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

ii

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.

iii

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.

iv

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

v

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

vi

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

vii

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

viii

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

ix

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

x

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

xi

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.

xii

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

1

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

2

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.

3

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

4

parallel to sub-parallel to the strike of the valley; 3) karst features would be found

proximal to surface streams.

5

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.

6

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

7

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.

8

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.

9

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).

10

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

11

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.

12

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

13

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

14

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.

15

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

16

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,

17

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).

86

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

88

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

89

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,

90

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.

91

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

92

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.

93

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.

97

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

98

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

99

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.

101

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.

102

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

WORKS CITED

Addison, A. 2003. ArcPAD GIS Mobile Software in Cueva Del Tecolote, Tamaulipas, Mexico. Abstract In The Program of the 2003 NSS Convention, 8-29.

Al-halal, A.B., Al-awadhi, J.M. 2006. Assessment of sand encroachment Kuwait using

GIS. Environmental Geology 49: 960-967. Angel, J.C., Nelson, D.O., Panno, S. 2004. Comparison of a New GIS-Based Technique

and a Manual Method for Determining Sinkhole Density: An Example from Illinois; Sinkhole Plain. Journal of Cave and Karst Studies 66 (1): 9-17.

Applegate, P. 2003. Detection of Sinkholes Developed on Shaly Ordivician Limestones,

Hamilton County, Ohio, Using Digital Topographic Data: Dependance of Topographic Expression of Sinkholes on Scale, Contour Interval, and Slope. Journal of Cave and Karst Studies 65 (2): 126-129.

ArcGIS 9.2. Environmental Systems Research Institute (ESRI). ESRI Support Center. Environmental Systems Research Institute (ESRI).

http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=welcome. Accessed May 30, 2006.

Arrell, K. 2002. Is GIS a useful tool for geomorphologists? Journal of the Durham

University Geographical Society 2002. Bocco, G., Mendoza, M., Velazquez, A. 2001. Remote Sensing and GIS-based and

regional geomorphic mapping- a tool for land use planning in developing countries. Geomorphology 39: 211-219.

Black, J.P. 1994. The hydrogeochemistry of the Mineral King area, Sequoia National

Park, California. Master’s Thesis, California State University, Fresno. Busby-Spera, C.J. Saleeby, J. 1987. Geologic Guide to the Mineral King Area, Sequoia

National Park, California. Society of Economic Paleontologists and Mineralogists, Pacific Section, 44 p.

Christensen, M.N. 1959. Geologic structure of the Mineral King area, California. PhD

diss, University of California, Berkely, Connair, D.P., Murray, B.S. 2002. Karst groundwater basin delineation, Fort Knox,

Kentucky. Engineering Geology 65: 125-131. Despain, Joel. 2007. Cave Management Specialist, Sequoia/Kings Canyon National

Parks.

109

Despain, J, Fryer, S. 2002. Hurricane Crawl Cave: A GIS-Based Cave Management Plan Analysis and Review. Journal of Cave and Karst Studies 64 (1): 71-76.

Despain, J., Stock, G. 2005. Geomorphic History of Crystal Cave, Southern Sierra

Nevada, California. Journal of Cave and Karst Studies 67 (2): 92-102. Despain, J. 2006. Hydrochemistry in an Alpine Karst System, Sequoia and Kings Canyon

National Parks, California. Master’s Thesis: Western Kentucky University. Dexter, L.R., Cluer, B.L. 1999. Cyclic Erosional Instability of Sandbars along the

Colorado River, Grand Canyon, Arizona. Annals of the Association of American Geographers 89 (2): 238-266.

Dinh, N.Q., Hung, L.Q., Batelaan, O., Tam, V.T., Lagrou, D. 2002. Remote Sensing and

GIS-based Analysis of Cave Development in the Suoimuoi Catchment (Son La – NW Vietnam). Journal of Cave and Karst Studies (64) 1: 23-33.

Doerfliger, N., Jeannin, P.Y., Zwahlen, F. 1999. Water vulnerability assessment in karst

environments: a new method of defining protection areas using multi-attribute approach and GIS tools (EPIK method). Environmental Geology 39 (2): 165-177.

Felzer, R., Winnett, T., ed. 1972. Mineral King. Berkely: Wilderness Press. Florea, L., Paylor, R.L., Simpson, L., Gulley, J. 2002. Karst Advances in Kentucky.

Journal of Cave and Karst Studies 64 (1): 58-62. Florea, L. 2005. Using State-Wide GIS Data to Identify the Coincidence Between

Sinkholes and Geologic Structure. Journal of Cave and Karst Studies 67 (2): 120- 124.

Ford, D., Williams, P. 2007. Karst Hydrogeology and Geomorphology. England: John

Wiley & Sons, Ltd. Forth, R.A., Butcher, D., Senior, R. 1999. Hazard mapping of karst along the coast of the

Algarve, Portugal. Engineering Geology 52: 67-74. Gao, Y., Alexander Jr., E.C., Tipping, R.C. 2002. The Development of a Karst Feature

Database for Southeastern Minnesota. Journal of Cave and Karst Studies 64 (1): 51-57.

Gao, Y., Alexander Jr., E.C., Tipping, R.C. 2005a. Karst database development in

Minnesota: design and data assembly. Environmental Geology 47: 1072-1082. Gao, Y., Alexander Jr., E.C., Tipping, R.C. 2005b. Karst database development in

Minnesota: analysis of sinkhole distribution. Environmental Geology 47: 1083-1098.

110

Glennon, A., Goodchild, M. 2004. A GIS Flow Data Model. National Center for Geographic Information and Analysis, University of California, Santa Barbara, 15 p.

Glennon, A., Groves, C. 2002. An Examination of Perennial Stream Drainage Patterns

Within The Mammoth Cave Watershed, Kentucky. Journal of Cave and Karst Studies 64 (1): 82-91.

Granger, D.E., Stock, G.M. 2004. Using cave sediments as geologic tiltmeters:

Applications to postglacial rebound of the Sierra Nevada, California. Geophysical Research Letters (31): 1-4.

Green, J.A., Marken, W.J., Alexander Jr., E.C., Alexander, S.C. 2001. Karst unit

mapping using geographic information system technology, Mower County, Minnesota, USA. Environmental Geology 42: 457-461.

Gross, M.R., Ghosh, K., Manda, A.K., Whitman, Dean. 2004. A GIS-Based Spatial

Analysis of Caves and Solution Cavities- Application to Predicting Cave Occurrence in Limestone Terrain In Studies in Military Geography and Geology, ed. Caldwell, D.R., Ehlen, J., Harmon, R.S., 287-306. Netherlands: Kluwer Academic Publishers.

Hallman, C.L. 1997. Prediction of potential groundwater pollution sites in a karst

area utilizing DRASTIC, DRASTIC modifications and GIS. Unpublished Master’s Thesis, Murray State University.

Haryono, E., Day, M. 2004. Landform Differentiation Within the Gunng Kidul

Kegelkarst, Java, Indonesia. Journal of Cave and Karst Studies 66 (2): 62-69. Hayes, Gerry. 1997. “Glacial Chronology of the Sierra Nevada” in Glaciation of the

Sierra Nevada, Modesto Junior College, Department of Geology. http://virtual.yosemite.cc.ca.us/ghayes/sierragla.htm. Accessed April 2, 2008.

ill, M. 2006. Geology of the Sierra Nevada. Berkely: University of California Press.

Horrocr Wind Cave, South Dakota. Journal of Cave

and Karst Studies 64 (1): 63-70.

Hugget Fundamentals of Physical Geography, London and New York: Routledge.

Iliffe, Trvation in Bermuda. Abstract. In The Program of the 2003 NSS Convention,

H

ks, R.D., Szukalski, B.W. 2002. Using Geographic Information Systems to Develop a Cave Potential Map fo

t, R.J. 2003. Fundamentals of Geomorphology. Routledge

.M., 2003. The BeCKIS Project- Establishing a GIS for Cave and Karst Conse8-30.

111

Jackson, Louise. 1988. Beulah: A Biography of Mineral King. Tucson: Westernlore Publications.

Jasper, rcPad Software. Abstract In The Program of the 2003 NSS Convention, 8-31.

Khalsals. IEEE

Transactions on Geoscience and Remote Sensing 42 (10): 2177-2182.

Kerski, aphic Information Systems. National Speleological Society News, April 2004.

Lyew-Ain the study of cockpit karst. Earth Surface Processes

and Landforms 10: 1-15.

McConGeomorphology, edited by R.J. Chorley, 111-133. London: Methuen and Co, Ltd.

McKen s on to GIS. Abstract In The Program of the

2003 NSS Convention, 8-31 - 8-32.

ineral King, CA. 1988. USGS 7.5’ Topographic Map.

Mineral King Webcam & Weather Station. www.mk-webcam.net

J. 2003. Inventorying Cave Features Using ESRI A

, S.R.S., Dyergerov, M.B., Khromova, T., Raup, B.H., Barry, R.G. 2004. Space_Based Mapping of Glacier Changes Using ASTER and GIS Too

J.J. 2004. Analyzing the Earth with Geogr

yee, P., Viles, H.A., Tucker, G.E. 2007. The use of GIS-based digital

morphometric techniques

nell, H., Horn, J.M. 1972. Probabilities of surface karst In Spatial Analysis in

zie, D., Veni, G. 2003. Walls 2D: Realistic Drawing and Morphing of Cave Walland Passage Details and its Applicati

M

. Accessed October 29, 2007.

Nationahttp://www.nps.gov/gis/data_info/

l Park Service Geographic Information Systems and Data.

. Accessed February 2, 2007.

Orndor arks of

South-Central Missouri, USA. Acta Carsologica 29/2 (11): 161-175.

Palmer ogy of limestone caves. Geological Society of America Bulletin 103 (1): 1-21.

Parsons ample From Mineral King, CA. Environmental Management 5 (4): 335-

340.

Peters, A. 1971. Mineral King Valley. Master’s Thesis: San Jose State University.

ff, R.C, Weary, D.J., LaGueux, K.M. 2000. Geographic Information Systems Analysis of Geologic Controls on the Distribution of Dolines in the Oz

, A.N., 1991. Origin and morphol

, D.J., Stohlgren, T.J., Fodor, P.A. 1981. Establishing Backcounty Use Quotas:An Ex

112

Phelan, Subsurface Conceptual Model. Journal of Cave and Karst Studies 64 (1):

77-81.

Reinfel am trends ecific steam power: a GIS approach.

Geomorphology 60: 403-416.

Reese, n of Mapped Karst Features in Pennsylvania. Pennsylvania Geological Survey.

tock, Greg. 2007. Geologist, Yosemite National Park.

Stock, ra a, revealed by cosmogenic dating of cave sediments. Geology

(32) 3: 193-196.

Staffor opment

Carbonate Island Karst Model. Journal of Cave and Karst Studies 67 (1): 14-27.

Szukals roduction to Cave and Karst GIS. Journal of Cave and Karst Studies 64 (1): 3.

Taylor,

Regional GIS-Based Approach. USGS Publication 5160 part 3Ba, 103-107.

Tinsley, J, and Schultz, L. 1999. New Karst Connection in Mineral King Valley. California Caver, 210, 22.

Terry, J f Island in Sub-Tropical Japan. Journal of Cave and Karst Studies 67 (1): 48-54.

obin, Benjamin. 2007. Cave Technician, Sequoia/Kings Canyon National Park.

Tobin,

ey, le 1:7,500,000. U.S.

Geological Survey Open-File Report 2004-1352.

Toepke, K. 2006. GIS Analysis of Caves and Karst of the Marianna Islands. Master’s Thesis: Mississippi State University.

T.L., 2002. Public Datasets Integrated with GIS and 3-D Visualization Help Expand

ds, I., Cohen, T., Batten, P., Brierly, G. 2004. Assessment of downstrein channel gradient, total and sp

S.O. and Kochanov, W.E. 2003. Digital Karst Density Layer and Compilatio

S

G.M., Anderson, R.S., Finkel, R.C. 2004. Pace of landscape evolution in the SierNevada, Californi

d, K., Mylroie, J., Taborosi, D., Jenson, J., Mylroie, J. 2005. Karst Develon Tinian, Commonwealth of the North Marianna Islands: Controls on Dissolution in Relation to the

ki, B. W. 2002. Int

C.J., Nelson, Jr., H.L., Hileman, G., Kaiser, W.P. 2005. Hydrogeologic- Framework Mapping of Shallow, Conduit-Dominated Karst: Components of a

. 2005. Karst Distribution and Controls on Yoron-Jima, An Emerged Ree

T

B.T., Weary, D.J. 2004. Digital Engineering Aspects of Karst Map: A GIS Version of Davies, W.E., Simpson, J.H., Ohlmacher, G.C., Kirk, W.S., and Newton, E.G., 1984, Engineering Aspects of Karst: U.S. Geological SurvNational Atlas of the United States of America, sca

113

Townse ated GIS with radar and optical remote sensing. Geomorphology 21: 295-312.

Veni, G. 2002. Revising the Karst Map of the United States. Journal of Cave and Karst Studies 64 (1): 45-50.

Vitek, J ourney puter mapping to GIS and virtual reality.

eomorphology 16: 233-249

Walsh, rocess : a remote sensing and GIS perspective.

Geomorphology 21: 183-205.

Wilkin ld Sand Dunes, Kane County, Utah, USA.

Geomorphology 83: 48-57.

Williamomorphology, edited by R.J. Chorley, 135-163. London: Methuen

and Co, Ltd.

nd, P.A., Walsh, S.J. 1998. Modeling floodplain inundation using an integr

.D., Giardino, J.R., Fitzgerald, J.W. 1996. Mapping geomorphology: A jfrom paper maps, through comG S.J., Butler, D.R., Malanson, G.P. 1998. An overview of scale, pattern, prelationships in geomorphology

s, D.E., Ford, R.L. 2007. Nearest neighbor methods applied to dune fieorganization: The Coral Pink

s, P.W. 1972. The analysis of spatial characteristics of karst terrains In Spatial

Analysis in Ge

114

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.

115

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

116

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.

117

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.

118

Figure B.1 Karst feature map of the Franklin Creek drainage.

119

Figure B.2 Karst feature map of the Monarch Creek drainage.

120

Figure B.3 Karst feature map of Timber Gap.

121

Figure B.4 Karst feature map of the northern extent of White Chief Valley.

122

Figure B.5 Karst feature map of the southern extent of White Chief Valley.

123