National Park, including a published
passage map of Crystal Cave (Despain and Stock, 2005), but these do not address the
broader question of cave distribution and hydrogeological relationships.
Caves are known to be present in Mineral King Valley of Sequoia National Park,
but the scientific literature largely examines the region’s history as a mining settlement,
its development potential, and general physical geography (Peters, 1971). The
distribution of caves and their hydrogeological relationships are poorly understood. Black
(1994) analyzed the hydrogeochemistry of lakes and streams in the area. Despain (2006)
studied the hydrochemistry of alpine karst, specifically at Spring Creek (Tufa Falls), and
Tinsley and Schultz (1999) used dye-tracing to establish hydrogeologic connections of
karst in White Chief Valley. Geologic mapping and historical interpretations have been
performed by Knopf and Thelen (1905), Christensen (1959), and Busby-Spera and
Saleeby (1987). A preliminary inventory of features in the area created by the Cave
Research Foundation identified 62 features with limited geographic accuracy (Tobin,
2007, personal communication).
At present, no published inventory exists for alpine karst features in the Mineral
King area, and its karst geomorphology and hydrogeology are poorly understood.
However, methods from previous studies outside the Sierra Nevada addressing mapping,
distribution and density analyses can be applied to the alpine karst of the Mineral King.
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1.1 Statement of Purpose
The purpose of this study was to map the karst features of Mineral King Valley
and create an inventory database with the intention of modeling karst development in
alpine settings. A model was created in ArcGIS 9.2 to predict the locations of surficial
karst features. Fieldwork was then done to record the feature locations. After dividing the
study area into four topographically distinct sections, analysis used ArcGIS 9.2 and SPSS
to investigate karst development patterns.
1.2 Research Questions and Hypothesis
In order to mode the distribution and extent of karst features in Mineral King
Valley, this study sought to answer the following questions:
1. What is the extent of surficial karst features in Mineral King Valley?
2. What distribution patterns are exhibited by alpine karst in Mineral King Valley?
3. Does the occurrence of karst features coincide with structural and lithologic
lineaments?
4. Does hydrography have an effect on the formation of karst?
5. Does slope influence karst formation?
Three hypotheses were used to help predict the locations of karst features in the study
area: 1) karst features would be found in marble beds in close proximity to their contacts
with impermeable rock units, including slate, calc-silicates and batholithic granites; 2)
karst features would exhibit a linear relationship with geologic structures within the
valley, forming within close proximity and parallel to faults and folds and occurring
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parallel to sub-parallel to the strike of the valley; 3) karst features would be found
proximal to surface streams.
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2. PHYSICAL ENVIRONMENT OF MINERAL KING
2.1 Introduction
Mineral King is a glaciated alpine valley situated in Sequoia National Park
(Figure 2.1). The rugged 6,500 ha area is located 40 km east of Three Rivers, California
and 378 km north of Los Angeles. The valley exhibits high relief and ranges in elevation
from 2,377 m on the valley floor to 3,790 m at Florence Peak. Multiple episodes of
glaciation are recorded in Mineral King, which exhibits a distinctive U-shaped valley and
the glacial cirques that border the valley are documentation of multiple episodes of
glaciation. The valley is flanked to the east by the peaks of the Sierra Nevada Great
Western Divide; the tallest being Florence Peak (3,790 m). The northern boundary is
marked by Timber Gap and the southern boundary by the low ridge and saddle forming
Farewell Gap, which also marks the southern border of Sequoia National Park.
Mineral King forms the headwaters for the East Fork Kaweah River. Numerous
tributaries feed the river, arising from springs and the high alpine lakes formed from
glacial and snow meltwater (Figure 2.2). Monarch, Crystal, and Franklin Creeks cascade
down the east side of the valley, and White Chief and Eagle Creek flow down the western
side. Two karst springs, Soda Spring and Tufa Spring, feed the East Fork from the east
and west side, respectively. Other unnamed springs below Farewell Gap create the
southernmost tributary of the river. The East Fork flows from the south of the valley to
the north. Its gradient increases significantly as it exits Mineral King and flows west
towards Three Rivers.
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Figure 2.1 Location map of Mineral King in southern Sequoia National Park. The two parks lie in central California east of the San Joaquin Valley.
Mineral King originated as a mining settlement, but failed to produce significant
ores, and eventually came under government control in the 1940’s. Prior to 1978, the
valley was subject to plans for a Disney ski resort (Peters, 1971; Jackson, 1988) before
the land was transferred from the U.S. Department of Agriculture to the National Park
Service. Currently, Mineral King serves as a popular summer destination for outdoor
enthusiasts. Campgrounds and other establishments including private cabins and a
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mountain resort are located in the northern end of the valley (Felzer, 1975). Numerous
steep hiking trails also begin in the northern part of the valley, leading to all parts of the
area.
A number of karst features have already been identified in Mineral King Valley.
Eagle Sinkhole, Soda Spring and Tufa Spring are found on the USGS 7.5’ quadrangle.
Two other karstic springs are found along the Farewell Gap trail and one other north of
Eagle Sinkhole. Previously identified and surveyed caves include White Chief and
Beulah caves, each over a mile long, and Cirque, Bat Slab, Panorama, Jordan, Never
Seen, and Seldom Seen caves.
A number of abandoned gold and silver mines are located throughout the valley.
Empire Mine is a mine located in Empire Cave east of Timber Gap. It housed the largest
19th century mining operation in Mineral King (Jackson, 1988). Other mines within
marble are Lady Franklin Mine, located in the Franklin Creek drainage and White Chief
Mine, located in White Chief Valley. There are numerous other mines not contained
within marble units, such as Chihuahua Mine.
2.2 Climate and Vegetation
Much of Southern California, including Mineral King, experiences a
Mediterranean type climate with wet, cool winters and dry, hot summers. The climate of
the Mineral King, however, is modified by its altitude. Winters are characterized by
below freezing temperatures and abundant snowfall. Summers are marked my mild
temperatures, and orographic convective storms are more common than in lowland areas.
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Figure 2.2 Shaded relief map of Mineral King Valley with stream, lakes, and hiking trails shown. Timber Gap and Farewell Gap mark the northern and southern boundaries, respectively. Major peaks are highlighted along with their elevation.
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Mineral King Valley receives moisture from both snow and rain. The valley’s
only weather station recorded 457 cm of snowfall for the 2006-2007 winter (Mineral
King Weather Webcam & Weather Station, 2007), providing ideal conditions for
dissolving atmospheric carbon dioxide, creating acidic waters. Typical summer daytime
high temperatures are mild. The valley’s weather station recorded daily maxima between
15° and 26° C. Precipitation is rare during the summer, but can fall in large amounts from
orographic convective cells. Spring and fall in Mineral King bring cooler temperatures.
Daily maxima for 2006-2007 ranged between 4° and 18° C. Precipitation includes
frontal rainfall and snowfall. The fall season also receives precipitation from convective
cells, stemming from residual monsoonal conditions. No historical records exist for
precipitation totals for Mineral King. Most weather stations throughout the Sierra Nevada
are located at lower elevations and near settlements. Comparison of yearly precipitation
totals with other weather stations indicates an increase in snowfall and total precipitation
with elevation. Higher precipitation totals lead to increased runoff and increased activity
within the karst system.
The vegetation is characteristic of sub-alpine forests. Red fir (Abies magnifica),
Lodgepole pine (Pinus contorta), and Foxtail pine (Pinus balfouriana) are common
throughout the area. Scrub-shrub fills the bowls commonly inundated by avalanches in
the winter. Alpine meadows are located adjacent to and below the alpine lakes
surrounding the valley. Many large mammals can also be found within Mineral King.
The most notable are the black bear (Ursus americanus), yellow-bellied marmot
(Marmota flaviventris), and mule deer (Odocoileus hemionus) (Parsons et al., 1981).
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2.3 Geology
The Sierra Nevada is a NNW-SSE trending mountain range approximately 650 km
long. It was uplifted as a result of the former subduction of the Farallon Plate beneath the
North American plate. Granitic plutons formed in response to subduction in the early
Mesozoic and were tectonically uplifted in the Paleocene, creating the Sierra Nevada
batholith. Meanwhile, existing Devonian to Jurassic sedimentary rocks in the ancestral
marine setting underwent contact metamorphism at the greenschist grade, were tilted
vertically, and superimposed on the granitic plutons (Busby-Spera and Saleeby, 1987;
Hill, 2006).
Uplift continues along the eastern margin of the range, creating a steep eastern face
and a more moderate western incline. During uplift, many of the metamorphic rocks were
eroded and deposited as sediments in California’s Central Valley. Remnants of the
metamorphosed marine rock, called “roof pendants”, are preserved as lenses throughout
the Sierra Nevada (Hill, 2006).
2.3.1 Lithology
Mineral King Valley is developed within a metamorphic “roof pendant”. The rocks of
the valley are an east-facing and vertically dipping homocline. The NNW striking “roof
pendant” is bound by a series of Cretaceous plutons and extends approximately 3 km
north of Timber Gap and 10 km south of Vandever Mountain. The Empire quartz diorite
pluton bounds the north end of the valley, and the Eagle Lake quartz monzodiorite and
Sawtooth Peak granite plutons are to the west and east, respectively.
The Mineral King “roof pendant” is part of a series of volcano-plutonic sections
which developed in response to subduction throughout much of the Mesozoic. This
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Figure 2.3 The geologic map of (Busby-Spera and Saleeby, 1987) is presently the most accurate depiction of the stratigraphy, structure, and contact boundaries of rocks in Mineral King. The inset at the bottom right shows the locations of Mineral King relative to other roof pendants and bodies of metamorphic rock in the Sierra Nevada.
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period was marked by the formation of stratocones and andesitic and rhyolitic lava flows,
after a long period of marine deposition (Busby-Spera and Saleeby, 1987).
The rocks in the metamorphic pendant can be classified into meta-volcanic and
meta-sedimentary. Three contact relationships between units are indicated on the map
(Figure 2.3). They include conformable facies changes, transitional or gradational
contacts, and abrupt, non-faulted contacts. Meta-volcanic rocks include metamorphosed
andesite and rhyolite. Five separate rhyolite units occur throughout the valley and are
inferred to be separate eruptive events. The four andesite units in the valley represent
multiple, smaller eruptive events, indicated by rapid vertical and lateral variations in rock
type.
The meta-sedimentary series is divided into those units that exhibit wave-
generated structures (shallow marine) and those that do not (deep marine), and account
for over half the rocks in the valley. Shallow marine rocks include marbles and
calcareous tuffs. The two shallow marine units lie stratigraphically above and below a
rhyolite and andesite unit, respectively. They are both cut to the south by the Sawtooth
Peak granite, with the easternmost member terminated by the Empire fault. These units
are interpreted as storm deposits on a high energy coast (Busby-Spera and Saleeby,
1987). Marbles, the permeable, karst-forming rocks, occur both as units with discernable,
conformable contacts, and as interbeds within the non-permeable shallow marine tuffs
and other units.
The deep marine rocks include calc-silicates, slate, turbidites, and breccia. Calc-
silicates indicate times of volcanic quiescence when stratocones were eroded down and
deposits settled onto a lime mud substrate. Six calc-silicate units occur through the
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pendant. When no sediment reached the substrate, limestones formed, revealed by the
marble units found adjacent and within the calc-silicates. The slate unit lies in the axis of
the valley, dissected by the Farewell Fault, and is interpreted to be a deep basin plain.
The breccia, or Sunridge Unit, lies on the Vandever stratocone and consists of sandstone
breccias, siltstones, turbidites, and tuffs. Two sandstone and shale turbidite beds,
interpreted as progradational submarine fans, line the east side of the valley, the northern
cut by the Bullfrog Lake faults and intruded by the western shallow marine unit (Busby-
Spera and Saleeby, 1987).
Other rocks include a meta-intrusive and travertine deposit. Meta-intrusive rocks
include a meta-gabbro sill, lying stratigraphically above the slate unit. A permeable
travertine bed is found just north of the Mineral King road, west of the Sawtooth
trailhead, containing two caves. Busby-Spera and Saleeby (1987) and Christensen (1959)
provide additional detailed geologic analysis.
2.3.2 Marble
Marble is the karst-forming rock of Mineral King Valley (Figure 2.4). It occurs as
lenses parallel to the strike of the valley. There are several informally named marble
units, each differing in extent, exposure, and thickness, but largely similar in
composition. For the purposes of this study, groupings of marble units were named for
the drainage near which they occurred. These include the Franklin marble, the Monarch
marble, the Timber Gap marble, and the White Chief marble.
The Franklin marble is highly deformed and folded and occurs as interbeds with
calc-silicates and shallow marine marl. Larger outcrops are found in the
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Figure 2.4 A map of the distribution of marble rock in each of the four study sub-sections. The map, produced for this thesis, is based on air photo interpretation and field mapping.
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Franklin drainage just west of Franklin Lake and in a drainage between two ridges north
of Franklin Lake. Two separate thinner lenses are found farther downstream of Franklin
Creek. Numerous small interbeds are also scattered west of Franklin Lake. The marble
exhibits substantial jointing and fracturing, making it a high potential area for karst
development.
The marbles in Monarch drainage begin as small interbeds at the lower elevations
near the valley floor. The thickness of each unit varies, though each outcrop is generally
thin and lenticular. The largest outcrop is found at 3050 m ASL, just west of Lower
Monarch Lake and is dissected by Monarch Creek. Another lens climbs the steep
northwest face of Empire Mountain north of Monarch Creek, beginning at an elevation of
2680 m ASL, extending to 2930 m ASL. These two units show significant jointing,
making them candidates for karst development. The last marble outcrop is a short, thin
lens just off the Monarch trail east of the drainage, extending approximately 100 m. This
unit is mostly covered by glacial detritus, though outcrops along its contact with the marl.
The Timber Gap marble occurs as a single exposed lens extending from just south
of Timber Gap to 2 km north before terminating just south of the confluence of Timber
Gap Creek and Cliff Creek. The expanse follows two small cirques and crosses two low
glacial ridges. This thinly bedded unit does not exceed 30 m in thickness and is highly
jointed and fractured, providing ideal conditions for karstification. The southern extent
exhibits evidence of glacial quarrying, whereas the northern extent becomes covered by
sediment and organic matter from surrounding trees.
The White Chief hanging valley marble extends from the White Chief bowl north
to its terminus at Tufa Falls. Approximately 150 m of the marble is mantled by Tioga
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stage glacial debris between White Chief Valley and Eagle Creek (Despain, 2006). The
marble appears again for approximately 150 m near Eagle Sinkhole before becoming
covered in glacial till and does not reappear until 1.5 km north, where it terminates at
Tufa Falls. The unit varies in thickness and is only exposed in the White Chief bowl.
Intense jointing and fracturing resulting from frost action and glacial plucking is evident
on the exposures, providing sufficient porosity necessary for karst development.
2.3.3 Lineations
Foliation, cleavage and jointing all occur parallel to sub-parallel to strike in meta-
sedimentary units, whereas the meta-volcanic units lack internal structure. For simplicity,
all forms of fracturing will be termed lineations in this study. Most marble units are too
deformed to show internal structure. Minor evidence of foliation and jointing parallel to
strike is exhibited in some exposures, whereas intense mechanical weathering and
glaciation have formed other fractures within the units. Mineralogical lineations are
detected in bedded rocks along cleavage planes, but sub-parallel to foliation. Where
bedded, marbles are foliated parallel to bedding planes (Christensen, 1959). Lineations,
which are termed secondary porosity due to their post-depositional formation, provide the
necessary conditions for the development of karst. The orientation and connectivity of
these fractures determines the character of the karst system.
2.3.4 Faults
Seven major faults have been mapped in Mineral King Valley. The two oldest faults
strike NW-SE and bound a caldera-collapse structure to the north and south, and are
interpreted as having formed during deposition of the surrounding rock units. The other
faults are probably post-depositional and pre-batholithic (Busby-Spera and Saleeby,
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1987). One other fault off-setting the White Chief marble with respect to the Eagle Lake
marble also strikes NW-SE. All other faults trend NNW-SSE, parallel to the valley’s axis.
The Empire Fault dissects marble in the Monarch drainage, possibly providing necessary
fracturing for karst development.
2.3.5 Folds
A number of mostly symmetrical and steeply dipping folds occur throughout the
valley, striking along its axis. Folds are found within meta-sedimentary units and are
more prevalent on the eastern slopes. No evidence has been found of folding in the meta-
volcanics. Four mappable synclines and one anticline were mapped by Busby-Spera and
Saleeby (1987) within the calc-silicate bed west of Monarch Lakes. Two large anticlines
and one syncline cut through the turbidite beds on either side of the Bullfrog faults north
of Bullfrog Lakes. A large fold complex was mapped southeast of Franklin Lakes in the
turbidite unit. Three anticlines were mapped in calc-silicates on the west side of the
valley. Folds can fracture the marble units and influence the flow of surface waters,
localizing karst development near its axis on a syncline, and away from its axis on an
anticline.
2.4 Glaciation
Mineral King contains abundant evidence of past glaciation of the Tioga stage
approximately 10 kya (Table 2.1), including cirques, horns, arêtes, hanging valleys, and
striations, grooves, and polish. However, moraines are absent or poorly preserved.
Numerous cirques and paternoster lakes are found near the crestline. Arêtes form high
ridges on the eastern and western side of the valley and Mineral Peak is a prominent horn
18
in the northeastern part of the area. Hanging valleys are formed in White Chief Valley
and the Crystal drainage. Terminal moraines are absent, with the exception of a 250 m
moraine on the west wall. Striations are found approximately 1 kilometer west of the
valley indicating the Tioga stage glacier was more than 300 m thick. Glacial deposits
mantle and obscure many of the marbles on the west valley wall (Knopf and Thelen,
1905).
Table 2.1 Wisconsin stage glaciations affecting the Sierra Nevada, withapproximate ages and key characteristics (Hayes, adapted from Yount and La Pointe, 1997).
The lateral valleys of Mineral King do not exhibit the typical U-shape expected from
tributary glaciers, and more closely approximate a V-shape. These valleys were glacially
19
carved, indicating glacial plucking and proving the efficiency of sub-glacial streams
(Knopf and Thelen, 1905). Evidence of plucking on the marbles is most prevalent just
north of Timber Gap and in the upper reaches of White Chief Valley. The step-like
sequence exhibited by the Monarch drainage is evidence of intense glacial quarrying.
2.5 Karst in the Sierra Nevada
In the southern Sierra Nevada, karst is largely found in marble, largely within the
boundaries of Sequoia and Kings Canyon National Parks. In the northern extent of the
range, caves are found principally in limestone (Tobin and Weary, 2004; Veni, 2002):
the most notable are the tourist caves Mercer, Moaning and California Caverns in
Calaveras County, east of Stockton, CA and northwest of Yosemite National Park; and
Black Chasm Caverns in El Dorado County, east of Sacramento, CA (Tobin, personal
communication, 2007).
In Sequoia and Kings Canyon National Parks, karst is found at a range of elevations.
Most of the known caves are found at lower altitudes, from approximately 1000 m ASL
to 1830 m ASL, including Crystal Cave. In Mineral King Valley (from 2430 m ASL and
above), the full extent of karst is unknown, providing the impetus for this research. There
have been a number of caves and sinks previously identified.
20
Figure 2.5 A sketch of the vertical distribution of karst within Sequoia and Kings Canyon National Parks. Much of the known karst is located between 1000 m and 1830 m. The 2430 m level represents the elevation of Mineral King, and the 3048 m label represents the lower limit of the alpine zone.
Some of the best exposures of alpine karst in the southern Sierra Nevada occur in the
Mineral King valley. Alpine is defined as elevations above the tree line, which is
approximated at 3048 m ASL in the Sierra Nevada. The only other alpine karst identified
within the Sierra is found near Mt. Pinchot on the eastern crest, west of Bishop, though
this area has not yielded substantial surficial formations (Despain, 2006). Relative to
karst at lower elevations, alpine karsts are formed in colder conditions (average winter
temperatures below 0°C), in regions that have been glaciated and lack organic soils. The
cold temperatures at high altitudes allow for increased dissolved carbon dioxide,
contributing to relatively high dissolution rates and rapid cave formation. One
characteristic of high altitude karst in Mineral King is the lack of substantial speleothems,
which only develop in caves where net deposition is greater than net dissolution: the high
CO2 concentrations of infiltrating water in Mineral King.
The Mineral King karst has not yet been successfully dated, and thus its relationship
with Pleistocene glaciations is unknown. The large caves of the White Chief karst could
21
not be Holocene in age, forming after ice disappeared from high altitudes, as the modern
dissolution rate of 148 mm/1000 yr (Despain, 2006) is too slow. However, Mineral King
caves also lack substantial speleothem formation, indicating relatively recent
development, possibly sub-glacially or inter-glacially (Stock, personal communication,
2007). These apparent contradictions remain to be resolved by future research in the
region.
By contrast, caves at lower altitudes have spectacular interior depositional features
and are very old in age. Speleothems at lower altitude caves in Sequoia and Kings
Canyon National Parks have been dated by 26Al/10Be methods, yielding approximate ages
of 2.7 Ma (Granger and Stock, 2004; Stock et al., 2004).
22
3. GEOSPATIAL TECHNOLOGIES AND GEOMORPHOLOGY
3.1 Introduction
The improvement of geospatial information technologies has led to many
advances in geomorphic science. Many geomorphological studies have incorporated GIS
to study dune migration (Andrews et al., 2002; Al-halal and Al-awadhi, 2006), glacial
mass balance (Khalsa, et al., 2004; Allen, 1998), stream channel migration (Reinfelds et
al., 2004; Townsend and Walsh, 1998; Dexter and Cluer, 1999), to model stream power
and drainage (Reinfelds et al., 2004), and for distribution analysis on desert dunes
(Wilkins and Ford, 2007).
3.1.1 Mapping Methods
Computer technology has revolutionized the geographic and geologic sciences
(Vitek et al., 1996). Three recent developments are the use of GIS and remote sensing,
the development of digital elevation models (DEM) at different resolutions for easy use
on personal computers, and automated frameworks for land evaluation (Bocco et al.,
2001). Historically, the cost of such data prohibited their use in all but those projects
with extensive budgets. Presently, much of this data is provided for free to the public.
Using geographic technologies with field techniques provides a method to analyze
relationships between space, pattern, and process. Remote sensing techniques can be used
for analyzing landscape composition and the geographic position of landscape patterns,
assessing spatial autocorrelation of landscape features, and evaluating the composition
and pattern of landscape variables (Walsh et al., 1998).
23
A GIS can facilitate analysis of spatial relationships; measure landscape features
(Vitek et al., 1996; Walsh et al., 1998) and most importantly, uses spatial metrics within
landscape studies. Spatial metrics quantify spatial patterns by defining their degree of
clustering or dispersal (Walsh, et al., 1998). A GIS also allows for the mapping of
landforms, process monitoring, and most importantly, modeling of the landscape and
processes (Vitek et al., 1996).
DEM’s are altitude matrices that form the base of useful data such as elevation,
slope and aspect. DEM’s are remotely sensed digital files containing terrain data for
ground positions at regularly spaced horizontal intervals. These layers aid in the
identification of geomorphic features. The quantification of landforms has eased the task
geomorphic modeling, and therefore helped combine the process and form approaches to
landscape study (Arrell, 2002).
3.2 GIS and Karst Geomorphology
Analysis performed using Geographic Information Systems is used to help
manage and protect karst resources. Caves and karst contain valuable resources relevant
to many fields of study including medicine, groundwater, petroleum, waste disposal, and
climate change. Due to the importance of cave resources, advancements using GIS have
been swift during recent years (Huggett, 2003; Kerski, 2004). Developments have
allowed for the management of complex datasets, provide a method to quantitatively
model karst processes, and visualize spatially and temporally complex data (Glennon and
Groves, 2002).
24
3.2.1 Mapping Methods
Technological advancements and improvements in field techniques have allowed
for accurate delineation of surficial karst features. A multitude of methods for
inventorying and mapping these resources include field mapping with hand-held GPS
units, surveys, map digitizing, and dye-trace tests for subterranean streams.
Hand-held geographic referencing tools are reliable contributors to field mapping.
Common geospatial software such as ArcPAD has been used successfully for field data
organization (Addison, 2003; Jasper, 2003). WALLS 2D software, which aides in cave
passage mapping, has been used in conjunction with ArcPAD to create on-the-fly
georeferenced cave maps (McKenzie and Veni, 2003). Further mapping and analysis can
be done in the ArcGIS software suite.
A range of features are inventoried to create surficial karst inventories. These
include sinkhole locations and catchments areas, springs, topographic drainage divides,
sinking streams, karst paleovalleys, hydrologic unit (HU) boundaries, vectors of
subsurface flow determined by dye-tracer tests, and boundaries of karst drainage basins
(Taylor, et al., 2005). Other mappable features include cave entrances, cave passages
(Florea et al., 2002), and spring chemistry (Green et al., 2001). The type of features
collected depends on the nature of the landscape.
Most karst projects employ conventional methods to inventory surficial karst
features. GPS point features (Iliffe, 2003), field surveys (Reese and Kochanov, 2003),
compilation of existing inventories (Florea, 2002; Gao et al., 2002; Gao et al., 2005a;
Gao et al., 2005b) and public dataset collections (Phelan, 2002) have been used to create
new karst feature databases. Feature digitization off topographic maps has also shown
25
success using scales of 1:100 (Applegate, 2003), 1:12,500 (Lyew-Ayee, et al., 2007), and
1:24,000 (Applegate, 2003). Feature identification from aerial imagery successfully
identified fault locations in Indonesia (Haryono and Day, 2004) and structural lineaments
(Doerfliger et al., 1999), though it failed to identify karst in the Marianna Islands
(Toepke, 2006).
Other unconventional methods have been developed to map features. GIS-based
algorithms have been used to count sinkholes based on topographic maps (Angel et al.,
2004). Applying a t-test to the two results revealed that the GIS algorithm is a reliable
method for sinkhole counts.
Mapped feature locations allows for further analysis to establish subterranean
connections. Using dye trace testing and node-to-node analysis, connections can be
established between known karst input and output points (Glennon and Goodchild, 2004).
Dye tracing can aide in characterizing springs, creating groundwater flow models in karst
environments, and delimiting karst drainage basins (Connair and Murray, 2002). Results
from these studies and their initial inventories can be manipulated in a GIS to create
higher order datasets such as bedrock and depth to bedrock maps (Green et al., 2001), and
calculation of karst drainage density (Glennon and Groves, 2002). In addition, survey
data analyzed within a GIS has identified the possible extent and relationship between
two cave systems (Horrocks and Szukalski, 2002).
26
4. DEVELOPMENT OF KARST SYSTEMS
4.1 Introduction
No single model of genesis explains the occurrence of karst in all regions, as it is
affected by many different spatial and temporal conditions. Varying geologic, biologic,
hydrologic, and climatic conditions account for significant variations in karst
development. Nonetheless, there are important common formative factors, and studies
conducted outside California have identified strong connections between karst
development and occurrence, and various geologic and surficial conditions, including
lithology, geologic structure and hydrography.
4.2 Structural and Stratigraphic Controls
The occurrence and distribution of karst is commonly linked to the presence of
lineaments, including both structural features such as faults and folds; and lithologic
lineaments, such as foliation and jointing. Cave conduit formation is promoted by pre-
existing porosity and the permeable nature of fissures (due to faulting, folding, and
foliation). The character of the primary porosity can be used to infer the general pattern
of subterranean conduit networks (Palmer, 1991). Moreover, mapping sinkhole
occurrence can be used to locate faults.
The relationship between karst development and geologic structure is scale
dependent (Florea, 2002). At a large scale, geologic structure determines the degree to
which limestone karst is exposed (Florea, 2005). At a medium scale, GIS analysis of
geologic structure maps reveals that sinkhole groups occur in association with faulting.
27
Remote Sensing and GIS have been used to analyze the relationship between
lineaments and karst. A study of the sensitivity of karst systems to water pollution was
improved when structural lineaments were added to the DRASTIC GIS program
(Hallman, 1997). In Vietnam (Dinh et al., 2002) and in Portugal (Forth et al., 1999),
lineaments overlain with faults and cave entrances revealed a positive correlation
between fault occurrence and karst development. Furthermore, regression analysis
reveals that sinkholes and caves preferentially form in proximity to faults and along
lithologic contacts between soluble and insoluble units (Stafford et al., 2005). Cavity
presence and number were successfully determined by power-log laws, which suggest
that karst feature density increases inversely with size of fractures and proximity to
fracture locations (Gross et al., 2004). The karst features formed parallel to the strike of
the karst-forming units.
4.3 Models of Karst Formation
Advanced spatial analysis tools allow for sophisticated studies of karst evolution.
Distribution analyses using GIS and remotely sensed imagery have examined the
uniformity and degree of clustering of sinkholes. Density analyses have been applied to
karst aggregations, such as sinkholes and karst streams. Preferential patterns of
development are commonly exhibited which, when related to geologic, structural, and
hydrologic conditions, may be used to infer karst development.
4.3.1 Distribution
The dispersion pattern of sinkholes has been related to both structural and
lithologic factors and to water table depth. Focal Sum Neighborhood (a measure of
28
clustering that sums the points in a defined neighborhood) analysis of sinkholes in
Missouri revealed a linear distribution parallel to faulting (Orndorff et al., 2000).
Sinkholes formed preferentially on slopes of 0 to 3 degrees. As the area of an individual
sinkhole increases, the depth to the water table has also been observed to increase.
Studies of sinkholes in Japan revealed an uneven distribution pattern of sinkholes (Terry,
2005). Where an uneven (and more random) pattern of sinkholes is observed, structural
bedrock deformation, major fault escarpments, and positions relative to the
carbonate/non-carbonate rock boundaries can be controlling factors.
Cockpit karst patterns were investigated using geostatistical analysis (Lyew-Ayee,
et al., 2007). Cockpit karsts are large dolines surrounded by hemispheroidal hills drained
by one or more sinkholes. Hills in close proximity were found to have similar
dimensions, and there was little variation in the depth and area of adjacent depressions.
Morphometry alone is not sufficient to determine karst genesis, but GIS modeling which
incorporates geologic data can be used to infer basic formative processes.
4.3.2 Density
Sinkholes differ greatly from region to region in their density of occurrence and
degree of clustering. Differences in bedrock type, porosity, geologic structure, soil cover,
and surface hydrology affect sinkhole development (Reese and Kochanov, 2003)
Sinkhole patterns range from dispersed (nearest neighbor is farther away than in an
expected distribution), to random (nearest neighbor is approximately the same distance as
in an expected distribution), to clustered (nearest neighbor is closer than in an expected
distribution). Cluster systems may be linear, as when sinkholes occur along a fault.
Sinkhole clustering results from multiple independent variables, such as slope and
29
character of the geologic structure (McConnell and Horn, 1972). The clustering of
sinkholes may be examined using nearest neighbor analysis, a technique which allowed
Gao et al. (2002, 2005b) to identify three regions of sinkholes in southeastern Minnesota.
However, the results are scale dependent. Analysis at three different scales revealed a
change from clustered to random to dispersed distributions as scale is decreased.
30
5. METHODOLOGY
5. 1 Introduction
The appropriate methods of obtaining geospatial information of karst features
depend on the needs of the project and the availability of existing data. Remote sensing
methods can positively identify large scale geologic and karst features, but are
problematic when resolutions are too coarse to locate surficial expressions. In such cases,
detailed field work is essential to identify karst features. This fieldwork may be guided by
geologic principals established by the existing literature and initial geospatial research
using aerial photography and geologic maps.
A number of procedures have been applied to the investigation of karst
development. Most notably, Gao et. al (2002; 2005a; 2005b) performed nearest neighbor
nearest neighbor analyses in Minnesota using ArcView 3.2. Other GIS-based studies
include Orndorff et al. (2000), who applied distribution analysis to sinkholes in Missouri,
and Terry (2005) who investigated sinkhole distributions in Japan. Power-log regression
(Gross et al., 2004) and simple linear regression (Stafford et al., 2005 and Williams,
1972) have been used to analyze the effects of geology, lithology, and hydrology on karst
formation.
5.2 Methodology: fieldwork, digitization, and GIS analysis
This project embodied three phases. The fieldwork phase was conducted in the
summer and fall of 2007 and consisted of six trips to Mineral King, totaling ten days of
fieldwork. The database management portion consisted of data set creation, digitization,
31
and database design. The final stage of this analysis involved using the geospatial data
and associated databases to perform spatial analyses using ArcGIS 9.2.
5.3 Fieldwork
Fieldwork in Mineral King Valley (Figure 5.1) was conducted from July to
October 2007 (Appendix A). Adjacent marble units were assigned informal names such
as Timber Gap, White Chief, Monarch, and Franklin, based on a nearby prominent
geographic feature. These areas offer varying geologic and hydrographic conditions.
Monarch and Franklin, for example, are steep drainages emerging from alpine lakes.
White Chief is a hanging valley with relatively gradual slope affected by water from a
stream springing from the granitic cirque wall. The marble at Timber Gap spans two
adjacent cirques, climbs three ridges, and is dissected by one perennial stream.
Karst features were mapped in the field using Global Positioning System (GPS)
units. Three types of Garmin GPS units were used, accurate to within 3-6 meters with the
Wide Area Augmentation System enabled: the primary units were Garmin GPSMap 60
CSx, but the Garmin Vista C and Garmin GPS III units were used when technical
difficulties arose with the primary units. All mapping was done using Universal
Transverse Mercator coordinates in the NAD 1983 datum.
The USGS 7.5’ topographic map was used to identify previously mapped karst
features in the area, which included springs, sinkholes, and mines. These elements were
digitized directly from the map. The preliminary inventory provided an opportunity to
spatially reference the 62 karst features previously identified by the Cave Research
Foundation. Marble units were located using a geologic map created by Busby-Spera and
32
Figure 5.1 View of Mineral King Valley looking south from the ridge west of Timber Gap. The White Chief Valley marble can be identified as a thin white band (red arrow) on the upper right side of the photo.
Saleeby (1987) and a tentative National Park Service geologic map overlaid onto the
USGS 7.5’ Mineral King quadrangle. National Park Service aerial photos and
hydrographic maps served as a validation tool for the features collected by GPS and
ensured the quality of the new data set for the project.
Point location mapping was performed using commercial quality GPS. Springs,
sinks, caves, swallets, and disappearing stream locations were all collected in the field as
points. A karst spring was identified as a point where water surfaced from the marble
(Figure 5.2). Sinks were identified surface depressions (Figure 5.3). No distinction was
made between open and closed depressions. Caves were any solutional opening that can
be physically accessed by humans (Figure 5.4), whereas solutional openings too small for
access were classified as swallets. Any stream disappearing into the marble was
33
categorized as a sinking stream (Figure 5.5). Over the field season 133 caves, 12 swallets,
70 springs, 12 sinking streams, and 386 sinks were tallied.
Fieldwork showed that the pre-existing geologic maps were often in error with
respect to the extent and location of marble units. Therefore, an important aspect of the
field mapping was the determination of marble boundary points. Polygons were created
from each series of points representing a marble unit during the data management stage.
These point features were used in concert with aerial imagery and surface photographs
taken during fieldwork to digitize the revised marble unit boundaries. The aerial photos
were used for marble outcrops in non-vegetated areas. The GPS points augmented the
photos and guided digitization in vegetated areas.
Figure 5.2 Point location mapping at a karst spring in the Monarch drainage.
34
Figure 5.3 A collapse sink in the lower White Chief Valley. Alluvium, shown in the collapse structure, overlies the White Chief marble.
Figure 5.4 One of the many entrances to White Chief cave.
35
Figure 5.5 A sinking stream at the upper entrance of Cirque Cave.
Naming conventions were implemented in the field for data organization and
management. Spring waypoints were expressed by blue nodes starting with “Sprng1” and
increasing numerically; sink waypoints were represented by green nodes starting with
“Snk1”; caves were symbolized by red nodes starting with “Cv1”; swallets were
represented by red nodes starting with “Swlt1” and disappearing streams were
represented by blue nodes starting with “SnkStrm1”. Catchment features were denoted by
yellow nodes and left with the default numerical name. Marble points were represented
by purple nodes and also left with the default numerical name. Where applicable, cave
features were saved with their known names.
5.4 Database Management
The data management process started with the creation and design of database and
was followed with the creation of geospatial data to be stored in the database. Using
36
ArcGIS 9.2, a geodatabase was created and designed to store feature classes for
hydrography, geology, vegetation, infrastructure, and karst features. Raster datasets used
for this project included a USGS 7.5’ topographic map of Mineral King, 10-meter
resolution DEM’s for Sequoia and Kings Canyon National Parks, and high resolution 1-
m aerial imagery for Mineral King. Vegetation, hydrography and infrastructure datasets
were obtained from the National Park Service GIS data website (National Park Service,
2007). Geology data were obtained from two geologic maps. Faults and folds were
digitized from the geologic map of Busby-Spera and Saleeby (1987) and lithology was
obtained from the geologic map supplied by the National Park Service. The marble
dataset was populated with the newly created marble map. The karst feature class
includes empty feature datasets for each sub-region (Timber Gap, Monarch, Franklin,
White Chief). These datasets were populated with data generated from fieldwork.
Field data were downloaded onto a laptop using Minnesota’s Department of
Natural Resources Garmin interface program. At the conclusion of the field season, the
data were compiled into feature classes according to sub-region. Primary attributes
ascribed were location (Franklin, Monarch, Timber Gap, White Chief) and feature names,
which begin with an abbreviation of the location name and ascending numerically from
one (e.g. TG1, TG2, etc). Pre-named caves and sinks retained their original names in this
field. Other attribute data assigned to each dataset include elevation, date collected, GPS
model used for collection and GPS accuracy, and a comments section to record important
notes.
Marble polygons (Figure 2.4) were digitized using aerial imagery, GPS point
features, and field photos. Where mantled by debris at northern White Chief and the
37
lower Monarch drainage, marble boundaries were estimated based on mapped features
and adjacency to the next outcrop. In the upper Franklin drainage, marble occurs as
interbeds with calc-silicate and marl. To account for scale issues arising from the
numerous interbeds, one unit was mapped just west of Franklin Lake.
A lineament feature dataset was created under the Geology feature class. This
dataset contains lineations digitized from the Color Infrared aerial imagery on the marble
lenses. Lineaments are defined as linear breaks in the marble based on the image. No
distinction was made between joints and other lineations resulting from mechanical
weathering, since all forms of secondary porosity provide a foundation for the formation
of karst.
The stream feature class was revised by digitizing channels omitted from the
original dataset. The revised dataset identified three additional channels. One perennial
stream flows east across the Timber Gap marble. A second channel was identified in the
Eagle Lake drainage, dissecting a small marble outcrop, but intermittent in nature. The
third was found north of Franklin Lake. It flows through a blind valley, ending abruptly
at a large talus-filled sink. A paleo-channel, apparently connected to the sunken stream,
continues west towards its confluence with Franklin Creek.
5.5 Developing the Predictive Model
In order to help predict the presence of surface karst features, a predictive model
was created in ArcGIS 9.2. Datasets for marble, faults, folds, streams, and lineaments
were used to create a map indicating areas of probable karst development. The model was
then compared to actual karst (point feature) locations to assess its accuracy.
38
Development of the predictive model required a four step process (Figure 5.6). In
the first step, streams, faults, folds, and lineaments (linear features) were buffered in 10 m
zones, with the highest value (20) assigned adjacent to the linear feature. In the second
step, outcrops were delineated with polygons, which were buffered concentrically in 5 m
increments, with the highest values (20) assigned to the buffer zones on the perimeter,
and the lowest values in the center. In step three, the five buffered layers (stream, fault,
fold, lineament and marble) were joined in a union overlay, which combines features into
a single dataset while retaining all associated attributes of each dataset. The fourth step
was to devise the final predictive variables by summing weight values for streams,
lineaments, faults, folds, and marble contacts (Equation. 5.1). The equation is given by
PV= MB + LB + FtB + FdB + 4(SB) (5.1)
where PV is the predicted value, MB is the value of the marble buffer, LB is the value of
the lineament buffer, FtB refers to the fault buffers, FdB is the fold buffer values, and SB
is the value for the stream buffers. The value of streams was multiplied by four, to
account for their potentially stronger influence (through their action as a solvent) on karst
feature development. The development of karst requires both a form of porosity (faults,
folds, marble contacts, and lineaments) and a solvent mechanism (water). Modern
streams were assumed to be the sole corrosive agent and their weight was considered
equivalent to the sum of the four porosity variables in the calculation of the final score. In
many respects, the model is overly simplistic with respect to water corrosion. It ignores,
for example, the effect of modern snowmelt runoff, which may have multiple entry points
39
into the karst system (other than the stream channel). Furthermore, the effects of historic
glaciations on karst formation are not considered. This model thus represents a first
approach to understanding karst formation and distribution.
Probabilities were symbolized on the map by classifying the predictive variables
into ten classes using the quantile method. The quantile classification method distributes
features evenly across the specified number of classes. For example, if there are 100
features and ten classes, there will be 10 features in each class. A green to red color
scheme was used to depict increasing probability. The highest probability is red, and the
lowest is green.
To complete the maps, the joined dataset was clipped to the marble polygons to
exclude buffered areas beyond the mapped marble units, as no karst features would be
found there. Lastly, to improve the appearance of the map, a dissolve tool was run to
combine adjacent areas of identical scores into single features.
The accuracy of the model was assessed by overlaying the recorded karst features
(from fieldwork) on the probability map. The intersect tool in ArcGIS 9.2 identified the
probability score associated with each feature. These scores were grouped by class and
regressed against the actual number of features located within each zone.
40
Figure 5.6 Flowchart showing the steps in the creation of the karst probability map. 5.6 Analysis
Spatial analysis tools in ArcInfo 9.2 were used for directional, cluster and density
analyses to investigate distribution patterns. Specific tools applied to the data were
directional distribution, nearest neighbor, and spatial auto-correlation. Density analysis
was performed to analyze specific cluster patterns. Results were overlaid with geologic,
hydrographic and slope data to analyze potential connections and influences on
development. Histograms showing distributions of distances from karst features to
formative factors, such as lineations, were then produced. Finally, simple linear
regression models were created relating density to proximity to streams and primary
porosity.
41
The four sub-regions identified in the study are Franklin, Monarch, Timber Gap,
and White Chief. The Franklin karst area includes all areas draining the Franklin Lakes
extending down to the confluence of Franklin Creek with the East Fork Kaweah River.
The Monarch karst area was considered to be all areas draining the Monarch Lakes down
to the confluence of Monarch Creek and the East Fork Kaweah River. The Timber Gap
karst area is the extent of the marble band from just south of Timber Gap north to its
termination south of the confluence of Timber Gap Creek and Cliff Creek. The White
Chief karst includes the marble in White Chief Valley southward through the Eagle Lake
drainage to the marble’s terminus at Tufa Springs. This grouping into one region results
from previous studies connecting the karst hydrology in White Chief to Tufa Springs
(Tinsley, 1999).
Distributional ellipses were created using Spatial Statistics tools in ArcGIS 9.2,
defining dominant directions of formation. This tool calculates the mean center of a
defined set of point features, and then calculates the standard deviation of each point
from the mean center in the x and y direction (ESRI Support Center). This calculation
defines an ellipse that indicates any preferred orientation by varying degrees of
elongation. Thinner, more elongated ellipses define a dominant orientation in a particular
direction, whereas more circular ellipses indicate no preferred directional distribution.
Nearest neighbor analysis was performed for each of the four sub-regions in the
study using all point features. The nearest neighbor statistic compares the average
distance of the nearest point in a given dataset to that of a completely spatially random
pattern. Values below 1 indicate clustering, whereas values above 1 indicate dispersion.
A table was created with Nearest Neighbor scores and Z-scores indicating statistical
42
significance for each of the four areas. The Z-score is a measure of statistical significance
that tests whether or not the results are due to random chance by testing the distribution
of the data. Using the standard statistical confidence level of 95%, Z-scores become
significant above 1.95 and below -1.95. This suggests the pattern is not due to random
chance, and must be influenced by other variables.
A simple density analysis layer was created using Spatial Analysis tools for each
sub-region. Default circle radius values of 30-m were used to define the neighborhood.
The tool totals the number of points occurring within a neighborhood and divides that
number by the area to define density. Output density layers were expressed in features
per hectare for this study. Faults, folds, streams, lineaments, and marble polygon outlines
were superimposed onto the density polygons to investigate possible influences exerted
by these variables on karst feature densities.
A proximity tool defined straight line distances between each feature density
value to nearest fault, fold, lineament, stream, and marble contact. Density raster datasets
were first converted into polygon layers to allow for calculation. Spatial identity overlays
were performed for each variable to create one feature class for each region (Figure 5.7).
The resulting table for each of the four sub-regions defined distances from each karst
feature with its accompanying density to the nearest faults, folds, streams, lineaments,
and marble contact.
43
Figure 5.7 Flowchart illustrating the steps taken during the proximity analysis. The results from each variable were combined using a spatial overlay to create one comprehensive dataset for each sub-region.
Slope raster layers were derived from the Mineral King 10-m Digital Elevation
Model using the Surface Analyst tool. The input DEM is used to calculate the maximum
change in elevation (z-value) between each adjacent cell. A new raster dataset is created
expressing slope between 0° and 90°. The slope layer for this study was converted to a
polygon layer to allow for a spatial intersect overlay with the converted density layer
from the previous analysis (Figure 5.8). The results were appended to the previous
overlays shown in figure 5.6, creating datasets defining distances from each density to
each nearest potentially formative feature (streams, faults, folds, lineaments, marble
contact) and slope for each region
44
Figure 5.8 Flowchart illustrating the steps taken in the slope analysis. A spatial overlay with each region feature class identified the slope angle on which they formed.
Tables from final overlays were exported into SPSS 16.0 for correlative analysis.
Histograms were created for each area showing distances from each karst feature to
streams, lineaments, faults, folds, marble contacts, and a histogram for slope. Large
ranging datasets were partitioned into 100-m bins, low ranging datasets were divided into
10-m bins, and slope histograms were put into five degree bins. The “Distance to Faults”
histogram was omitted from the Franklin area, the “Distance to Folds” histogram from
White Chief, and “Distance to Faults” and “Distance to Folds” from Timber Gap since
they did not cross the marble.
Simple regression analysis linking density dependence to proximity streams was
performed for the four regions. The karst literature identifies several pre-requisites for
karst formation, including the need for pre-existing primary porosity in the form of
faulting, fracturing, jointing, or other lithologic lineations, and the input of a solvent.
45
While the presence of pre-existing porosity is necessary for development, a solvent must
be available to drive dissolution and corrosion. Although Pleistocene glaciers may have
affected karst development in the past, altering runoff regimes, water pressure, and water
acidity, their role in the formation of features is beyond the scope of this study. The direct
effects of precipitation in the study area are unknown, and thus streams are considered as
the primary solvent mechanism
Curve estimation regression was used for each of the eight regressions in SPSS
16.0. This tool allowed a simple and reliable method for determining the best type of
regression line needed for the data by testing a number of regression curves. Each
regression resulted in an x-y graph of the independent and dependant variables, a “Model
Summary” table, and a “Coefficients” table.
The “Model Summary” table gives values of R and R2. The R statistic is the
correlation coefficient and is expressed as a value between -1 and 1. Values near -1
indicate a strong negative correlation, whereas values near 1 indicate a strong positive
correlation, and values near 0 show no correlation. The R2 statistic is a value between 0
and 1 and expresses how much variability in the dependant variable is explained by the
independent variable. A perfect value of 1 would suggest that the independent variable is
the sole influence on the dependant variable, whereas lower values imply the influence of
variables not accounted for within the regression. The model summary table also gives
the standard error of the curve, which expresses the average distance of each point from
the regression line in the y direction.
The “coefficients” table shows the regression constants. The values of interest are
the unstandardized coefficients. These values represent the slope and y-intercept of the
46
regression curve. This table also gives a significance value as a fraction of 1. Significance
values ranging from .000 - .050 show statistical significance at the 95% confidence
interval, which will be assumed in this study.
47
6. RESULTS
In this chapter, the results of the predictive model, GIS analysis and statistical
analyses will be discussed. Each karst region will be covered in turn: the Franklin Creek
drainage, the Monarch Creek drainage, the Timber Gap region, and the White Chief
region.
6.1 Predictive Model
Four maps predicting karst feature locations were created (Figures 6.1 – 6.4); one
for each karst region in Mineral King. Using the quantile method of classification,
predictive values were equally distributed into ten classes, with values ranging between 1
and 129. Areas of higher likelihood of karst development are denoted in red, whereas
areas least likely to reveal surficial features are shaded green.
In addition to the karst potential maps, the data were analyzed using linear
regression (Figure 6.5). Each of the four models depicts the distribution of recorded karst
features relative to the predicted values, and compares the trend to the expected
distribution.
The distributions observed for each of the four karst regions indicates a decrease
in karst features with an increase in the predicted variables, contrary to expectations.
Additionally, locations above values of 66 yielded no karst features. This suggests that
there are other factors, beyond those included in the model, that have a strong influence
on karst formation. These may include small scale lineaments that were not mapped, the
role of past glaciation, or snowmelt runoff. Reasons for the large deviations from the
48
expected distribution of karst features are examined for each karst region in the following
sections using various correlative analyses.
Figure 6.1 Karst probability map for the Franklin drainage. Red areas indicate highest likelihood of karstification.
49
Figure 6.2 Karst probability map for the Monarch drainage. Red areas indicate highest likelihood of karstification.
50
Figure 6.3 Karst probability map for Timber Gap. Areas of highest potential are indicated in red.
51
Figure 6.4 Karst probability map for the White Chief marble. Areas in red indicate highest potential.
52
Predictive Model vs. Karst Feature Locations Expected
0
20
40
60
80
100
0 20 40 60 80 100 120 140
Probability Value
No.
Fea
ture
sFeaturesLinear (Features)
Predictive Model vs. Karst Feature Locations
Franklin
0
10
20
30
40
50
60
0 20 40 60 80 100 120 140
Probability Value
No.
Fea
ture
s
FeaturesLinear (Features)
Predictive Model vs. Karst Feature Locations
Monarch
02468
10121416
0 20 40 60 80 100 120 140
Probability Value
No.
Fea
ture
s
FeaturesLinear (Features)
Figure 6.5 Regressions showing the expected distribution of features within each class of the predictive model, and actual distributions for Franklin and Monarch. Trendlines indicate a decrease in features with an increase in predictive variables.
53
Predictive Model vs. Karst Feature Locations Timber Gap
05
10152025303540
0 20 40 60 80 100 120 140
Probability Value
No.
Fea
ture
sFeaturesLinear (Features)
Predictive Model vs. Karst Feature Locations
White Chief
0
50
100
150
200
0 20 40 60 80 100 120 140
Probability Value
No.
Fea
ture
s
FeaturesLinear (Features)
Figure 6.5 (cont.) Regressions showing the distribution of features within each class of the predictive mode for Timber Gap and White Chief. Trendlines indicate a decrease in features with an increase in predictive variables.
6.2 Distribution, Density, Correlative Analysis, and Regression
In general, the marble lenses in these regions are narrow and elongate, a factor
that influences the distribution of karst features, causing a basic linearity in their
distribution. Therefore, several precautionary measures were undertaken to reduce this
influence. Cluster features were identified visually on the map, and then delineated with
an ellipse. Standard deviation ellipses, which show whether there is a preferential
orientation, were created using ArcGIS 9.2: each ellipse was based on one standard
deviation (68% of features), removing outlier points which tend to orient the major axis
54
of the ellipse parallel to that of the marble. A nearly circular ellipse (length:width ratio
~1) indicates an apparently random distribution of karst features, whereas an ellipse with
a high length:width ratio suggests a preferred linear orientation, potentially linked to
underlying geologic or hydrologic variables, such as faults, joints, or stream courses.
The marble units and karstic features are named for ease of discussion. For
example, the three marble exposures in the Franklin Creek drainage are designated AF,
BF, CF, and those of the Monarch Creek are designated AM, BM, etc. Each cluster is
further described, using a numerical subscript, such as CF1 and CF2.
6.3 Franklin Creek Drainage
Three marble units (two large, one small) are exposed in the Franklin Creek
drainage, of which only the two largest have karst features. There is a narrow, elongate
exposure of basically pure marble and a more extensive outcrop of marble interbedded
with marls and calc-silicates (Figure 6.6). Franklin Creek runs through the interbedded
unit. Both units appear to be moderately fractured, with joints trending north-northwest,
parallel to strike. The folds that appear within the marble units do not appear to influence
the distribution of the karst features.
The main channel of Franklin Creek emerges from Lower Franklin Lake and
flows northwest, crossing the marble and then sinking to flow subsurface. Multiple
springs then feed its reemergence as it cascades down the middle and lower reaches of
the drainage, crossing the lowest marble unit before connecting with the East Fork
Kaweah River. The tributary to Franklin Creek commences at a spring in granite, and
55
then flows across the interbedded marble, disappearing and reappearing numerous times
through a chain of caves, sinks, and springs.
There were 153 features mapped in the Franklin Creek drainage, not all of which
can be clearly seen in the map. Three ellipses were created for the Franklin marble: two
for features in the upper drainage (CF1 and CF2) and one the lower drainage (AF1) (Figure
6.1). No ellipse was created for BF1 as there were insufficient karst features. The three
ellipses are moderately to highly elongated (length:width = 3.4-6.9:1), indicating strong
underlying influences. CF1 and CF2 appear to be aligned with, and in close proximity to
Franklin Creek and its tributary. The northwest-trending ellipse at CF1 (inset) is oriented
approximately parallel to the strike of the marble, near the contact with non-carbonate
rock, parallel to joints, and in close proximity to Franklin Creek. Other karst features fall
outside of the ellipse (cluster of sinks to the southeast), but maintain the tendency to be
parallel to joints and to trend northwest. AF1 (23 features) parallels the marble boundary
and the orientation of joints. No karst features were observed in Unit BF1, either on aerial
photography or in the field.
56
Figure 6.6 Distributional ellipses showing preferred orientation of karst feature clusters in the Franklin drainage.
57
Figure 6.7 Karst density map for the Franklin marble. Density is expressed in points per hectare. The highest density of features occur closest to Franklin Creek
The orientation of the ellipses (CF1, CF2, and AF1) varies: AF1 and CF1 are broadly
parallel to one another, to the marble contact, and to the jointing. By contrast, CF2, with
its more westerly orientation, more closely parallels the trend of the tributary stream. The
largest numbers of features occur in the region of CF1, parallel to both the joints and the
stream.
Feature density is not well illustrated in the maps showing distributional ellipses,
and therefore a karst density map was produced for each region. Feature areas of highest
density in the Franklin marble tend to occur near streams (Figure 6.7). In general, karst
58
features in Mineral King are clustered (Nearest Neighbor Score = 0.36). The highest
density of features (25 per hectare) is found along Franklin Creek – an area characterized
by numerous small caves. The second highest density is along the tributary (12 features
per hectare), where 4 cave systems and a large sink/sinking stream are found. Although
Figure 6.1 suggests that may karst features parallel joints, water appears to be an essential
ingredient, as the upper jointed slopes lack karst development. Folds have no apparent
affect in the Franklin Creek drainage.
6.3.1 Histogram Correlation
Histograms can help indicate potential independent variable correlations on karst
development. While the character of histogram distributions can be useful, the statistical
means are generally skewed by outliers. Specific distributions and defined peaks can lead
to productive correlative analysis.
Faults, folds, marble contacts, and large-scale lineaments provide primary
porosity for karst formation. Perennial, ephemeral, and intermittent streams provide
potential solvent action driving karst formation. Karst feature distances to these variables,
collectively termed “formative variables”, were compiled into histograms. Analysis of
lithologic slope angle at the location of each feature was also included in a histogram.
Histograms for the Franklin marble (Figure 6.8) indicate strong correlations with
streams, lineations, and distance to marble boundaries. No faults occur in the Franklin
karst.
59
Analysis of feature distance from streams in Franklin show that 74% of all
features occur within 50 meters of a stream. Thereafter, there is a marked drop off in
karst features, which form up to 375 m from either stream.
Distance analysis to marble contacts was only done for the pure marble unit,
owing to the complicated geologic conditions in the interbedded marble unit. The
histogram shows that 59% of karst features lie within the first 10 m of the contact. A
slight preference is evident between 5 m and 10 m, where 41% of karst features occur.
Increasing distance from the contacts is accompanied by a decrease in karst development.
The histogram for lineament proximity indicates 51% of karst occurs within 25 m
of fractures. Additionally, 68% are positioned within 50 m of a lineament, indicating a
positive correlation. A distinct decrease in karst is observed with increasing distances.
Three folds lie within the marble boundaries. Analysis shows that <1% of karst
features occur within 50 m of a fold and <10% fall within 100 m. Features formed
outside this threshold are likely unaffected by the fold’s presence. A syncline and
anticline complex dissects the marble unit in the lower drainage, but shows little apparent
effect on karst development. In additon, the overturned anticline in the interbedded
marble shows no correlation with the presence of karst.
The mean slope of the Franklin Creek region is 23.2˚. A histogram of slope angles
and karst features shows preferential development between 20˚ and 30˚ (45% of
features). An additional 31% of features occur on slopes between 10˚ and 20˚.
Karstification is minimal on more level surfaces and steeper slopes.
60
Figure 6.8 Histograms showing karst feature distances to streams, marble contacts, lineations, folds, and slope for the Franklin drainage.
61
6.3.2 Regression
Regression analysis is a statistical method for determining the amount of variation
in a dependent variable “explained” by independent variable(s). Karst densities
(dependant variable) are regressed against distances to streams in each region
(independent variable) to determine how much influence present stream locations had on
the development of karst in Mineral King Valley.
Karst feature densities in Franklin decrease with respect to the normal logarithm
with increased distance from streams (Figure 6.9). The lognormal regression provided the
best fit through empirical investigation of regression models. The regression equation
(Equation 6.1) is given as:
Y = -2.019ln(x) + 16.261 (6.1)
where -2.019 is the slope and 16.261 the y-intercept. The adjusted R-squared value of
0.155 (Table 6.1) shows a less than perfect fit, indicating that other forms of solvent
action are also important for producting karst (for example, snowmelt on slopes). This is
also evidenced by the standard error of the estimate, 5.278. However, the correlation
coefficient (0.402) does indicate a positive correlation between streams and karst density.
Results indicate statistical significance at the 95% confidence interval, indicated by the
significance (p) value (.000).
62
Figure 6.9 Regression model analyzing stream distance on karst feature densities in the Franklin drainage is given above. Table 6.1 Tables of the R, R-squared, error and regression coefficients for the Franklin drainage.
63
6.3 Monarch Creek Drainage
Three lenticular, NNW-SSE striking marble units are located within the Monarch
Creek drainage (Figure 6.10). Only two units, which occur sub-parallel to strike, exhibit
mappable fracturing (CM1 and DM1). AM1 represents a cluster of springs formed in
unmapped interbeds found along the channel of Monarch Creek. These units begin in the
Monarch drainage and extend southward along the steep east wall of Mineral King
Valley. The karst features were mapped, but the extent of the marble was not as the
access was too difficult. BM1 occurs as a thin, short outcrop on a north-facing slope,
devoid of any identifiable geologic and lithologic structure. The short unit becomes
buried and reappears at a spring along Monarch Creek. CM1 is found along the steep
southeast slopes of Empire Mountain and is moderately folded by a syncline at its upper
contact with calc-silicates. DM1 is the largest unit, and the only one dissected by Monarch
Creek. An anticline occurs along its northeast extent.
Monarch Creek begins at Lower Monarch Lake, to the east of the area shown in
Fig. 6.10, and then follows a course westward toward its confluence with the East Fork
Kaweah River. The stream cascades down the glacially quarried benches through the
interbeds that compose AM1.
There were 68 features mapped in the Monarch Creek drainage. Many occur as
clusters and are represented by only a single point on the map. Only one ellipse was
created (DM1- shown in inset) since the other units were too narrow and did not yield
sufficient karst. The elongated (length:width = 3:1) ellipse is located along the western
contact in close proximity to a cluster of caves and springs. The long axis approximately
64
parallels the strike of the marble. However, it does not parallel the fold, lineations, and
stream.
The Monarch drainage (Figure 6.11) contains two areas of pronounced density.
Karst features are generally clustered (Nearest Neighbor Score = 0.21). The highest
density (37 features per hectare) occurs along the lower reaches of Monarch Creek
(AM1). The second area of high density (~25 features per hectare) lies along the western
edge of unit DM1 and along Monarch Creek. Caves and springs form near the marble
contact and along the Monarch Creek streambed in this location. Moderate densities (~16
features per hectare) coincide with anticlines and their related lineations crossing the
same unit.
65
Figure 6.10 Distributional ellipses showing preferred orientation of karst feature clusters in the Monarch drainage.
66
Figure 6.11 Karst feature density map for the Monarch marble. Density is given in points per hectare. The densest clusters occur along the western reach of the channel. 6.4.1 Histogram Correlation
Folds, faults, lineaments, and streams are found within the Monarch drainage. No
faults occur within a marble unit.
Distances between karst features and streams (Figure 6.12) indicate a high
positive correlation. Of the 68 features in Monarch drainage, 54% occur within 25 m of
Monarch Creek. Development of the other 46% of features does not appear to correlate
with the location of the stream.
A high positive correlation exists with development along the marble contact. The
graph indicates that 44% of features in the Monarch marble units occur within 5 m of a
67
contact with non-carbonate rocks, and 51% fall within 10 m. No karst is found beyond 55
m.
Lineament proximity analysis displays a weaker positive correlation with karst
features than in Franklin drainage. Although only 8% of karst features were developed
within 25 m of a lineament, 29% are positioned within the first 50 m. The Monarch basin
contains the fewest lineaments of the drainages studied.
One syncline and one anticline lie within the Monarch drainage. Analysis
indicates that 24% of karst develops within 100 m of a fold.
The mean slope in Monarch drainage (23.9˚) is similar to that observed in the
Franklin drainage. However, a general preference for lower angle slopes is displayed: the
histogram shows that 91% of the karst developed on slopes angles between 10˚ and 35˚.
Within this range, however, there was no preferred slope angle; slopes steeper than 35˚
did not exhibit significant karst.
Figure 6.12 Histograms showing karst feature distances to formative variables are shown above for the Monarch drainage.
68
Figure 6.12 (cont.) Histograms showing karst feature distances to formative variables are shown above for the Monarch drainage. 6.4.2 Regression Regression for karst densities in Monarch reveals a weak lognormal relationship
with distance to streams (Figure 6.13, Equation 6.2). The model is represented by the
equation,
Y = -1.133ln(x) + 20.363 (6.2)
69
where -1.133 is the slope and 20.363, the y-intercept. The standard error of 8.899 (Table
6.2) is larger than Franklin, indicating an exceptionally large variance. The adjusted R2
value of 0.055 signifies a poor fit, suggesting other variables influence dissolution.
However, the coefficient of correlation does suggest a slight positive correlation (0.264).
Karst formations along streams are localized to spring clusters in the lower drainage and
caves along Monarch Creek below Lower Monarch Lake. The significance values (.036)
indicate statistical significance at the 95% confidence interval.
Figure 6.13 Regression model analyzing stream distance on karst feature densities in the Monarch drainage. Table 6.2 Tables of the R, R-squared, error and regression coefficients for Timber Gap.
70
Table 6.2 (cont.)
6.5 Timber Gap Timber Gap is comprised of one narrow, lenticular, NNW-SSE striking marble
unit. Faults and folds are not found within the unit, but numerous lineations occur, largely
parallel to strike. The marble crosses two small glacial valleys. An unnamed stream
crosses perpendicular to the trend of the marble.
Timber Gap (Figure 6.14) contains three natural assemblages of karst features,
which were used to define each of the three ellipses. All three are moderately
(length:width = 4.5:1) to highly (length:width = 12:1) elongated. The distribution centers
are located in topographic lows on north-facing slopes. AT1 (10 features) is located near
the northern terminus of the marble unit. AT2 (inset) occurs in proximity to the stream,
within a cirque, and parallel to the marble contact and lineations. AT3 occurs along a set
of parallel and perpendicular lineations, likely resulting from glaciation. Each assemblage
exhibits strong preferential distribution with the lithologic strike and lineations.
71
Three areas of high density (Figure 6.15) coincide with the three natural clusters
(Nearest Neighbor Score = 0.18). The southernmost has the highest feature density (22
features per hectare), formed in association with the relatively close marble contact and
numerous lineations, and interpreted to be glacial in origin. The central cluster has the
second highest density (~20 features per hectare) and is positioned adjacent to the stream
and along the lower elevations of the glacial valley. The northernmost assemblage is the
least dense of the three (~4 features per hectare) and is characterized by relatively
dispersed sinks toward the northern terminus of the marble, where the bedrock becomes
mantled by alluvium.
72
Figure 6.14 Standard deviation ellipses showing preferred orientation of karst feature assemblages on the Timber Gap marble.
73
Figure 6.15 Density map for the Timber Gap marble. Density values are given in points per hectare. The highest density is near the southern tip of the outcrop.
74
6.5.1 Histogram Correlation
Analysis of stream distance reveals that 44% of the 89 karst features occur within
the first 50 m of the lone stream on the Timber Gap marble. Only those features at AT2
were used for analysis. Most of the karst features are located in adjacent valleys and thus
are unaffected by the stream.
Karst features form within the first 30 m of the contact. While 23.5% of the
features lie within the first 5 m, there appears to be an even distribution at five meter
intervals up to 25 m. The relatively thin marble unit varies in width but does not exceed
60 m in any area.
A high positive correlation exists between karstification and lineaments. The
distribution mirrors those of Franklin and Monarch drainages. The vast majority of karst
features on Timber Gap are found within 25 m of a lineament (58.5%). Another 16% are
located between the 25 m and 50 m distance.
The distribution shown by the slope values reveals a distribution similar to that of
Franklin. A preference for karst development is shown between 20° and 25° (29% of
features). Approximately 76% of all karst occurs on slopes between 15° and 30°.
Relatively few karst features are found on slopes below 10° and above 30°.
75
Figure 6.16 Histograms showing karst feature distances to streams, marble contacts, lineations, and slope for the Timber Gap marble. 6.5.2 Regression
Point densities decrease linearly with distance to the tributary stream on the
Timber Gap marble (Figure 6.17, Equation 6.3). Empirical investigation revealed that the
linear regression model provided a considerably better fit than other models for Timber
Gap. The prediction equation for the model is:
Y = -0.50 (x) + 16.447 (6.3)
76
where -0.50 is the slope and 16.447 is the y-intercept of the linear function. The standard
error is 5.419 and adjusted R2 is 0.102 (Table 6.4), indicating a poor goodness of fit. The
correlation coefficient (0.362) indicates a slight positive correlation between stream
distance and density. Results are affected by the presence of only one stream on the
Timber Gap marble.
Figure 6.17 Regression model analyzing stream distance on karst feature densities on Timber Gap is given above. Density is measured in points per hectare. Table 6.3 Tables of the R, R-squared, error and regression coefficients for Timber Gap.
77
Table 6.3 (cont.)
6.6 White Chief
The White Chief marble (Figure 6.18a and b) occurs as one NNW-SSE striking
lenticular unit, which is mantled beneath 150 m of Tioga stage glacial debris for much of
its northern extent (Figure 6.18a and the northernmost part of the outcrop shown in
Figure 6.18b). The unit clearly emerges in the southern White Chief Valley (Figure
6.19b). One pre-batholithic east-west trending fault cuts the marble on a ridge north of
White Chief Valley (Figure 6.18a). Extensive fracturing, such as jointing, glacial
plucking, and frost action cracks, are prominent on the exposed marble outcrop in White
Chief Valley (Figure 6.18b).
Four streams cross the White Chief marble. White Chief Creek, which springs
from high in the granitic walls of the cirque, travels through a series of sinks, caves, and
springs along the large outcrop before sinking into the subterranean karst in White Chief
meadow. Eagle Creek, which carries water from Eagle Lake, sinks into Eagle Sinkhole
during low flow periods. During high flows, water enters the sinkhole, but also flows
northeasterly across the marble near AW1. Thus, the stream near AW1 is intermittent. A
fourth (northernmost, unnamed) stream springs from the marble unit as Tufa Falls and
Spring Creek.
78
The 359 features recorded in the field prompted the use of ten groupings for
distributional analysis in the White Chief marble. Three groups were created in the
northern extent of the marble in units AW and BW (Figure 6.18a) and seven groups were
chosen for unit CW in White Chief Valley (Figure 6.18b). AW1 coincides with the
intermittent stream channel where the marble is briefly exposed. BW1 represents a group
of larger sinkholes located parallel and adjacent to Eagle Creek. A large complex of sinks
in glacial till composes BW2, although there is no stream in this area. The orientation of
this ellipse parallels to the NE-SW striking fault approximately 0.25 km to the south. The
orientation of the three ellipses varies. AW1 (length:width = 2.6:1) trends parallel to the
stream channel, but not the inferred marble contact. The large sinks that comprise BW1
parallel the inferred contact and form approximately parallel to the sinking stream. The
BW2 ellipse suggests the sinks are distributed NE-SW, which correlates with the
orientation of the ridge, and the fault 0.2 km to the south.
The seven ellipses within unit CW are oriented approximately parallel to the
marble unit’s strike. These ellipses are less elongated than those to the north
(length:width = 1.3-2.9:1), suggesting a weak preferential distribution. The sinks
comprising CW1 lie in the meadow and display a weak north directional distribution
(length:width = 1.55:1). Groups CW2, CW3, CW4, CW5, and CW6 represent features
distributed along the carbonate/non-carbonate boundary and they parallel lineations.
Three clusters (CW1, CW2, CW4) are also located adjacent to White Chief Creek. CW7 is
nearly circular (length:width = 1.3:1) and is set among a cluster of caves and sinks along
NW-SE trending lineations.
79
Higher densities within the White Chief marble tend to occur in association with
geologic and lithologic structure. Generally, features exhibit a clustered pattern (Nearest
Neighbor Score = 0.22). The highest density occurs at the White Chief Cave (inset)
complex with a density of 48 features per hectare. This area is positioned near, but not
on, the marble contact and lineations. The second highest density (~35 features per
hectare) is the group of sinks north of the pre-batholithic fault. Another high density
locale (~25 features per hectare) is situated near numerous parallel to sub-parallel
lineations, the contact, and along White Chief Creek. One other noteworthy location is an
assemblage of sinks and caves (Cirque, House, Bat Slab) in the southern reaches of the
valley along the marble contact, coinciding with numerous lineations.
80
a)
81
b)
Figure 6.18 Distributional ellipses showing preferred orientation of karst feature clusters in White Chief Valley.
82
Figure 6.19 Density map for the White Chief marble. Density values are given in points per hectare. The highest density is located at the White Chief cave complex.
83
6.6.1 Histogram Correlation
Four streams, numerous lineaments and one fault characterize White Chief
Valley. Distances between features and streams (Figure 6.20) in the White Chief karst
indicate a slight positive correlation. Only 15% of the features within the unit are found
within 25 m of a stream. The majority of features (39%) are located between 50 m and
125 m.
Due to the inherent error of mapping inferred marble contacts, only distances to
marble contacts in the exposed section of the marble (Figure 6.18) were included in this
analysis. Of the 355 mapped features, 36% are located within 5 m of the carbonate/non-
carbonate boundary and an additional 10% lie between 5 m and 10 m. There is a marked
decrease in feature frequency with increasing distance from a contact.
Numerous lineaments are exposed in the White Chief marble outcrop (Figure
XX). The graph indicates that 62% of karst features lie within 25 m of a lineament.
Thereafter, a steep decline occurs with increasing distance, as was previously observed in
Franklin drainage, Monarch drainage, and Timber Gap.
One NE-SW striking fault crosses the mantled marble south of BW2 Four
percent of features lie within 50 m of this fault, and 5% are within 100 m. Feature density
increases 0.25 km to the north of the fault, with the ellipse oriented parallel to the fault
trend, suggesting that the fault may extend as a broader zone or be paralleled by fractures.
In any event, the fault may exert some influence on karst formation.
The mean slope of the White Chief region is 19˚. Karst feature formation is most
common on slopes of between 20° and 25° (29% of features). Seventy seven percent of
84
features occur between 5˚ and 30˚. As seen with the other three regions, few features are
developed on steeper slopes.
Figure 6.20 Histograms showing karst feature distances to streams, marble contacts, lineations, faults, and slope for White Chief Valley.
85
6.6.2 Regression
Regression analysis of the White Chief karst (Figure 6.21) densities against
stream distance exhibits a poor logarithmic fit (Equation 6.4). A slight increase in density
is shown between distances of 50 m and 100 m. Adjusted R2 indicates a non-fit for the
logarithmic model, suggesting relatively little effect of streams on the development of
karst, though the correlation coefficient (0.182) indicates a slight positive correlation. A
significance value of .001 indicates statistical significance at the 95% confidence level.
The equation given for this model is
Y= 1.858 ln(x) + 4.855 (6.4)
where 1.858 is the slope of the curve and 4.855 is the y-intercept. The standard error of
9.941 (Table 6.4) reinforces the high variance in the model in addition to the low
goodness-of-fit coefficient (0.029).
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Figure 6.21 Regression model analyzing stream distance on karst feature densities in White Chief Valley is given above. Table 6.4 Tables of the R, R-squared, error and regression coefficients for White Chief.
87
7. DISCUSSION
This chapter discusses the results in Chapter 6, examining each of the drainages in
turn. Karst feature distributions and clustering is related to the strike of the bedrock,
fracture orientation, the degree of mantling by till or vegetation, the angle of the slope,
and the proximity to streams and bedrock contacts. In addition, this section considers the
role of Pleistocene glaciations in providing a source of meltwater and secondary porosity
(through quarrying and fracture development). The role of climate change is briefly
reviewed. As the solubility of carbon dioxide is inversely related to water temperature,
karst formation may have been greatest during glacial stages, or during periods when
glaciers were in retreat. During glacials, the paths of subglacial meltwater streams may
have been considerably different than those of today’s rivers, possibly accounting for the
location of some features far from modern channels. In the waning stages of glaciation,
waters may not only have been more acidic than today, but water volumes were very
high, and bedrock was freshly exposed, suggesting the potential for enhanced karst
development. Thus, the distribution of karst features within Mineral King is strongly
linked to past events, which will only be fully understood when a cave chronology is
established.
7.1 Franklin Drainage
In the Franklin drainage (Figure 6.6), an analysis of karst distribution indicates
preferential feature formation parallel to sub-parallel to carbonate/non-carbonate
boundaries. The narrow nature of one of the marble units (AF1) appears to influence this
distribution. However, lineaments, such as joints and rock breakages due to mechanical
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weathering, also form approximately parallel to strike, indicating the ellipse’s orientation
may be influenced primarily by geologic structure.
Ellipses within Franklin drainage exhibit differing orientations. Two ellipses (AF1,
CF1) are aligned parallel to strike (NNW-SSE). Upon exiting the CF marble unit, Franklin
Creek bends west as it cascades to the valley floor. This suggests that Franklin Creek may
have formed in an antecedent lineament. The orientation of the CF2 ellipse trends WNW-
ESE, and appears to be controlled largely by the presence of a perennial tributary to
Franklin Creek. The bedrock here lacks significant jointing, limiting karst formation.
Overall, the clustering and feature density in the Franklin drainage appears to be
primarily influenced by stream location. Perennial flow provides a consistent source of
water for dissolution, driving cave development. Karst springs along surface channels
provide additional flow as water exits the karst system: however, their solvent ability is
likely to be lower than that of the streams owing to carbon dioxide expenditure during
conduit flow. Good conditions for karst development on the bare slopes are provided by
moderate slopes and intense jointing and fracturing. During high flow events on Franklin
Creek and its tributaries, lower cave entrances serve as influent points, increasing flow
within the subterranean karst.
The region with the highest density of karst features in Franklin drainage lies in
the area of CF1. The orientation of the creek parallels the strike of Mineral King Valley
and the dominant orientation of lineaments. Continuous stream flow, coupled with
intense jointing, enhance karst genesis in this section. This reach is characterized by high
bedrock walls, confining flows into a canyon-like channel. Upon its emergence from the
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bedrock (at the northwestern edge of the ellipse), the channel is underlain by fine-grained
alluvial material, slowing infiltration and reducing karstification.
CF2 represents a grouping of caves, springs, and sinks that occurs along a tributary
to Franklin Creek near a marble contact. Feature densities are lower than along Franklin
Creek. It is unclear whether such features are absent or merely buried. The marble is
mantled by alluvial deposits, masking potential karst. Additionally, evapotranspiration
by dense vegetation reduces deep percolation.
Two areas of moderate feature density occur in the lower drainage within the AF
unit. The first occurs within the Franklin Creek channel, represented by springs, a stream
sink, and cave entrances. These were all easily identified during the low flow conditions.
The second area is located approximately 0.5 km from Franklin Creek. This area contains
more soil and organic material; however, caves also occur in a small bare outcrop.
Modern annual snow pack runoff and early Quaternary glacial activity may be important
factors in the formation and maintenance of these caves.
Regression is a reliable statistical tool for giving insight into cause-effect
relationships. Each of the four curves (Figures 6.9, 6.13, 6.17, 6.21) comparing distances
from nearest streams to feature density gives varying degrees of positive correlation. Of
the four regions examined, the Franklin karst displays the highest correlation with stream
presence.
Regression of feature density against stream distance in the Franklin drainage
indicates a moderate correlation. Franklin Creek and its tributary to the north have driven
much of the karst development within the drainage, producing numerous caves and
springs. The highest density of features, composed primarily of caves, springs and sinks,
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is located in and around the main channel (Figure 7.1). During early stages of
development, downcutting into the marble may have instigated cave development, and
springs may have formed, draining the subterranean karst water. In recent times,
groundwater lowering erased the buoyant support of the overlying marble, leading to
subsidence, and forming the sinks adjacent to Franklin Creek. Similar explanations can
be applied to karst near CF2 (Figure 6.6), but at a smaller scale.
The varying densities of karst features along streams in the Franklin drainage can
be attributed to differences in marble porosity and ground cover. Differences in densities
of lineaments lead to proportional differences in karst development. Moreover, lower
feature densities along streams may be affected by the presence of scree, till, and
alluvium, which slow infiltration. If karst is present in these locations, post-genetic
infilling has masked their surface visibility.
Glacial activity cannot be ignored as a solvent mechanism. Though there is strong
evidence suggesting stream activity is the primary factor in karst genesis, glaciation has
played a significant role. Surficial karst features are seen near the lower reaches of the
cirque and the likely terminus of the Tioga stage glacier. Meltwater from this Pleistocene
temperate glacier may have initiated karst formation prior to channelization of the
modern Franklin Creek.
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Figure 7.1 The marble outcrop along Franklin Creek west of Franklin Lake. The photo is taken looking east towards Franklin Lake.
7.2 Monarch Drainage
The Monarch drainage has similar topographic and hydrologic characteristics to
Franklin drainage. The drainage sits between two steep glacial ridges. Monarch Creek
drains water from two alpine lakes and flows westward towards the main valley, crossing
marble along its path.
Two major cluster areas occur in the Monarch drainage (Figure 6.10). The first is
in the lower reaches of Monarch Creek (AM), where a number of springs have developed
along the edge of thinly bedded, unmapped marble units. The other cluster, which occurs
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along Monarch Creek in the upper drainage (DM), is also affected by the presence of
Monarch Creek, and the close proximity of the marble contact. Another less intense
cluster in DM is situated away from the stream but along an anticline in the northern
drainage. Its situation below a relative steep slope suggests the influence of runoff
diversion widening fractures resulting from folding.
The distribution of features in unit DM is influenced by fracturing owing to the
anticline crossing the marble. Many features are located along the channel of Monarch
Creek, yet lineaments exert a stronger influence. The position of the ellipse suggests the
marble contact also provides porosity sufficient for the formation of caves and springs.
The Monarch drainage (Figure 7.2) is topographically similar to Franklin, yet
reveals a poor regression model fit with distance to streams. Monarch Creek provides the
necessary solvent for the development of caves and a stream sink in the upper drainage.
Karst is relatively sparse in other parts of the Monarch drainage, therefore lowering
overall density values. Four caves are situated too distant from Monarch Creek to be
influenced by the stream. Two are found along the anticline in the upper reach of the
drainage and the other two are located on the northern ridge of the drainage on the
southeast face of Empire Mountain in CM. The location of these caves suggests the
influence of spring runoff. The distribution of glacial quarrying features indicates that
former glaciers in Monarch drainage likely extended to lower elevations than in Franklin
drainage. Colder conditions promoted higher levels of dissolved carbon dioxide, allowing
the faster dissolution rates observed in alpine karsts (Ford and Williams, 2007). As
glacial melt diminished and warmer conditions prevailed, the cave systems became
hydrologically inactive.
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Figure 7.2 Photo looking east, up the Monarch drainage. The drainage is characterized by benches, indicating glacial quarrying.
7.3 Timber Gap
The Timber Gap marble (Figure 7.3) provides a unique location for karst
development. It crosses three topographic divides and lies in the upper reaches of small,
steep glacial valleys. The middle of the three valleys contains a stream dissecting the
marble. When it crosses the western contact, it is swallowed and reappears at the eastern
contact.
Karst features in Timber Gap occur in three clusters (Figure 6.14), two of which
(AT2 and AT3) lie within small glacial valleys. The ellipses surrounding each group are
highly eccentric and oriented parallel to the strike of the bedrock, the orientation of the
bedrock contact, and lineaments. As such, it is difficult to determine which of these
factors has the strongest influence on feature orientation. The highly elongated nature of
the ellipses in Timber Gap suggests possible influence by the narrow marble exposure:
94
many sinks are found along the contact with less-soluble units. However, the orientation
of the lineaments also seems to play a role.
The two densest feature clusters in the Timber Gap drainage lie in the topographic
lows of cirques and along north-facing slope aspects. Owing to their greater snowpack,
north-facing slopes provide colder conditions, and produce higher amounts of annual
runoff than south-facing slopes. The densest cluster of features at AT2 (Figure 6.15),
which lies toward the middle of the marble unit, appears to result from the influence of
the stream. Though not reflected on the map, fracturing is found to the south of the
stream, where Jordan and Glacier Plug Caves are located. The second densest cluster of
features is located toward the southern tip of the marble (AT3). While the largest sinks are
found in the topographic low, a number of sinks also form on the steeper northern slope.
The dominant fracture pattern on this section of the marble is perpendicular to strike.
Glacial quarrying is assumed to have formed the bench-like fracture pattern, providing
the necessary porosity for karst formation.
Regression analysis indicates a weak linear correlation between karst feature
density and distance to streams. The highest densities occur within the first 50 meters of
the stream, but no karst features are found within the channel. However, under current
conditions, even high flow conditions could not reach cave entrances. Although point
densities positively correlate with stream distance, the stream’s presence is unlikely to
account for karst development. The solvent is probably yearly snowmelt runoff down the
northern slopes.
Much of AT1 (Figure 6.14) is covered by trees, organic material, and soil. Karst
features begin to diminish, either due to mantling or infilling of existing karst.
95
Alternatively, organic cover in alpine settings may not contain enough carbon content to
create an acid of sufficient corrosive strength to develop karst.
Figure 7.3 The Timber Gap marble. This photo was taken from the top of the ridge west of Timber Gap looking north as the marble crosses the upper valley before climbing the next ridge.
7.4 White Chief
The White Chief marble represents the most complex example of karst
development in Mineral King. Intense glaciation has deformed much of the exposed
sections of the marble and buried other areas under deep till deposits. These factors
provided a variety of conditions for karst genesis.
96
Feature distributions in the northern exposure of the White Chief marble are
largely controlled by streams and the unit’s strike. Distributions in the large outcrop
(Figure 6.18) within White Chief Valley are controlled by the position of lineaments and
the carbonate/non-carbonate boundary.
The distribution of AW1 and BW1 (Fig. 6.18a) are primarily influenced by the
presence of Eagle Creek and its tributaries. Features at AW1 are distributed northeast-
southwest, controlled by the presence of the stream channel, which probably contains
water only during high flow conditions. During low flow, water flows into Eagle
Sinkhole and thence to the subterranean system. Higher flows occupy an intermittent
channel north of Eagle Sinkhole. The small outcrop (AW1) is cut by the stream, where a
number of karst features are located. Much like in the Franklin drainage, downcutting
into the jointed marble unit likely initiated karst formation.
The orientation of the group of sinks at BW2 suggests that ancient faulting may
have provided fractures for water infiltration. These inferences cannot be confirmed due
to the 150 m of Tioga stage glacial till that covers this area. Whether these sinks are karst
in nature, and not glacial, is still uncertain. The deep glacial debris cover has made this
determination difficult.
Feature groups CW1 through CW7 (Fig. 6.18b) in White Chief Valley demonstrate
a relationship to the complex joint system and the location of the contact between marble
and granite. Karst features are generally distributed parallel to sub-parallel to strike. The
orientation of CW3 and CW6 are influenced by the contact. Where ellipses appear more
concentric (length:width = ~1), lineaments of differing orientations occur concurrently.
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Thus, a more random distribution pattern is observed for these areas as displayed,
compared to more eccentric ellipses.
Quaternary glaciations appear to have exerted the primary control on karst
development in the White Chief region. While some clusters form adjacent to White
Chief Creek, most are not found near stream channels. This suggests that the corrosive
power of meltwater from glacial ice and snow may be more important in this area. The
densest cluster (CW6 - 48 points per hectare) is located approximately between the two
contacts. Large-scale joints are oriented parallel- to sub-parallel to strike. Smaller
lineaments are also present, but these were not mapped in this study. Porosity due to
fractures, bedding planes, and repetitive frost wedging create an ideal environment for the
development of cave features. Infiltration due to snowmelt, saturated in atmospheric CO2,
drove karstification in this setting.
The majority of karst features in White Chief (Figure 6.18b) are located in the
largest marble band in the upper reaches of White Chief Valley (CW1 – CW7). As the
marble becomes mantled by soil and organic material in White Chief meadow, the
occurrence of karst is confined to sinkholes. A large cluster of sinks (BW2) is located just
north of the fault (Figure 6.18a). Though not directly adjacent to the fault, some
fracturing within the mantled marble facilitates sinkhole formation. Snow melt runoff
sinks into the subterranean system where it joins waters injected from the stream sink in
White Chief meadow. Another lower density cluster (10 points per hectare) forms at AW1
where the marble is exposed, which presumably receives flow in peak runoff times. Upon
reburial, karst features become scarce until karst water resurgence at Tufa Springs. Slopes
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here become steep (45°). Increased runoff velocities and the thick Tioga deposits deflect
runoff from the mantled marble as overland flow toward the valley floor.
As evident from the regression, there is a poor correlation between point density
and stream proximity. The data scatter shows higher densities occurring approximately
100 meters from White Chief Creek, suggesting other variables account for the intense
karst formation.
Numerous springs feed White Chief Creek, which is positioned below the marble
outcrop. During low-flow, the stream, which springs from melting of the “Little Bear”
snowfield high in the granite of the cirque wall, is swallowed by the upper entrance to
Cirque Cave, and begins its journey through the White Chief karst. After flowing through
several cave systems, resurfacing after each cave, the stream emerges from the White
Chief bogaz (collapsed cave) and flows along the valley before disappearing into the
subterranean karst in the meadow. During high flow when the karst system is at
maximum capacity, the overflow follows the otherwise dry channel down to the East
Fork Kaweah River (Despain, 2006).
Although a cause-effect relationship seems logical from the streams journey
through the White Chief karst, genesis may have occurred before the stream was
established in its present position. Karst development may initially have been influenced
by sub-glacial streams. As individual cave systems matured and glaciers retreated, the
stream course became aligned with the karst system. Hence, White Chief Creek’s present
path across the marble is presumably a result, rather than a cause, of karst development.
Repeated Quaternary glaciations probably affected the entire marble unit in White
Chief Valley (Figure 6.18a and b), which strikes parallel to the long axis of the cirque in
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which it lies. Plucking influenced cave development in the vicinity of the White Chief
Cave complex (Figure 7.4) and at the lineament complex containing the entrances to
Cirque and Bat Slab Caves (CW7 in Figure 6.18b). Removal of bedrock may have
increased bedrock porosity, with sub-glacial streams acting as a solvent.
White Chief Valley represents the best argument for sub-glacial karst genesis in
Mineral King. The time frame for this development is uncertain. Cave formation could
have occurred during early Pleistocene glaciations, which suggests this cave system is
relict of a larger system destroyed by recent Quaternary glaciation. White Chief Cave has
numerous entrances, all located within a glacially plucked section of the marble. Early
Quaternary glacial action may also have provided secondary porosity through plucking,
providing a foundation for runoff-induced dissolution. Present dissolution rates
determined by Despain in 2006 for the White Chief marble were 148 mm/kya. While
these dissolution rates may account for the development of the smaller cave systems of
White Chief Valley in the mid- to late-Holocene, they are not adequate to explain the
evolution of the larger caves (White Chief and Cirque). These caves would have required
higher dissolution rates, a function of colder temperatures and higher discharges in the
past. Caution must be exercised when using dissolution rates, as they vary with time and
changing conditions. Moreover, dissolution rates represent both denudation (lowering of
the carbonate surface) and corrosion, and do not account for corrasion and other
mechanical processes.
100
Figure 7.4 The White Chief Cave complex, which represents one example of intense glaciation in the Tioga stage.
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8. CONCLUSIONS
Mineral King Valley, in Sequoia National Park, provides an excellent example of
alpine karst development. The study is significant because much of the existing literature
concerns karst formation in non-alpine locations. The Mineral King karst system was
studied by locating surficial features in the field and on aerial photographs and relating
them to surface geology, topography, and hydrology. A predictive model of alpine karst
formation was used to analyze the relationship between these variables. The model
included both the availability of water for dissolution (principally streams) and the
presence of subsurface entry points (fractures). The results were complex, suggesting that
additional factors such as snowmelt entry on slopes, water chemistry variations, or the
effects of past glaciation may have played an important role in karst development.
In addition to trying to develop a general model for karst location, the study
sought to answer five questions:
1. What is the extent of karst in Mineral King Valley?
Surface karst features within Mineral King are located within marble
units. Each of the four karst regions identified in this study, Franklin drainage,
Monarch drainage, Timber Gap, and White Chief, are set within the drainages
of glacial valleys. The nature and extent of karst features in Mineral King
could not be fully ascertained, as much of the marble is buried beneath, or
partially mantled by, Quaternary till. The burial and mantling of marble has
profound effects on the accuracy of the analysis.
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Despite the limitations imposed by the poor exposures of marble, this
study was able to uncover numerous features that had not previously been
mapped. A total of 133 caves, 12 swallets, 70 springs, 12 sinking streams,
and 386 sinks were mapped during fieldwork (Appendix B). In general, it can
be said that karst features are relatively common in the marble units of
Mineral King, particularly on north-facing slopes with moderate slope angles
(15˚ to 30˚), adjacent to streams, and near lithologic and geologic structure.
Karst features are less common on south-facing slopes and higher slope angles
(>30˚).
The chemistry of the water probably plays a role in karst formation,
although this aspect of karst formation was not examined in this study. In
some areas of Mineral King, the marble is exposed directly to the atmosphere,
whereas in others, biologic material (meadows or forests) cover the marble.
Biological material affects soil and water chemistry, potentially increasing
rates of marble solution. Contrary to expectations, marble units that were
covered in a substantial layer of alluvium, till, or organic mass, appeared to
lack significant karst development. The reasons for this are unclear. One
possibility may be that pre-existing karsts could simply have been buried
beneath a substrate and the apparent absence of karst features is not real.
2. What distribution patterns are exhibited by alpine karst in Mineral King
Valley?
103
The distribution pattern of karst in Mineral King is shown by mapped
ellipses (Chapter 6). Ellipse analysis shows that karst features commonly
occur as linear clusters (Timber Gap, Franklin, Monarch), although more
equant distributions are also observed (White Chief). The ellipse shapes are
not entirely independent, however, as their elongate shape appears to be
influenced by the linear nature of marble exposures. As such, it is sometimes
difficult to determine the degree to which fracturing, folding, and faulting
influence these distribution patterns. Viewed at a small scale, lineations
appear to play an important role as entry points for water, and to influence the
linear arrangement of exposed karst features. Additionally, the boundary
between the marble and non-carbonate rock unit appear to provide sufficient
porosity for karst formation and therefore influence distributions parallel to
strike.
Distributions within the White Chief marble tend to follow a more
random pattern. One possible explanation for this phenomenon is the complex
fracturing resulting from jointing, glaciation, and freeze-thaw action. As a
result, a multitude of access points are created for runoff to enter, influencing
the uneven distributions observed.
3. Does the occurrence of karst features coincide with the presence of
structural and lithologic lineaments?
Distributional analysis indicates that most karst features are
aligned approximately parallel to the lithologic strike. This trend coincides
104
with the orientation of lithologic lineaments and where applicable, the
presence of folds. In turn, the orientation of streams is also influenced by
structure, such that features may be aligned parallel to both lineations and
water courses.
Not all karst features are oriented parallel to structure, however.
Where this is the case, the most common control appears to be the
orientation of stream channels. The influence of faults is less clear in this
study. For example, the fault located south of BW2 may play a role in the
cluster of karst features that lie to its north, but the relationship remains
unclear.
4. Does hydrography have an effect on the formation of karst?
There are four potential sources of water for alpine karst dissoluton: (1)
modern streamflow, (2) direct precipitation on slopes, (3) snowmelt runoff on
slopes, and (4) Pleistocene glacial runoff/snowmelt. Of these, only modern
streamflow was directly examined in this thesis.
The role of streamflow in karst formation is unequal in the drainages
studied. Present stream locations appear to play a significant role in the
karstification of the Franklin drainage, and only minor roles in the Monarch
and Timber Gap drainages. Though the distinct possibility exists that the
White Chief karst was influenced by White Chief Creek, it appears that
Quaternary glaciations may have played a more important role. Runoff from
the temperate glaciers that once occupied the four karst regions may have
105
instigated karst development in the Pleistocene. The further exploration of this
hypothesis awaits dating of karst features.
At present, annual snowmelt runoff is probably an important solvent.
Within the drainages, karst features are largely located where runoff
convergences in topographic lows, whether or not a stream is present. On
slopes, karst development appears to be more pronounced on shady north-
facing slopes, particularly in the Timber Gap drainage. This suggests that the
role of snowmelt merits further investigation.
5. Does slope influence karst formation?
In the Mineral King region, most marble outcrops are located on slopes
ranging from 0° - 45°. An analysis of karst formation and slope angles suggests
that features are preferentially formed where slopes range from 20° to 27°. This
range is extended somewhat in the Monarch drainage, where the range is between
10° and 30°. Moderate slopes appear to provide the best conditions for snowmelt
entry and subsurface groundwater pressure gradients that help propagate
subterranean conduits.
Although a few features occur on slopes lower than 15°, a steep drop-off
in frequency occurs on gradients greater than 30°. As a result, steep slopes (>30°)
on the west wall of Mineral King Valley have only a few minor sinks. High slope
angles increase runoff velocity and lower infiltration rates.
In some areas, such as Empire Mountain, the Timber Gap outcrop
climbing the ridges between valleys, and a portion of the Franklin Lake marble
106
along the high north ridge, the slopes exceed 45°. Karst in these areas is limited to
small karrens, such as rills. Modern runoff appears minimal and yearly snowfall
accumulations are probably low owing to wind erosion and avalanching. Summer
runoff from thunderstorms runs off quickly and therefore has little opportunity to
infiltrate the slopes.
Limitations and Future Work
Many challenges were faced during this project. While the preliminary predictive
model provided a good general guideline for locating karst features, actual locations
differed significantly owing to the complex nature of glaciated karst terrains. The
numerous small fractures resulting from glacial activity and jointing could not be feasibly
mapped in this study. Furthermore, it was not possible to measure annual runoff and
precipitation. A complete understanding of alpine karst development will require further
assessment of Quaternary glacial influences.
Burial of the marble under glacial debris may have masked the visibility of
additional karst features. Accuracy of the analysis in this study may be affected by the
mantling. This is most important in the White Chief marble, which is mantled for
approximately 2.5 km of its extent.
There are several other limitations to this study. The short season of field mapping
prevented more detailed morphological information from being collected, such as
sinkhole and cave dimensions. Slope angles were calculated from Digital Elevation
Models with 10-m ground resolution, limiting their accuracy for analysis: additional time
would have allowed the use of field clinometers. Long term monitoring of available CO2
and dissolved calcium and magnesium in stream channel runoff may also improve the
107
model. Combining annual runoff data with calculated denudation rates would improve an
understanding of the role of modern stream flow.
Future work might consider the impact of global warming on karst activity in the
Sierra Nevada. The effects of current climate change require monitoring changes in
precipitation and carbon dioxide levels. Rising carbon dioxide levels will increase the
acidity of water in high altitude rainfall and snowmelt. Initially, this could increase
denudation and corrosion rates. However, higher temperatures may decrease annual
snowfall, and increase stream temperatures, limiting their ability to dissolve CO2.
Furthermore, sinkhole development could occur as annual water budgets in the karst
system decrease, dropping the buoyant support of the subterranean conduits.
Future work needs to address karst modeling in other alpine settings, in order to
validate the results obtained in this study. Although the geologic, hydrographic, and
topographic conditions of the Mineral King area cannot be replicated, future studies in
glaciated high-altitude locations can provide further insight into karst genesis in these
unique settings. Work of this nature is fundamental in developing an understanding of
each individual karst system, and provides the foundation for more detailed analyses.
108
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APPENDIX A
Daily fieldw k synopsis
July 12, 2007
or
This field day started mapping the upper extent of the White Chief marble. The
primary researcher was joined by Joel Despain, Cave Management Specialist for Sequoia
and Kings Canyon National Parks. A Garmin GPSMap 60 CSx and Garmin Vista C were
used for mapping. The day concluded at the lower extent of Cirque Cave.
July 27, 2007
Two research assistants accompanied the author on this trip. Approximately 1 km
of the southern extent of marble and karst features just west of Timber Gap were mapped.
Three Garmin GPSMap 60CSx units were used. Mapping terminated at the top of the
first ridge north of Timber Gap due to a steep incline and prospects of inclement weather.
August 16, 2007
Two research assistants accompanied the primary researcher on this trip. Field
mapping resumed in White Chief Valley at the terminal point from July 12. Two Garmin
GPSMap 60CSx units and a Garmin Vista C unit were used. Fieldwork ended in the
meadow where the marble becomes mantled.
August 17, 2007
Ben Tobin, Cave Technician in Sequoia and Kings Canyon National Park, joined
the three field researchers and mapping was resumed in White Chief Valley.
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Malfunctioning in one Garmin GPSMap 60CSx from the previous day warranted re-
mapping of the area assigned to the respective field assistant. A Garmin GPS III was put
into commission in place of the malfunctioning unit (The third unit had been shipped
back to Garmin due to faulty buttons). The mantled marble was then followed north
toward Eagle Creek and the day ended at Tufa Springs where the marble ends.
September 1, 2007
Four research assistants joined the author on this weekend trip, including Ben
Tobin. A large marble unit laying in the lower elevations of the Franklin drainage and the
marble units surrounding Lower Franklin Lake to the west were mapped. Three Garmin
GPSMap 60 CSx and two Garmin GPS III units were used. The day’s work concluded at
the lake’s western terminus.
September 2, 2007
Mapping was continued near Lower Franklin Lake. Focus was directed towards
marble units and interbeds north of the lake between the low ridge bordering the north
shore of the lake and the higher ridge to its north. The same GPS units were used as the
previous day.
September 22, 2007
One field assistant accompanied the author. Mapping resumed just below the
termination point on July 27. Poor visibility challenged the mapping process. Both
researchers used Garmin GPSMap 60 CSx units for mapping and navigation. The steep
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pitch on the north side of the ridge was climbed within reasonable limits. Unreachable
steeper areas did not appear to yield any karst. Future visits may be necessary. Mapping
was continued north until the termination of the marble unit, south of the confluence of
Timber Gap and Cliff Creeks.
September 29, 2007
Ben Tobin joined the author for fieldwork in the Monarch drainage. Garmin
GPSMap 60 CSx units were used. Marble and karst was mapped starting at the lower
extent of Monarch Creek and continued upstream. Three separate marble units and
several interbeds were mapped, extending to just below Lower Monarch Lake.
September 30, 2007
The author proceeded mapping alone with a Garmin GPSMap 60 CSx.
Questionable spots from previous exploration around Lower Franklin Lake were
examined and mapped in addition to one extent of marble south of the lake.
October 14, 2007
The final field day was undertaken alone by the author using a Garmin GPSMap
60 CSx. The first part of the day included re-examining the southern tip of the Timber
Gap marble, where several new karst features were located. The second part focused on
mapping the karst and marble on Empire Mountain.
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APPENDIX B
Mineral King Karst Maps (by sub-region) Five maps were created that depict the marble and karst features mapped for each
of the sub-regions in this study. Caves are represented by triangles, and sinks, sinking
streams, springs, and swallets are symbolized by circles. Major cave systems have been
labeled. Streams (perennial and intermittent), and geologic features such as faults, folds,
and lineaments were also included. The background is a digitized version of the USGS
7.5’ Mineral King quadrangle.
Disclaimer:
The caves and karst of Mineral King Valley in Sequoia National Park are
precious, irreplaceable natural resources protected by the National Park Service. The
disclosure of their locations is intended for an academic audience and must be treated in
a sensitive manner. Furthermore, due to the rugged, alpine nature of the area, many cave
and karst locations are difficult to reach and require advanced navigation skills and
backcountry experience. Therefore, searching for these magnificent formations is
unadvisable.
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Figure B.1 Karst feature map of the Franklin Creek drainage.
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Figure B.2 Karst feature map of the Monarch Creek drainage.
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Figure B.3 Karst feature map of Timber Gap.
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Figure B.4 Karst feature map of the northern extent of White Chief Valley.
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Figure B.5 Karst feature map of the southern extent of White Chief Valley.
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