EVALUATION OF LODGEPOLE PINE TREE REMOVAL ON THE STORAGE
POTENTIAL OF A SHALLOW AQUIFER IN A SIERRA NEVADA MOUNTAIN
MEADOW
Matthew William Lesh
B.S., Oregon State University, 2001
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
GEOLOGY
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SUMMER
2010
EVALUATION OF LODGEPOLE PINE TREE REMOVAL ON THE STORAGE
POTENTIAL OF A SHALLOW AQUIFER IN A SIERRA NEVADA MOUNTAIN
MEADOW
A Thesis
by
Matthew William Lesh
Approved by:
__________________________________, Committee Chair
Kevin C. Cornwell, Ph.D.
__________________________________, Second Reader
David G. Evans, Ph.D.
__________________________________, Third Reader
Thomas E. Koler, Ph.D.
____________________________
Date
ii
Student: Matthew William Lesh
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
______________________, Department Chair
David G. Evans, Ph.D.
Department of Geology
iii
___________________
Date
Abstract
of
EVALUATION OF LODGEPOLE PINE TREE REMOVAL ON THE STORAGE
POTENTIAL OF A SHALLOW AQUIFER IN A SIERRA NEVADA MOUNTAIN
MEADOW
by
Matthew William Lesh
This study evaluates the physical characteristics and predicted hydrologic function in
response to the removal of Pinus contorta – var. latfolia (commonly referred to as
lodgepole pine trees) in Timothy Meadow located in the northern Sierra Nevada,
Eldorado County, California. Forest Service managers in the Eldorado National Forest
are presently considering the removal of lodgepole pine trees that have encroached on the
meadow in an effort to increase the amount of groundwater storage in meadow
sediments. Previous work in vegetation management as it relates to groundwater storage
has been conducted in other parts of the country with differing results following tree
harvesting. The results of these studies appear to indicate that the effectiveness of
vegetation removal correlates to the hydrologic functionality of the meadow or wetlands
prior to removal. Timothy Meadow offered a unique opportunity to study the predicted
response of a hydrologically functional meadow to simulated removal of lodgepole pine
trees. The physical characteristics of Timothy Meadow that were measured include:
surface area, sediment thickness, specific yield and permeability of subsurface materials,
and potential water storage volume. A groundwater flow model was constructed to
predict the change in water table elevations to the removal of lodgepole pine transpiration
from the groundwater budget. Results suggest that subsurface storage increases
significantly in response to tree removal during the summer months when transpiration,
should the trees be left in place, would be the greatest. This study addresses questions
that have broad implications for vegetation management as it relates to water resources as
much of California receives water from Sierra high elevation watersheds. The results of
this study will be used to inform Forest Service management throughout Northern
California as to the effectiveness of vegetation removal as a remedial alternative.
_______________________, Committee Chair
Kevin C. Cornwell, Ph.D.
_______________________
Date
iv
DEDICATION
This thesis is dedicated to my wife Leslie and my newly born son Isaac. Thank you for
all of your support these past few years as I have worked through this project, I could not
have done it without you and I am so fortunate to have you both in my life.
v
ACKNOWLEDGMENTS
I would like to thank the following individuals for their technical expertise, support, and
mentorship during my time at California State University, Sacramento:

Dr. Kevin Cornwell, CSUS

Dr. David Evans, CSUS

Dr. Tom Koler, USFS

Dr. Diane Carlson, CSUS

Dr. Tim Horner, CSUS

Kent Parrish, URS Corporation

Kevin Ellett, USGS

Steve Poletski, CSUS

Rich Redd, CSUS

Geocon Consultants, Rancho Cordova, CA

Leighton Consulting, San Diego, CA
vi
TABLE OF CONTENTS
Page
Dedication ........................................................................................................................... v
Acknowledgements ........................................................................................................... vi
List of Tables .................................................................................................................... ix
List of Figures ................................................................................................................... xi
Chapter
1.
2.
3.
INTRODUCTION ....................................................................................................... 1
1.1
Objectives ......................................................................................................... 1
1.2
Background ....................................................................................................... 2
1.3
Development of Project .................................................................................... 6
1.4
Summary of Approach ...................................................................................... 6
1.5
Tables and Figures ............................................................................................ 8
STUDY AREA DESCRIPTION AND GEOLOGIC/HYDROLOGIC SETTING ... 11
2.1
Introduction ..................................................................................................... 11
2.2
Study Area Description ................................................................................... 11
2.3
Geologic Setting.............................................................................................. 13
2.4
Geologic History ............................................................................................. 15
2.5
Hydrologic Setting .......................................................................................... 19
2.6
Tables and Figures .......................................................................................... 24
PHYSICAL CHARACTERIZATION ...................................................................... 38
3.1
Introduction ..................................................................................................... 38
3.2
Methods........................................................................................................... 38
3.3
Results ............................................................................................................. 44
3.4
Discussion ....................................................................................................... 48
3.5
Tables and Figures .......................................................................................... 51
vii
4.
5.
6.
7.
HYDROLOGIC CHARACTERIZATION ............................................................... 66
4.1
Introduction ..................................................................................................... 66
4.2
Methods........................................................................................................... 66
4.3
Results ............................................................................................................. 77
4.4
Discussion ....................................................................................................... 83
4.5
Tables and Figures .......................................................................................... 88
TRANSPIRATION ESTIMATES OF LODGEPOLE PINE .................................. 117
5.1
Introduction ................................................................................................... 117
5.2
Methods......................................................................................................... 118
5.3
Results ........................................................................................................... 126
5.4
Discussion ..................................................................................................... 129
5.5
Tables and Figures ........................................................................................ 132
SIMULATIONS OF TREE REMOVAL ................................................................ 142
6.1
Introduction ................................................................................................... 142
6.2
Methods......................................................................................................... 142
6.3
Results ........................................................................................................... 148
6.4
Discussion ..................................................................................................... 151
6.5
Tables and Figures ........................................................................................ 153
ANALYSIS AND CONCLUSIONS ....................................................................... 162
7.1
Summary of Results ...................................................................................... 162
7.2
Analysis......................................................................................................... 164
7.3
Conclusions ................................................................................................... 166
Appendix A: Seismic Survey Reduction Worksheets .................................................. 169
Appendix B: Boring Logs ............................................................................................. 178
Appendix C: Grain Size Distribution Plots................................................................... 187
Appendix D: Daily Maximum and Minimum Temperature and Relative Humidity
Data ......................................................................................................... 191
References ....................................................................................................................... 198
viii
LIST OF TABLES
Page
Table 2-1:
Summary of weather stations used to approximate climatic variations in
the meadow for the duration of the study ................................................. 24
Table 3-1:
Summary of seismic surveys performed in Timothy Meadow ................. 51
Table 3-2:
Summary of depths to bedrock obtained from seismic surveys ............... 52
Table 3-3:
Summary of depths to bedrock obtained from hand auger borings and
piezometer installations ............................................................................ 53
Table 4-1:
Summary of piezometer construction details and elevations .................... 88
Table 4-2:
Summary of depth to water measurements based on manual electronic and
recorded transducer readings .................................................................... 89
Table 4-3:
Summary of grain size analysis data and error analysis ........................... 90
Table 4-4:
Percent finer by weight results based on grain size analysis .................... 92
Table 4-5:
Grain size classification as defined by ASTM D 2488-06 ....................... 93
Table 4-6:
D50 values obtained from grain size analysis results and ASTM
classification of soil type .......................................................................... 94
Table 4-7:
Classification of soil samples based on the UDSA Textural Triangle...... 95
Table 4-8:
Summary of hydraulic conductivity (K) values of the meadow
sediments................................................................................................... 96
Table 4-9:
Results of specific yield laboratory analysis ............................................. 97
Table 4-10:
Soil moisture retention curve parameters obtained from the USDA Rosetta
Lite v. 1.1 Database .................................................................................. 99
Table 4-11:
Calculated specific yield values based on soil moisture retention
characteristics of the meadow sediments ................................................ 100
Table 4-12:
Summary of specific yield values of the meadow sediments ................. 101
ix
Table 5-1:
Summary of Leaf Area Index (LAI) values obtained from the Aqua
satellite operated by NASA for the meadow vicinity ............................. 132
Table 5-2:
Summary of average daily minimum and maximum temperature and
relative humidity for June to September 2009 ........................................ 133
Table 5-3:
Summary of calculated daily transpiration rates for June to September
2009......................................................................................................... 134
Table 5-4:
Summary of the estimated error due to utilizing average values of
temperature in transpiration calculations ................................................ 135
Table 5-5:
Summary of the estimated error due to utilizing average values of relative
humidity in transpiration calculations ..................................................... 136
Table 6-1:
Summary of initial calibration data for the steady-state groundwater flow
model....................................................................................................... 153
Table 6-2:
Summary of the sensitivity of recharge due to variations in hydraulic
conductivity (K) ...................................................................................... 154
Table 6-3:
Summary of sensitivity analysis of specific yield (Sy) ........................... 155
Table 6-4:
Summary of model results of tree removal simulations for the steady-state
groundwater flow model ......................................................................... 156
Table D-1:
Summary of daily maximum, minimum, and range of temperatures
recorded at the Van Vleck weather station ............................................. 192
Table D-2:
Summary of daily maximum, minimum, and range of relative humidity
recorded at the Tahoe Vista weather station ........................................... 195
x
LIST OF FIGURES
Page
Figure 1-1:
Conceptual diagram of the typical characteristics of degraded mountain
meadows ..................................................................................................... 8
Figure 1-2:
Example of the pond and plug method of meadow restoration .................. 9
Figure 1-3:
Photo of tree encroachment in a mountain meadow located in Yosemite
National Park, California .......................................................................... 10
Figure 2-1:
Location map of the Timothy Meadow study area ................................... 25
Figure 2-2:
Aerial photo of the meadow perimeter ..................................................... 26
Figure 2-3:
Photo of the meadow showing clusters or islands of encroaching
lodgepole pine trees .................................................................................. 27
Figure 2-4:
Aerial photo of the meadow showing the extent of lodgepole pine tree
encroachment ............................................................................................ 28
Figure 2-5:
Regional Geologic Map of the meadow study area .................................. 29
Figure 2-6:
Local Geologic Map of the meadow study area ....................................... 30
Figure 2-7:
Geologic cross-section A-A’..................................................................... 31
Figure 2-8:
Incremental rainfall recorded at the Van Vleck weather station during
the 2008-2009 water year.......................................................................... 32
Figure 2-9:
Daily snow water content recorded at the Van Vleck weather station
during the 2008-2009 water year .............................................................. 33
Figure 2-10:
Photo of meadow conditions in late spring, 2009 ..................................... 34
Figure 2-11:
Photo of Tells Creek in late spring, 2009 ................................................. 35
Figure 2-12:
Photo of meadow conditions in late summer/early fall, 2009 .................. 36
Figure 2-13:
Photo of Tells Creek in late summer/early fall, 2009 ............................... 37
xi
Figure 3-1:
Photo of the typical transition between the meadow and the surrounding
hill slopes .................................................................................................. 54
Figure 3-2:
Photo of the setup of a seismic survey conducted in the meadow ............ 55
Figure 3-3:
Photo of the geophysical sledgehammer and strike plate method ............ 56
Figure 3-4:
Locations of seismic surveys conducted in the meadow .......................... 57
Figure 3-5:
Example of a seismic reduction worksheet used in calculating meadow
sediment depths ......................................................................................... 58
Figure 3-6:
Locations of soil boring advancements and piezometer installations
conducted in the meadow and adjacent hillslopes .................................... 59
Figure 3-7:
Photo of a typical hand auger boring advanced in the meadow ............... 60
Figure 3-8:
Depiction of the meadow extent in relation to the surrounding
topography ................................................................................................ 61
Figure 3-9:
Surface topography within the meadow perimeter ................................... 62
Figure 3-10:
Control points of meadow sediment thicknesses used for subsurface
interpolation .............................................................................................. 63
Figure 3-11:
Topographic map of top of bedrock elevations in the meadow
subsurface ................................................................................................. 64
Figure 3-12:
Isopach map of sediment thicknesses throughout the meadow ................ 65
Figure 4-1:
Photo of a typical piezometer installation in the meadow ...................... 102
Figure 4-2:
Location of piezometers and soil borings in the meadow....................... 103
Figure 4-3:
Comparison of calculated groundwater elevations using transducer
readings uncorrected and corrected for fluctuations in barometric
pressure ................................................................................................... 104
Figure 4-4:
Corrected groundwater elevations recorded from the piezometers installed
in the meadow for the entire duration of monitoring (September 29, 2008
to October 17, 2009) ............................................................................... 105
xii
Figure 4-5:
Depth to water from the meadow surface in piezometers PZ-2 and
PZ-3......................................................................................................... 106
Figure 4-6:
Soil sample classification based on USDA soil textural triangle ........... 107
Figure 4-7:
Initial displacement of the water column in piezometer PZ-2 due to slug
testing ...................................................................................................... 108
Figure 4-8:
Initial displacement of the water column in piezometer PZ-3 due to slug
testing ...................................................................................................... 109
Figure 4-9:
Normalized plot of the recovery of the hydraulic head in PZ-2 during slug
testing ...................................................................................................... 110
Figure 4-10:
Normalized plot of the recovery of the hydraulic head in PZ-3 during slug
testing ...................................................................................................... 111
Figure 4-11:
Plot of the change in volumetric water content over time for two soil
samples collected from the meadow ....................................................... 112
Figure 4-12:
Soil moisture retention curve for soils classified as sandy loams ........... 113
Figure 4-13:
Soil moisture retention curve for soils classified as loamy sands ........... 114
Figure 4-14:
Plot of water table levels below land surface and incremental
precipitation ............................................................................................ 115
Figure 4-15:
Average depth to water below the meadow surface during the 2008-2009
water year ................................................................................................ 116
Figure 5-1:
Graphical plot of the relationship between the stomatal conductance of
various species and vapor pressure deficit as described by Waring and
Schlesinger (1985) .................................................................................. 137
Figure 5-2:
Graphical recreation of the relationship between stomatal conductance and
vapor pressure deficit for Douglas fir species......................................... 138
Figure 5-3:
Graphical recreation of the relationship between stomatal conductance and
vapor pressure deficit for hemlock species ............................................. 139
Figure 5-4:
Landcover classification generated from the Aqua satellite associated with
obtaining leaf area index data ................................................................. 140
xiii
Figure 5-5:
Plot of leaf area index (LAI) values obtained from the Aqua satellite for
January to October 2009 ......................................................................... 141
Figure 6-1:
Boundary conditions assigned in the model to simulate groundwater flow
in the meadow subsurface ....................................................................... 157
Figure 6-2:
Extent of model areas assigned as recharge zones to simulate tree
removal ................................................................................................... 158
Figure 6-3:
Location of recharge zones assigned to the model during calibration .... 159
Figure 6-4:
Results of sensitivity analysis of hydraulic conductivity (K) compared to
recharge ................................................................................................... 160
Figure 6-5:
Results of sensitivity analysis of specific yield (Sy) .............................. 161
Figure A-1:
Seismic survey reduction worksheet for survey TMS 1 ......................... 170
Figure A-2:
Seismic survey reduction worksheet for survey TMS 2 ......................... 171
Figure A-3:
Seismic survey reduction worksheet for survey TMS 3 ......................... 172
Figure A-4:
Seismic survey reduction worksheet for survey TMS 4 ......................... 173
Figure A-5:
Seismic survey reduction worksheet for survey TMS 5 ......................... 174
Figure A-6:
Seismic survey reduction worksheet for survey TMS 6 ......................... 175
Figure A-7:
Seismic survey reduction worksheet for survey TMS 7 ......................... 176
Figure A-8:
Seismic survey reduction worksheet for survey TMS 8 ......................... 177
Figure B-1:
Boring log for hand auger boring HA-1 ................................................. 179
Figure B-2:
Boring log for hand auger boring HA-2/PZ-2 ........................................ 180
Figure B-3:
Boring log for hand auger boring HA-3/PZ-3 ........................................ 181
Figure B-4:
Boring log for hand auger boring HA-5 ................................................. 182
Figure B-5:
Boring log for hand auger boring HA-6 ................................................. 183
Figure B-6:
Boring log for hand auger boring HA-7 ................................................. 184
xiv
Figure B-7:
Boring log for hand auger boring HA-8 ................................................. 185
Figure B-8:
Boring log for hand auger boring HA-9 ................................................. 186
Figure C-1:
Grain size distribution plots for soil samples collected from boring
HA-1 ....................................................................................................... 188
Figure C-2:
Grain size distribution plots for soil samples collected from boring
HA-2 ....................................................................................................... 188
Figure C-3:
Grain size distribution plots for soil samples collected from boring
HA-3 ....................................................................................................... 189
Figure C-4:
Grain size distribution plots for soil samples collected from borings
HA-5, HA-7, and HA-8 .......................................................................... 189
Figure C-5:
Grain size distribution plots for soil samples collected from borings
HA-6 ....................................................................................................... 190
Figure C-6:
Grain size distribution plots for the soil sample collected from boring
HA-9 ....................................................................................................... 190
xv
1
Chapter 1
INTRODUCTION
During a time of ever-increasing awareness of the importance of well functioning
stream and associated riparian areas, forest managers face a wide range of restoration
alternatives (Hammersmark et al., 2008). Restoration efforts are typically considered
when it has been determined that a riparian system may be functioning below its
potential. Healthy systems are generally characterized by their hydrologic functionality
and their ability to support diverse botanical and zoological communities (Allen-Diaz,
1991). However, each stream system is unique which can lead to a complex decision
making process for managers in determining the most beneficial and cost effective
restoration alternative. A common theme of these restoration efforts in western North
America over the past 20 years has been to re-establish degraded systems to enhance
sources of groundwater storage in an effort to preserve and replenish water resources and
support the ever growing population (Smerdon et al., 2009).
1.1 Objectives
The primary objective of this study is to address the relationship of vegetation
management and potential groundwater storage in a mountain meadow in the Eldorado
National Forest in the California Sierra Nevada. More specifically, the study addresses
the quantitative impacts that encroaching lodgepole pines (Pinus contorta – var. latfolia)
have on shallow groundwater in mountain meadow settings. The hypothesis of this study
is that the removal of encroaching lodgepole pine trees is an effective method of
increasing the available groundwater storage in a mountain meadow over a time period
2
when the majority of meadows in similar environments exhibit declines in storage. Any
increase in available groundwater will be reflected in the rise of the water table from its
current level and allow for a greater amount of subsurface groundwater storage and
surface area of overall wetlands within the meadow system.
The primary product of this research is quantified values of net changes in the
potential groundwater storage in a meadow system due to the removal of lodgepole pines.
This research is also intended to provide a compilation of methods and techniques to
serve as a reference in making informed decisions as to the effectiveness of this
restoration alternative when considered by meadow and national forest managers.
1.2 Background
Historically, restoration efforts in meadow systems have often focused on
quantifying the water supply impacts of stream and river degradation due to unchecked
erosion (Micheli and Kirchner, 2002). This erosion is often linked to the land use of the
system (i.e. grazed or non-grazed) and can lead to significant shortfalls of subsurface
storage available in a given system (Lindquist and Wilcox, 2000). Figure 1-1 depicts a
conceptual example of a typical meadow that has undergone this type of degradation.
Remediation of this condition is often achieved by utilizing methods such as “pond and
plug” where an invasive procedure is completed by excavating the alluvial floodplain
material, which forms the ponds, followed be relocation of excavated material to the
incised channel (the plug) (Figure 1-2). Pre-restoration and post-restoration monitoring
has shown that restoring a watercourse to its natural state can result in significant
increases in available groundwater storage as well as an increase in the amount of time
3
the system is fully inundated or “wet” (Hammersmark et al., 2008; Cornwell and Brown,
2008).
Mountain Meadow Characteristics
Many of the aforementioned meadow systems are located in upper elevations and
are classified as mountain meadows (meadows) based upon the ecosystems they support
and their hydraulic role in surface-water groundwater interactions (Hayashi and
Rosenberry, 2001). The meadows typically form valley bottom aquifers in mountainous
terrain and have been shown to be a considerable source of groundwater recharge in the
western United States and Canada when hydrologically functional (Smerdon et al., 2009).
In addition to a source of groundwater, meadows that are hydrologically functional often
support root systems that can stabilize stream or river banks that can decelerate erosion
(Ponce and Lindquist, 1990).
Meadows have also been shown to play an important role in flood attenuation as
surface water retention times are often long in duration in the meadow environment. This
function is largely due to the greater development in vegetation compared to the steep,
typically sparsely vegetated, mountainous terrain that surrounds them (Stohlgren et al.,
1989). Although meadows only account for less than 10 % of the California Sierra
Nevada (Ratliff, 1985), they provide unique ecosystems in what are characterized as
challenging environments and support many forms of wildlife (Lindquist and Wilcox,
2000).
Conifer encroachment in meadows
Many researchers have observed the dynamics of the encroachment of conifers
4
into meadow environments over the past 50 years in western United States. Figure 1-3
depicts a meadow in Yosemite National Park where conifer encroachment has occurred.
Tree encroachment in this area of the Sierras appears to be widespread as the National
Park Service reports on their website that some 75 to 90 percent of Yosemite meadows
have been lost to tree encroachment since the late 1800s. Tree encroachment is not
limited to only the Sierras, as a study conducted in mountain meadows in the Cascade
Mountains of western Oregon reports that conifer encroachment has become more
extensive in recent history, with a decrease of open meadow areas from 5.5 % in 1946 to
2.5 % in 2000 over a study area of 350 km2 (Griffiths et al., 2005). The results of this
study note that there does not appear to be a single event that triggers the encroachment
but rather may be variations between meadows. They cite factors such as disturbances
caused by road construction through meadows that may lead to an increased likelihood of
encroachment. Research by Miller and Halpern (1998), indicates agreement with this
factor and adds that the practice of fire suppression in national forests may also be a
contributor as fires are thought to keep meadows open.
Other researchers note that the potential for tree encroachment may be tied
directly to particular species and to the relatively high amount of soil moisture found in
meadows. A study conducted by Tarrent (1953) concluded that some species of conifers
(specifically lodgepole pine) have an affinity for low gradient sites with relatively
shallow water tables and poorly drained soils that are commonly found in meadows.
Conversely, species such as ponderosa pine are more suited to steeper terrain, deeper
water tables, and well drained soils. The conclusions of this study are generally consistent
5
with observations by Berlow et al. (2002) and Dwire et al. (2006) that note many types of
meadow vegetation including various shrubs and forbs are controlled by soil moisture
conditions and water table depths.
Observed effects of vegetation removal in mountain meadows
The primary focus of meadow research as described in Cornwell and Brown
(2008), Hayashi and Rosenberry (2001), and Hammersmark et al. (2008), was to
document the response of meadow hydrologic function due to degradation and eventual
restoration of the streams or rivers found within the meadow environment. However,
another area of research has focused on the hydrologic changes in the meadow as a result
of vegetation removal.
Research has been conducted on the relationship between
meadow hydrology and the distribution of different vegetation types and communities
however, for the purposes of this study, the main area of interest is focused on
documented responses of meadow hydrology to tree removal or harvesting. Studies by
Bliss and Comerford (2002) and Sun et al. (2000) have shown that following tree
harvesting in a cypress dominated wetland in Florida, the hydrologic response was a
significant elevation gain in the groundwater table, or a groundwater table nearer to the
ground surface. This rise in the water table equates to a greater volume of water that is
stored in the subsurface. The water table rise was noted to be the greatest during the fivemonth dry periods of the observed years following harvesting as well as in areas where
trees were not only harvested from the low lying wetland, but from the surrounding
uplands as well.
Conversely, in a study consisting of a juniper dominated riparian habitat (Dugas
6
and Hicks, 1998) a significant hydrologic response was not noted to have occurred
following tree harvesting. During review of the literature, documentation was not found
addressing the hydrologic response of vegetation removal in area with lodgepole pine –
the species of interest for this study.
1.3 Development of Project
This study was developed in conjunction with the United States Forest Service –
Pacific Ranger District located in the Eldorado National Forest. The results of this study
are of significant interest to the Pacific Ranger District, as the results of the study will
assist management in determining whether removal of lodgepole pine trees encroaching
on the meadow ecosystem is a viable form of restoration. The conventional wisdom by
Pacific Ranger District staff is that strategic removal of encroaching confers in mountain
meadows will assist in restoring a wetter wetland within the meadow (Koler and
Cornwell, 2008).
1.4 Summary of Approach
To evaluate the validity of the hypothesis effectively, the study area was carefully
chosen based on a number of criteria: 1) the study area is an undisturbed meadow in a
relatively natural state (i.e. lack of grazing or other anthropogenic activities); 2) the study
area is easily accessible as non-invasive field equipment will be used to characterize the
meadow; and 3) the meadow appears to have been degraded due to the encroachment of
lodgepole pine trees, the species of interest for this study. Through communications with
the Pacific Ranger District, the selected study area successfully met these criteria.
Determining the impact to potential meadow groundwater storage required the
7
determination of a number of meadow specific properties and conditions. The initial
scope of this study included the physical and hydrological characterization of the
meadow to determine total potential subsurface storage as well as any hydrologic changes
in the meadow over the course of the 2008-2009 water year (October 1, 2008 to
September 30, 2009). Methods were then employed to quantify estimated transpiration
rates of the encroaching lodgepole pines. The response of the water table to tree removal
was simulated by constructing a groundwater flow model. The results of the physical and
hydrologic characterization were used to define the limits and parameters of the model.
The estimated transpiration rates were then input into the model as recharge to simulate
the change in hydraulic head due to the elimination of groundwater withdrawal by the
encroaching lodgepoles. Changes in storage were determined based on overall water
table fluctuations calculated by the model. The results were tabulated and reviewed to
determine if this method of meadow restoration is effective in increasing the amount of
available groundwater in the meadow throughout the water year.
8
1.5
Tables and Figures
Figure 1-1: Conceptual diagram of the typical characteristics of degraded mountain
meadows. Diagram courtesy American Rivers (www.americanrivers.org).
9
Figure 1-2: Example of the pond and plug method of meadow restoration. Note the
ponds on the right side of the figure that have been excavated to plug the incised channel
near the bottom of the figure. Photo courtesy Wild Fish Habitat Initiative, Big Flat
Restoration Project (http://wildfish.montana.edu).
10
Figure 1-3: Photo of tree encroachment in a mountain meadow located in Yosemite
National Park, California. Photo courtesy National Park Service (http://www.nps.gov).
11
Chapter 2
STUDY AREA DESCRIPTION AND GEOLOGIC/ HYDROLOGIC SETTING
2.1 Introduction
This chapter describes the location and characteristics of the study area selected
for this research including: the meadow extent, regional topography, and vegetation
distribution. Many geological processes over time have led to the formation of the
meadow landscape found in the study area. This chapter also describes the current
geological configuration of the study area as documented by previous researchers and the
general relationship of the geologic units encountered during the course of this study.
Additionally, the sequence of historical geological events that have led to the
development of the study area is discussed. Finally, the regional and local hydrological
setting of the study area is described including: the physical characteristics of
hydrological features, climatic events recorded during the duration of the study, and
observed changes in the study area hydrology over the duration of the study.
2.2 Study Area Description
Location
The study area is a meadow known as Timothy Meadow (herein referred to as the
meadow) and is located approximately 60 miles east of Sacramento, California in the Van
Vleck Ranch area of the El Dorado National forest (Figure 2-1). The meadow is located
within the southwest quarter of Section 28 and southeast quarter of Section 29 of
Township 13 N and Range 15 E (USGS, 1993). Tells Creek flows perennially to the
southwest through the northern central portion of the meadow. The northern portion of
12
the meadow is bounded by a gravel service road that provides access to the meadow from
Ice House Road, one of the main arteries through the Eldorado National Forest.
Meadow Extent and Regional Topography
The surficial extent of the meadow was estimated from the topography where the
surrounding steep hill slopes act to define the perimeter of the meadow. Specific details
on the methods utilized to initially define the meadow extent are discussed in Chapter 3.
The meadow extent was corroborated based on the distribution and depth of
unconsolidated meadow sediments that is discussed in Chapter 3. The meadow boundary
is shown on Figure 2-2.
The meadow covers approximately 64,000 m2 (or 15 acres) and lies at an
elevation of approximately 2018 m in the north-central Sierra Nevada Mountains.
Topographically, the meadow slopes gently to the southwest at an approximate gradient
of 2%. The meadow is bounded to the north, south, and east by relatively steep slopes
that provide a relatively unique setting for the overall gentle meadow topography. This
configuration is consistent with topographical relationships found in meadows elsewhere
in the Eldorado National forest where mountain meadows cover approximately 9,000
acres of the forest landscape (Stillwater Sciences, 2008).
Vegetation distribution
Review of aerial photographs combined with meadow reconnaissance indicates
that the vegetation in the study area ranges from a grass and forb dominated riparian zone
adjacent to Tells Creek to a willow shrub with lodgepole pine stands near the boundaries
of the meadow. Lodgepole pine encroachment is most apparent along the eastern portion
13
of the meadow but has not reached the central portion of the meadow, which is primarily
a riparian corridor along Tells Creek. As depicted on Figure 2-3, lodgepole pines are
commonly found in groups or islands within the meadow which is consistent with
descriptions of meadows researched in the Pacific Northwest that exhibit evidence of
active conifer encroachment (Griffiths et al., 2005). As shown on Figure 2-4,
approximately 44% of the meadow (28,000 m2) has been inundated by lodgepole pine.
Although a detailed tree inventory was not conducted as part of this study, it appears that
younger trees are found on the fringes of the areas of encroachment. The exact age
distribution of the trees within the meadow is unknown, but a number of the trees within
the meadow perimeter appear well established and reach heights up to approximately
10 m.
2.3 Geologic Setting
Summary of previous geologic interpretations
The geologic units encountered in this study are consistent with the general
descriptions of upper elevation Sierra Nevada meadows by previous researchers. Wood
(1975) describes that glaciation is a common geomorphic process that often result in the
formation of low-gradient basins where meadows may form over time. The primary
meadow-forming mechanism is to create an enlarged depression the basement rock of
pre-existing valleys that may be subsequently infilled. Glacial processes also often leave
a portion of the early infill in the form of till and moraine deposits that comprise the
unconsolidated meadow sediments (Coyle, 1993). As shown on Figure 2-5, the units that
underlie Timothy Meadow consist of basement rock comprised of Mesozoic granite,
14
granodiorite, and diorite overlain by glacial deposits of Quaternary age. Irwin and
Wooden (2001) further constrain the age of the granitics found in this portion and much
of the Sierra Nevada as approximately 125 to 82 Ma (mid-early to mid-late Cretaceous)
and notes that these rock are a part of the larger Sierra Nevada batholith.
Subsequent to CDMG mapping (Figure 2-5), Coyle (1993) conducted a more
detailed mapping study of the Eldorado National Forest. The interpretations from Coyle
are similar to the CDMG report, however alluvium is mapped by Coyle as overlying the
glacial deposits in the low-lying meadow basin (Figure 2-6).
This interpretation is
consistent with alluvial soil units that were mapped in the meadow as part of the
Eldorado National Forest Soil Inventory (Mitchell et al., 1993). The alluvium is described
as non-cohesive sands and gravels that are comprised of silty sands and silty gravels.
Geologic units encountered during this study
During the course of the subsurface investigations for this study, comparisons
were made with the interpretations of previous researchers. Following review of the
subsurface data collected (discussed in greater detail in Chapter 4) the units described
above were encountered with one notable addition. Both of the above researchers
describe glacial deposits as existing not only in the meadow but regionally on the
surrounding steep hill slopes as well. Evidence was found for glacial deposits in the
meadow, however hillslopes adjacent to the meadow were observed to have little, if any,
glacial deposits. The overlying sediments were subsequently mapped as Quaternary-aged
slopewash deposits (Figure 2-7). Glacial deposits may have previously existed on these
slopes, but given the overall steepness it is likely they were subsequently removed over
15
the course of numerous episodes of post-glacial precipitation events. Numerous large
boulders (up to 3 m in diameter) are noted to be surficially exposed in the meadow which
may provide evidence of glacial activity within the meadow as the fluvial flow velocities
needed to relocate rocks of this size are unlikely to occur in this environment. Additional
evidence of glacial deposition encountered in the meadow subsurface is discussed below.
Silty sands with occasional gravels and clays were observed in the upper meter of
the meadow sediments which is consistent with the descriptions of alluvium observed and
mapped by Coyle (1993) and Mitchell et al. (1993). The alluvium is generally observed
to be moderately-sorted and are underlain by what is interpreted to be poorly sorted and
heterogeneous glacial till deposits. Grain sizes in these deposits range from silt to gravel,
although the sediment distribution was generally very similar to the alluvial deposits.
Clays were generally not observed in the glacial deposits and the sediments often lack
internal cohesion.
A key difference in identifying the general contact between the
alluvial and glacial deposits is the poorly sorted nature and the maximum size of the
coarser sediments in the glacial deposits, which was significantly higher when compared
to the alluvial deposits. This interpretation is consistent with the general observations
cited by Coyle (1993) and the description of glacial deposits found in the Sierra Nevada
discussed by Harden (2004).
2.4 Geologic History
The geologic history of the study area includes numerous events dating back to
the Mesozoic. Researchers generally agree that the Sierra Nevada was a prominent
mountain range in the Late Cretaceous although there many interpretations of the
16
subsequent events that have led to the surficial expression of the range that exists today
(Cecil et al., 2006). This section discusses the major geological and geomorphic
processes, from the Mesozoic to the present, that have shaped the landscape of the study
area.
Emplacement of the Sierra Nevada Batholith
The Sierra Nevada Batholith comprises the majority of the Sierra Nevada and is
the basement rock beneath the meadow. The batholith is comprised of approximately one
hundred known overlapping plutons of varying composition that intruded into preJurassic roof rocks between 88-206 Ma but mainly between 125 to 82 Ma. The batholith
consists of a number of plutons as large as 500 square miles and many as small as one
square mile (Bateman and Wahrhaftig, 1966, Irwin and Wooden, 2001).
Numerous models for the formation of the batholith have been submitted with
varying levels of acceptance among researchers. However, it is generally accepted that
emplacement of the batholith was due to the east-dipping subduction of the Farallon plate
beneath the North American plate beginning in the late Triassic ~210 Ma and continuing
episodically until the late Cretaceous ~85 Ma (Jones et al., 2004 and Cecil et al., 2006).
Additional evidence for subduction is the calc-alkaline nature of the present day rocks
that comprise the presently exhumed batholith (Ernst and Snow, 2008). The source
magma was initially mantle derived and as it made its way through the crust, it
subsequently mixed with crustal sources to generate magmas with compositions between
mantle and crustal sources (Bateman, 1988). In addition to the emplacement of the
batholith, continental-arc subduction resulted in widespread volcanism (similar to
17
Andean-type) throughout the present day location of the Sierra Nevada, the majority of
which may have been subsequently been eroded away (Harden, 2004).
Exhumation of the Sierra Nevada Batholith
At approximately 80 Ma, when subduction ceased along western North American,
the batholith was subterranean. Given that the batholith is currently surficially exposed at
elevations reaching greater than 10,000 ft, a significant amount of exhumation has
occurred. Since Cretaceous time subduction ended, the total amount of exhumation of the
batholith has been estimated to be 3 to 20 km (Cecil et al., 2006). The timing and
mechanism of exhumation or uplift has been of great debate among researchers (e.g.
Cecil et al., 2006, Jones et al., 2004, Wakabayashi and Sawyer, 2001, Small and
Anderson, 2001).
Cecil et al. (2006) built upon the thermochronology work by House et al. (2001)
and used the cooling ages of apatite and zircon crystals extracted from plutons at various
elevations and locations to constrain the timing of greatest uplift. This uplift occurred
from late Cretaceous to early Cenozoic and the maximum elevation of the range existed
during this time period. They concluded that the present day relief is the result of erosion
occurring since the early Cenozoic. Other researchers have argued that the Sierra Nevada
was relatively flat and contained a small amount of overall elevation during this time
period when compared to the middle-late Cretaceous (Jones et al., 2004). Many of the
proponents of this hypothesis have found that geomorphic and climatic evidence
supports their hypothesis. Wakabayashi and Sawyer (2001), cited evidence from
stratigraphic markers associated with river incision to support their hypothesis that
18
approximately 2 km of uplift occurred from 5 to 3 Ma. Small and Anderson (1995) used
combined erosion rates along the crest of the range with warmer and historically wetter
climatic patterns to support late Cenozoic uplift and increases in overall relief due to
flexural isostatic rebound due to unloading.
The mechanism by which uplift was accommodated also appears to differ among
researchers. Zandt et al. (2004) and Jones et al. (2004) hypothesize that uplift resulted
from replacement of the crustal root beneath the mountain range with more buoyant
asthenosphere. Zandt et al. (2004) additionally hypothesized that the weakening of the
root was initially due to low angle subduction associated with the Laramide Orogeny.
The root was further weakened by the northwestward migration of the Mendocino Triple
Junction. The majority of researchers agree that more work is needed to develop a model
that provides a clearer picture of the Sierran uplift.
Glacial Processes
As previously discussed, evidence of glacially derived sediment appears to be
present in the meadow subsurface. Additionally, glacial movement has been shown to
have drastically altered the existing landscape of the Sierra Nevada (Harden, 2004) and
has likely contributed to the formation of the meadow basin. The extent of the most
recent Pleistocene glaciation, the Tioga, has been mapped to include the study area. This
glaciation has been documented to have been active approximately 20,000 ka (Norris and
Webb, 1990).
Alluvial Deposition
Following glacial retreat, alluvial deposition is assumed to have commenced in
19
the meadow. Based on the current configuration of the meadow basin, it appears that
sediment has been fluvially deposited from catchments upgradient of the meadow. Given
the range in grain sizes of the alluvial deposits (clay/silt to coarse grained sand)
encountered in the meadow, it appears that fluvial deposition of varying magnitudes has
occurred from the late Pleistocene to the present. The magnitude of amount of sediment
inundation is likely climatically controlled. The meadow sits at an elevation where a
significant winter snowpack (discussed below in climatic setting sub-section) is an annual
occurrence, although it is subject to variability depending on precipitation patterns.
Seasonal rainfall and snowmelt may account for the deposition of the smaller grain sizes
and rain on snow events may generate flow magnitudes capable of depositing larger
grained sediments. Stillwater Sciences (2008) noted that one of the key benefits of
mountain meadows is that they can provide a record of historical climate regimes through
the sediment they retain and assist to piece together the history of these dynamic
environments.
2.5 Hydrologic Setting
Overview of regional and local hydrology
The regional setting of the meadow, the mapped geologic units, and the mapped
soils previously discussed suggests that the shallow groundwater in the meadow is
controlled by the underlying granodiorite and diorite bedrock, which functions as a
shallow groundwater aquitard. The meadow geology also suggests that the alluvial and
glacial deposits act as aquifer materials for shallow groundwater in the area. A deeper
aquifer is probably present and controlled by the bedrock structure as commonly seen
20
elsewhere on the Eldorado National Forest (Koler, 2007). Surface water to the meadow
is provided by Tells Creek, a northeast to southwest flowing perennial stream with its
headwaters (spring fed) within approximately 1000 m of the study area (Figure 2-1).
Characteristics of hydrologic features
The most readily apparent hydrologic feature within the meadow is Tells Creek.
The creek provides surface water to the meadow during periods of high recharge from
adjacent hill slopes and upgradient catchments. The creek was observed to have a well
defined channel in the upper and lower elevations of the meadow and a less defined
channel in the central portion. Multiple measurements of the channel depth indicated an
average channel depth of 32 cm, when referenced from the ground surface of the adjacent
banks. The sediment observed along the creek banks was generally consistent with the
meadow subsurface descriptions previously discussed (silty sands and gravels).
The creek enters the meadow from considerably steeper topography to the
northwest (Figure 2-1) and the channel appears to be bedrock controlled in these upper
reaches as significant sediment was not observed in the channel bottoms. The channel is
considerably different as it leaves the meadow through a 10 m long and 90 cm diameter
culvert that underlies the main access road to the meadow. Significant sediment was
observed in the channel bottom in this area of the meadow. Surface outflow may have
been restricted historically by the culvert as a circular ponded area was noted to exist
immediately to the northeast (meadow side) of the culvert. This ponded area is
approximately 6 to 7 m in diameter and is observed to be comprised primarily of fine
grained sediments (visually observed as silt). This pond was noted to contain through-
21
flowing water during late spring and stagnant water through the majority of the mid to
late summer of 2008 and 2009.
Climatic Patterns
Precipitation to the meadow is provided in the form of rain and snow, depending
on the local daily climatic conditions. The California Data Exchange Center (CDEC),
operated by the California Department of Water Resources (DWR) and available online
(http://cdec.water.ca.gov), was reviewed for daily precipitation records over the 2008 to
2009 water year (October 1, 2008 to September 30, 2009) as well as historic records of
average rainfall in the northern Sierra Nevada. CDEC reported that the closest weather
station with most consistent daily precipitation records of rain and snow over this time
period was the Van Vleck Station (DWR Id: VVL). Table 2-1 summarizes the details of
this weather station as well as other regional stations utilized to determine climatic
variations during the duration of the study. Due to the proximity of this station to the
meadow (0.5 km to the north), it is assumed that precipitation recorded at this station is
reflective of conditions within the meadow.
Total rainfall for the 2008 to 2009 water year was reported at 164 cm at the Van
Vleck Station. As shown on Figure 2-8, daily incremental rainfall events reach a
maximum of approximately 8 cm and the majority of rainfall falls from December to
March. DWR reports the rainfall for the 2008 to 2009 water year was approximately 80%
of the historical average for the northern Sierra Nevada (calculated by DWR based on
records of numerous stations throughout the region).
The daily snow water content quantified over this same time period was also
22
reported (Figure 2-9). The maximum daily snow water content reported was
approximately 90 cm. The depth of the daily snow pack was not reported at this location;
however studies have previously been conducted in the Sierras that have investigated the
density of fresh snow when compared with water. McGurk et al. (1988) conducted a
density study of fresh snowfall at an elevation of 2100 m in the central Sierra Nevada
region over a period of three years. The results of this study reported that the mean
relative density of fresh snow to water is approximately 12%. The relative density was
determined by dividing the density of the snow (in this case an average of 120 kg/m 3) by
the density of water (1000 kg/m3 at 4 oC, Fetter, 2001). Using these density results as a
proxy for the meadow results in a maximum snow pack depth of 7.6 m. This is a coarse
estimate but provides useful insight into the climatic setting of the meadow as well as
potential access limitations when conducting field studies at high elevations under
climatic regimes such what is observed at the meadow.
Observed hydrologic trends
Meadow hydrology is a dynamic process and can be particularly affected by
multiple processes such as evaporation and transpiration, and local and regional climate
(Dwire et al., 2006). During the course of this study, the meadow hydrologic conditions
were observed on numerous occasions during late spring and late summer/early fall as
access to the meadow was extremely limited from the late fall to mid-spring due to the
existing snow pack. Significant differences were noted in the meadow appearance during
these visits.
23
Figures 2-10 and 2-11 depict the observed meadow conditions during late spring.
The meadow appears to be near or approaching full saturation as water is observed at the
surface. Tells Creek appears to have over run its banks and has branched off into multiple
side channels that appear to have been previously established during high flows. Wood
(1975) notes that this “fanning out” of meadow streams onto their floodplains a common
condition in stable meadows with relatively shallow gradients (less than 3%). The
condition of the meadow surface appears to indicate recharge to the meadow from the
regional spring snowmelt exceeds any loss of storage due to surface evaporation, surface
outflow, or transpiration by meadow vegetation.
Figures 2-12 and 2-13 depict the
conditions as observed during late summer/early fall. Surface conditions were observed
to be considerably drier throughout the meadow and the channel of Tells Creek appearing
dry with the exception of some minor stagnant ponding in low lying areas.
This
condition appears to indicate that any recharge to the meadow is exceeded by losses due
primarily to transpiration of vegetation, including lodgepole pine.
24
2.6 Tables and Figures
Station
Name
Van
Vleck
Station
Van
Vleck
Station
Van
Vleck
Station
Bear
Meadow
Tahoe
Vista
Operator
Sacramento
Municipal
Utility
District
(SMUD)
Sacramento
Municipal
Utility
District
(SMUD)
Sacramento
Municipal
Utility
District
(SMUD)
Sacramento
State
University
Dept. of
Geology
MADIS
NOAA
Database (1)
Elevation
(m)
Distance/Direction
from Meadow
(km)
Parameters
Recorded and
Frequency
2042
0.5 km to the north
Air Temperature
Maximum and
Minimum (Daily)
Accumulated and
Incremental
Precipitation (Hourly,
Daily)
Units
o
F
2042
0.5 km to the north
in
2042
0.5 km to the north
Snow Water Content
(Hourly, Daily)
1560
52 km to the
northwest
Barometric Pressure
(15 min)
m of water
1950
40 km to the
northeast
Maximum and
Minimum Relative
Humidity (Daily)
Percent
in
(1) The Meteorological Assimilation Data Ingest System (MADIS) operated by the National Oceanic and
Atmospheric Administration (NOAA).
Table 2-1: Summary of weather stations used to approximate climatic variations in the
meadow for the duration of the study.
25
Figure 2-1: Location map of the Timothy Meadow study area. Base map from Loon Lake
topographic 7.5-minute quadrangle (USGS, 1993).
26
Figure 2-2: Aerial photo of the meadow perimeter. The meadow was initially defined
using topography and further constrained by the distribution of subsurface sediment
thicknesses. Tells Creek, a southwest flowing perennial stream, is shown in the northern
portion of the meadow.
27
Figure 2-3: Photo of the meadow showing clusters or islands of encroaching lodgepole
pine trees.
28
Figure 2-4: Aerial photo of the meadow showing the extent of lodgepole pine tree
encroachment. The hatched outlined areas show the approximately 44% of the meadow
that has been inundated by lodgepole pine trees.
29
Figure 2-5: Regional Geologic Map of the meadow study area. Modified from California
Division of Mines and Geology (CDMG, currently referred to as the California
Geological Survey) 1:250,000 Sacramento Sheet, 1966.
30
A
A’
Figure 2-6: Local Geologic Map of the meadow study area. Modified from Coyle, 1993.
Base map is 1:24,000 Loon Lake Topographic Quadrangle (USGS, 1993). The location
of geologic cross-section A-A’ is shown transecting the meadow from NW to SE.
31
Figure 2-7: Geologic cross-section A-A’. The depth and descriptions of the
unconsolidated sediments that are shown to overlie bedrock are from subsurface
investigation data that are described in further detail in Chapters 3 and 4, respectively.
32
Increm ental Daily Rainfall During the 2008-2009 Water Year
9.0
8.0
Precipitation (cm)
7.0
6.0
5.0
4.0
3.0
2.0
1.0
9/1/2009
8/1/2009
7/1/2009
6/1/2009
5/1/2009
4/1/2009
3/1/2009
2/1/2009
1/1/2009
12/1/2008
11/1/2008
10/1/2008
0.0
Date
Figure 2-8: Incremental rainfall recorded at the Van Vleck weather station during the
2008-2009 water year. This station is located approximately 0.5 km north of the meadow
at an elevation of 2042 m above mean sea level. Data was obtained online from the
California Data Exchange Center (CDEC) operated by the California Department of
Water Resources (DWR).
33
Daily Snow Water Content During the 2008-2009 Water Year
100
90
Snow Water Content (cm)
80
70
60
50
40
30
20
10
9/1/2009
8/1/2009
7/1/2009
6/1/2009
5/1/2009
4/1/2009
3/1/2009
2/1/2009
1/1/2009
12/1/2008
11/1/2008
10/1/2008
0
Date
Figure 2-9: Daily snow water content recorded at the Van Vleck weather station during
the 2008-2009 water year. Data was obtained from CDEC.
34
Figure 2-10: Photo of meadow conditions in late spring, 2009. The meadow appears to be
fully saturated and standing water is common as is shown above.
35
Figure 2-11: Photo of Tells Creek in late spring, 2009. As shown near the bottom right of
the photo, the creek appears to have overrun its banks and is beginning to fan out across
the meadow.
36
Figure 2-12: Photo of meadow conditions in late summer/early fall, 2009. The meadow
water table appears to have significantly dropped and overall surface condtions are much
drier.
37
Figure 2-13: Photo of Tells Creek in late summer/early fall, 2009. Water in the creek is
nearly non-existent with the exception of some stagnant ponding. Locating the channel in
some portions of the meadow during this time period proved to be difficult due to lack of
flow.
38
Chapter 3
PHYSICAL CHARACTERIZATION
3.1 Introduction
To quantify the groundwater storage potential of an aquifer underlying a meadow,
the meadow must first be defined spatially and vertically (Cornwell and Brown, 2008).
Determining the surficial and subsurface configurations of the meadow allowed for the
quantification of the amount of unconsolidated sediment (that comprises the meadow
shallow aquifer) potentially available for groundwater storage. The determination of this
pattern also greatly assisted in determining the physical constraints of the groundwater
flow model beneath the meadow. This chapter describes the techniques utilized to
determine the physical and three-dimensional shape of the meadow. These techniques
included a combination of field and computer analyses of surficial features as well as the
subsurface sediment. This chapter presents the results of these analyses and a discussion
of noted limitations.
3.2 Methods
Numerous methods of surficial and subsurface investigation techniques were
utilized in the field to record geographic data on the meadow extent and to determine the
thickness of the underlying sediment. Additionally, available remote imaging coverage
was obtained for the study area from the USGS and online services. Data collected and
recorded in the field were processed using computer software offsite. These data were
combined with remote images to aid in conceptualizing the specific configuration of the
meadow surface and subsurface.
39
Survey of meadow boundaries
The meadow boundaries were defined based on specific physical criteria. These
criteria were necessary because the meadow is surrounded by a range of regional
environments, commonly steep granitic slopes with dense conifer coverage and periodic
gradual slopes with a thin (less than 20 cm) layer of topsoil and sparse vegetation. These
regional environments are common in upper elevation mountain meadows in the Sierra
Nevada (Wood, 1975). For the purposes of this study, defining the meadow boundaries
was a key component of determining the extent of the underlying aquifer.
The surficial meadow boundaries were delineated by an overall low topographic
gradient and the presence of vegetation typically encountered in non-degraded meadow
environments, such as: grasses, rushes, and small forbs.
Meadow boundaries were
visually defined at significant topographic transition zones (i.e. low to high gradient) and
at significant changes in vegetation (i.e. grass and forb to dense, well-established
conifers). An example of the typical transition between the meadow and the surrounding
hill slopes is shown in Figure 3-1.
The boundaries of the meadow were specifically defined in the field using a
handheld global positioning system (GPS) unit. Individual GPS points were collected at
a minimum of every 30-40 m along the meadow perimeter. The horizontal precision of
the GPS unit ranged from less than 50 cm up to 6 m, depending on the quality of the
satellite coverage during each day of data collection. Collected points were imported into
Geographic Information System software, specifically ArcMAP version 9.3 (GIS) and
manipulated to create a polygon defining the perimeter/boundaries of the meadow.
40
Calculations were then performed in GIS to determine the area of the meadow surface.
Surficial Topography
Surficial elevation data of the meadow and vicinity were provided by USGS
Digital Elevation Models (DEM) obtained online from the “GIS Data Depot” site
available at http://data.geocomm.com. DEM data coverage was obtained for the entire
7.5-minute Loon Lake Quadrangle (includes the study area) at 10 m resolution, or one
calculated elevation data point (at 1 m resolution) for each horizontal 10 m by 10 m grid
cell (square) overlying the coverage area. The DEM data were processed using GIS to
create a surficial layer of the topography within the meadow boundary from which
analysis could be conducted.
Geophysical surveys of subsurface
Shallow refraction seismic surveys were conducted at selected locations
throughout the meadow to determine the thickness or depth of the sediment in the
meadow. This method of subsurface investigation was selected primarily due to its low
overall impact to the environmentally sensitive study area. Seismic surveys are typically
much less invasive than advancing boreholes with a truck-mounted drill rig, as the latter
would involve driving heavy machinery across the meadow, potentially causing damage.
The key assumption that is required for conducting seismic surveys is that the density of
materials increases with depth (Telford et al., 1990). Reviewing the mapped geologic
units in the vicinity of the study area appeared to indicate that unconsolidated meadow
sediments overlie bedrock and therefore, this assumption appeared valid.
Figure 3-2 depicts a typical survey setup as conducted in the study area. Seismic
41
surveys were obtained by recording the refraction of sound waves induced into the
subsurface. Sound waves were generated in the field by striking an iron plate seated in
the ground with a sledgehammer (Figure 3-3).
A line of 12 geophones spaced
approximately 3 m apart was temporarily installed at the ground surface to record the
travel times of the resultant waves. The surveys were recorded utilizing a multi-channel
signal enhanced engineering seismograph (EG&G Model 1225).
Each survey was
approximately 34 m in length and locations were selected based on the perceived
distribution of sediment in the meadow. A total of eight surveys (TMS-1 to TMS-8) were
conducted throughout the meadow (Figure 3-4). Each of the surveys provided two points
where depth to bedrock was determined, for a total of 16 individual measurements.
The travel time to each geophone is a direct function of the relative density of the
subsurface materials. The velocity of the sound waves will vary, depending on the
differences in the substrate. These differences can be used to calculate the depth to the
higher velocity interval utilizing the following equation, which assumes a horizontal
contact between intervals of varying velocities, from Telford et al., 1990:
D
X c V2  V1

2
V2  V1
where:
V1 = velocity of sound waves in layer 1
V2 = velocity of sound waves in layer 2
Xc = Critical depth of the intersection of V1 and V2
D = Depth to the top of layer 2
42
The velocities of the two layers (V1 and V2) were determined by calculating the
inverse of the slope through a minimum of two points plotted as a function of travel time
versus distance. Surveys were performed in both directions (N to S and S to N) along
each transect and generated two sets of seismic data. The surveys were considered
representative and of adequate quality if the end travel times from each survey were
calculated to be within 10% of each other. The critical depth was determined graphically
by determining the distance intercept of the intersection of the of the two velocity lines.
Figure 3-5 depicts an example of a seismic survey reduction worksheet developed for
graphically solving the above equation. This survey and the remainder of the worksheets
are included in Appendix A.
Advancement of soil and rebar borings and shallow piezometers
A total of six hand auger borings (HA-1 to HA-3 and HA-5 to HA-9) and four
piezometers (PZ-1 to PZ-4) were advanced within the defined meadow boundary. Hand
auger borings HA-2 and HA-3 were advanced directly adjacent to PZ-2 and PZ-3,
respectively and were considered individual points of bedrock depth. Additionally, eight
rebar borings (RB-1 to RB-8) were advanced along the slopes of the surrounding
meadow boundary. The purpose of the hand auger borings was to determine the depth to
bedrock in areas where seismic surveys were unable to be conducted due to access
limitations (primarily due to vegetation) and to verify depth to bedrock indicated by the
seismic surveys. The purpose of the rebar borings was to verify that the meadow
boundary as determined by topography was accurate. Specifically, if significant depths of
unconsolidated sediment were noted to exist outside the perceived meadow boundaries,
43
then the meadow extent would require adjustment. The installation of the piezometers
also served to provide additional depths. Figure 3-6 shows the location of the borings
and piezometers. It should be noted that the advancement of the hand auger borings and
piezometers served an additional purpose, which was to obtain representative samples of
the subsurface sediment for visual classification, laboratory analysis of sediment
properties, and to monitor groundwater levels in the meadow. The details of the methods
and results associated with these analyses are discussed in Chapter 4.
Hand auger borings were advanced using a traditional hand auger equipped with a
9 cm diameter bucket approximately 30 cm in length (Figure 3-7) and rebar borings were
advanced by pounding 1.8 m sections of rebar into the ground using a small
sledgehammer. Borings were advanced continuously until refusal was encountered at
bedrock or the depth of the borehole exceeded the equipment limitations (total depth of
4.5 m). Piezometers were manually advanced using a post-hole driver until bedrock was
encountered.
Subsurface topography and sediment volume calculations
Results of the above investigations were utilized to construct the subsurface
profile of the bedrock underlying the meadow sediments and to verify the surficial extent
of the meadow. Twenty-five individual points quantifying the depth to bedrock from the
ground surface were obtained from these investigations and depth values determined at
these locations served to function as control points in interpreting the meadow
subsurface. Individual depth measurements were subtracted from corresponding surface
elevations obtained from the DEM. Although the distribution of depth measurements
44
provides fair coverage of the meadow, some interpolation was required to generate the
meadow-wide subsurface bedrock profile at the same scale as the DEM. Interpolation
between individual points was accomplished using GIS, specifically an inverse-distanceweighted method where interpolated values between points are a function of the distance
from the control points (i.e. the closer in proximity to a control point weights the
interpolated value more towards the value at the control).
The interpolated values allowed construction of a subsurface DEM at the same
resolution (10 m by 10 m) as the previously constructed surface DEM. Both of the
DEMs were confined to limits of the meadow boundaries. To quantify the volume of
unconsolidated sediment within the meadow, the elevations from the subsurface DEM
were subtracted from the elevations of the surface DEM. This process was accomplished
utilizing GIS, which contains a built-in function for determining the volume between
layers of the same resolution.
3.3 Results
Survey of meadow boundaries and surface topography
The surficial extent of the meadow as constrained by the surrounding topography
is shown on Figure 3-8. The meadow boundary as surveyed in the field is generally
widest in the central portion of the meadow (up to 210 m in width) and is considerably
narrower in the upper and lower portions (down to 25 m in width). The surface area of
the meadow was calculated in GIS to be approximately 64,000 m2 (or 15 acres).
The meadow surface topography generated from the USGS DEM is shown on
Figure 3-9. Surficial elevations in the meadow range from approximately 2021 m in the
45
northeast portion to 2010 m in the southwest portion. The horizontal distance between
these two areas is approximately 500 m, resulting in a calculated gradient of 2%.
Seismic surveys
The results of the seismic surveys are summarized on Table 3-1. Velocity
calculations (V1) for the shallow unconsolidated sediment ranged from 313 to 472 m/s
with a mean of 383 m/s and for the bedrock (V2) ranged from 1,583 to 2,438 m/s with a
mean of 1,872 m/s for the deeper bedrock. The shallow velocities are consistent with
reported velocities found in loosely consolidated sediments (Sharma, 1976). Calculated
depths to bedrock from the seismic surveys using the horizontal contact equation (Telford
et al., 1990) ranged from 1.30 m (TMS-8S located near the southwestern boundary of the
meadow) to 2.96 m (TMS-3S located in the east-central portion of the meadow).
Based upon review of the seismic profiles and the differences in calculated depths
from surveys performed in two directions (N to S and S to N) at each location, it was
evident that the contact between the V1 interval and V2 interval was not horizontal but
rather slightly dipping. The differences in depths indicated an approximate dip of 1o to 2o
towards the central portion of the meadow (to the south for surveys conducted in the
northern portion of the meadow and to the north for the southern surveys). Due to an
angled contact between two intervals, the V2 velocities and calculated depths above were
considered apparent as they were determined using an equation that did not account for a
dipping subsurface. Therefore, a series of equations outlined in Sharma (1976) were
utilized to reinterpret the seismic surveys. These equations accounted for a dipping
subsurface and are summarized below:
46



1
sin 1 V1 / Vd  sin 1 V1 / Vu
2

where:

= the dip between the V1 and V2 intervals
V1 = velocity of sound waves in layer 1
Vd = the apparent velocity of sound waves in layer 2 in the down-dip direction
Vu = the apparent velocity of sound waves in layer 2 in the up-dip direction; and

ic 

1
sin 1 V1 / Vd  sin 1 V1 / Vu
2

where:
ic= the critical angle of refracted sound waves from layer 2; and

hu 
V1Tiu
2 cos ic
and
hd 
V1Tid
2 cos ic
where:
hu= the true perpendicular depth to layer 2 in the up-dip direction
hd= the true perpendicular depth to layer 2 in the down-dip direction
Tiu = the intercept of the line for the layer 2 apparent velocity in the up-dip direction
Tid = the intercept of the line for the layer 2 apparent velocity in the down-dip
direction; and

V2  V1 / sin ic
where:
V2 = the true velocity of sound waves in layer 2
The calculated dip ranged from 0.2o south in survey TMS-1 to 2.8o north in survey
TMS-7. The range of V2 velocities utilizing the above equations was 1,642 m/s to 2,148
47
m/s with a mean of 1,854 m/s. Reinterpreted depths to bedrock ranged from 1.46 m
(TMS-8S) to 2.91 m (TMS-3S). The overall mean depth to the top of bedrock using this
method was 2.35 m, which is slightly higher than the mean depth using the horizontal
method. The depths and velocities resulting from reinterpretation of the seismic surveys
is summarized on Table 3-1. The surface elevations and calculated top of bedrock
elevations from the individual survey depths are summarized on Table 3-2.
As shown in Appendix A, the error estimates calculated from comparing the end
travel times for surveys in both directions were found to be below 10 % for all surveys in
which the travel time was obtained from all geophones. In two surveys (TMS-1 and
TMS-5), travel times were unable to be recorded at distances approximately 24 m to 30 m
from the strike plate. This was primarily due to the presence of background noise (likely
from wind and rustling brush) being recorded in the geophones furthest from the strike
plate.
This noise made determining the first arrival time of the seismic waves
problematic and did not allow for error estimates to be calculated for these two surveys.
Although this is a recognized data gap, a comparison of the calculated velocities in the
upper and lower layers in both directions for these surveys indicated good repeatability
and therefore, the calculated depths from these two surveys were used as part of the
inventory of meadow sediment depths.
Borings and Piezometers
The surface elevations and calculated depth to bedrock encountered from hand
auger borings and piezometer installations are summarized on Table 3-3. Depths to
bedrock determined form these two methods ranged from 0.61 m (HA-8 located along the
48
southern extent of the meadow) to 3.84 m (PZ-1 located near the northeastern most extent
of the meadow). The depth to bedrock from rebar borings ranged from 0.21 m to 0.55,
significantly less than the typical depths encountered in the meadow.
Subsurface topography and sediment volume calculations
Figure 3-10 depicts the individual depths recorded from the rebar borings as well as the
depths to bedrock obtained from the seismic surveys and hand auger borings. These
individual depths were used as control points for subsurface interpolation in GIS. The
resulting bedrock elevation map is shown on Figure 3-11 and an isopach map depicting
the depth to bedrock throughout the entire meadow extent is included as Figure 3-12.
The volume between the surface topography and the interpolated bedrock surface was
calculated to be 130,000 m3. Dividing this volume by the surface area of the meadow
(64,000 m2) results in an average meadow sediment depth of approximately 2 m.
3.4 Discussion
The use of topography to define the limits of the meadow (Figure 3-8) appears to
result in a representative estimate of the extent of the subsurface meadow sediment.
Results of the sediment depths determined by various methods of subsurface
investigation indicate that sediment thicknesses surrounding the meadow are significantly
less than thicknesses observed in the meadow (typically less than 0.5 m as opposed to
greater than 2 m, respectively). This relationship reflects a key difference in the setting
of the meadow when compared to the surrounding steep hill slopes. Significant sediment
profiles are noted to have the ability to accumulate in meadows due to their overall gentle
gradient (Wood, 1975). Conversely, the steep topography surrounding meadows appears
49
to inhibit significant accumulations of sediment. Therefore, the surrounding hill slopes
appear to provide good delineation of the meadow sediments that comprise the shallow
aquifer.
The use of selected soil borings to confirm the sediment depths from the seismic
surveys indicates agreement between the two methods, with noted limitations. As shown
on Figure 3-10, sediment depths from soil borings advanced in the central portion of the
meadow indicate total thicknesses ranging from 1.68 to 2.15 m while nearby seismic
surveys indicate depths ranging from 2.07 to 2.69 m. Based on the distribution of seismic
depths obtained in the remainder of the meadow, it appears that the depths from the soil
borings may be slightly underestimated. This is likely due to the limitations of using the
hand auger to determine depths to bedrock at a cm scale in borings with occasionally
coarse grained sediments and water table depths shallower than 1 m (Appendix B –
Boring Logs). During the advancement of soil borings, particularly in the central portion
of the meadow, soils with significant gravels were encountered near the terminal depth of
each boring. Continuing hand auguring through these gravels proved to be difficult and
the depth of bedrock was estimated at hand auger refusal at these locations. As shown on
Table 3-1 the calculated velocities of bedrock layers were generally consistent with a few
extremes. Overall, there appears to be greater confidence in the depths calculated using
this method due to this consistency as well as the lack of the physical limitations
commonly associated with manual methods such as hand auguring.
As shown on Figure 3-12, sediment depths throughout the meadow are
inconsistent, ranging from slightly less than 1 m along the southern meadow perimeter to
50
greater than 3.5 m in the northeastern extent of the meadow. Comparing the distribution
of these depths with data collected outside the meadow perimeter indicates that the
transition from the surrounding hill slopes to the meadow is relatively abrupt (from less
than 0.5 m to greater than 2 m often over a distance of less than 20 m). This relationship
likely due to the generally steep topography found in this area and as observed in other
Sierra Nevada meadows (Wood, 1975). The variability noted in the sediment depths also
illustrates the need to obtain multiple measurements when conducting subsurface
interpretations as part of volume calculations. The use of too few points may result in
significant over or underestimations of the total sediment volume as well changes in the
subsurface profile of the area of interest.
51
3.5 Tables and Figures
Survey ID#
Velocity V1
(m/s)
Apparent
Velocity V2
(m/s)
Apparent
Depth (m)
TMS-1N
324
1583
2.60
TMS-1S
330
1670
2.62
TMS-2N
375
1742
2.45
TMS-2S
313
2032
2.22
TMS-3S
338
2032
2.96
TMS-3N
332
2217
2.75
TMS-4N
369
1717
2.69
TMS-4S
381
1905
2.24
TMS-5N
445
1876
2.15
TMS-5S
469
1626
1.93
TMS-6N
397
2032
2.13
TMS-6S
472
1876
1.89
TMS-7N
406
1742
1.56
TMS-7S
353
2438
2.90
TMS-8N
381
1717
1.58
TMS-8S
435
1742
1.30
MIN
MAX
MEAN
313
472
383
1583
2438
1872
1.30
2.96
2.25
Calculated
Dip and
Direction
0.2o South
1.8o North
0.5o South
0.4o North
1.5o North
1.7o North
2.8o South
0.8o North
----
True
Velocity
V2(1) (m/s)
True
Depth(1)
(m)
1612
2.57
1640
2.69
2031
2.67
1693
2.07
2138
2.91
2101
2.78
1780
2.55
1835
2.57
1693
2.54
1783
2.19
1774
2.44
2112
2.30
2148
1.55
1864
2.78
1615
1.57
1845
1.46
1612
2148
1854
1.46
2.91
2.35
(1) = True velocity and depth calculated using seismic refraction equations for a dipping contact
between V1 and V2 intervals
Table 3-1: Summary of seismic surveys performed in Timothy Meadow. V1 velocities
represent travel times in the unconsolidated meadow sediment and apparent V2 velocities
represent travel times in the underlying bedrock, assuming a horizontal contact between
the V1 and V2 intervals. Following the determination that the contact between these
intervals was dipping, seismic data were reinterpreted using equations that calculate the
magnitude of dip and true V2 velocities and depths in the up-dip and down-dip directions.
52
Survey ID#
Ground Surface
Elevation (m)
Depth to
Bedrock (m)
Elevation of
Top of Bedrock
(m)
TMS-1N
2015.00
2.57
2012.43
TMS-1S
2015.77
2.69
2013.08
TMS-2N
2015.00
2.67
2012.33
TMS-2S
2015.00
2.07
2012.93
TMS-3S
2017.00
2.91
2014.09
TMS-3N
2017.00
2.78
2014.22
TMS-4N
2017.00
2.55
2014.45
TMS-4S
2018.00
2.57
2015.43
TMS-5N
2016.00
2.54
2013.46
TMS-5S
2017.00
2.19
2014.81
TMS-6N
2016.00
2.44
2013.56
TMS-6S
2017.00
2.30
2014.70
TMS-7N
2016.00
1.55
2014.45
TMS-7S
2016.00
2.78
2013.22
TMS-8N
2014.00
1.57
2012.43
TMS-8S
2016.00
1.46
2014.54
Table 3-2: Summary of depths to bedrock obtained from seismic surveys. The elevation
of the top of bedrock at each location was determined by subtracting the depth to bedrock
from the local ground surface elevation.
53
Borehole ID#
Ground Surface
Elevation (m)
Depth to
Bedrock (m)
Elevation of Top
of Bedrock (m)
PZ-1
2020.19
3.84
2016.35
PZ-2/HA-2
2015.77
2.15
2013.62
PZ-3/HA-3
2015.42
1.68
2013.74
PZ-4
2012.56
1.28
2011.28
HA-1
2019.00
2.29
2016.71
HA-5
2020.00
1.58
2018.42
HA-6
2018.00
2.44
2015.56
HA-7
2018.00
0.76
2017.24
HA-8
2017.00
0.61
2016.39
HA-9
2014.00
1.07
2012.93
Table 3-3: Summary of depths to bedrock obtained from hand auger borings and
piezometer installations. The elevation of the top of bedrock at each location was
determined by subtracting the depth to bedrock from the local ground surface elevation.
54
Figure 3-1: Photo of the typical transition between the meadow and the surrounding hill
slopes.
55
Figure 3-2: Photo of the setup of a seismic survey conducted in the meadow.
56
Figure 3-3: Photo of the geophysical sledgehammer and strike plate method. This method
was used to generate sound waves in the subsurface during seismic surveys.
57
TMS-1S
TMS-5N
Figure 3-4: Locations of seismic surveys conducted in the meadow. Note the
concentration of surveys in the central portion of the meadow where sediment was
perceived to be the greatest in depth.
58
Distance
(feet)
0
5
10
20
30
40
50
60
70
80
90
100
105
110
Time
(mS)
TMS2N
0
5.25
9.5
16.5
19
19.5
21.75
24
25.25
26
27.75
28
Time
(mS)
TMS2S
30.5
29.5
28
27
25
22.75
21.25
19.75
18
15.75
9.75
4.75
0
29.5
Error Between Final Travel Times
of N and S Surveys
Determined to be a Valid Survey
3.28%
TMS 2
35
V2=1693 m/s
30
V2=2031 m/s
Time (mS)
25
20 V1=375 m/s
V1=313 m/s
Survey N
Survey S
15
10
Xc= 17 ft
Xc= 20 ft
5
0
0
10
20
30
40
50
60
70
80
90
100
110
Distance (ft)
Figure 3-5: Example of a seismic reduction worksheet used in calculating meadow
sediment depths.
59
Figure 3-6: Locations of soil boring advancements and piezometer installations
conducted in the meadow and adjacent hillslopes.
60
Figure 3-7: Photo of a typical hand auger boring advanced in the meadow.
61
Figure 3-8: Depiction of the meadow extent in relation to the surrounding topography.
Surficial topography was obtained from the USGS digital elevation model (DEM) for the
Loon Lake 7.5-minute quadrangle.
62
Figure 3-9: Surface topography within the meadow perimeter. Surficial topography was
obtained from the USGS digital elevation model (DEM) for the Loon Lake 7.5-minute
quadrangle.
63
2.62
Figure 3-10: Control points of meadow sediment thicknesses used for subsurface
interpolation. Depths from seismic surveys were calculated using equations for a dipping
interface between the unconsolidated meadow sediments and the underlying bedrock.
64
Figure 3-11: Topographic map of top of bedrock elevations in the meadow subsurface.
Elevations were determined based on interpolation of the control points shown in Figure
3-10. Interpolation was performed utilizing ArcGIS software.
65
Figure 3-12: Isopach map of sediment thicknesses throughout the meadow.
66
Chapter 4
HYDROLOGIC CHARACTERIZATION
4.1 Introduction
Mountain meadows exhibit dynamic hydrologic environments that are often
reflected in changes in the depth of the water table (Hill, 1990). The depth to the water
table can be correlated directly to the amount of available subsurface groundwater storage
in the meadow (De Vries and Simmers, 2002). Water table monitoring was conducted
over the course of the 2008-2009 water year to determine seasonal hydrologic changes in
the meadow and to provide calibration data for the groundwater flow model. Monitoring
the water table for this time period provided a baseline for existing meadow conditions to
be used as a comparison for model simulations. Meadow specific lithologic and
hydrologic properties of the subsurface sediment were also determined for use in
groundwater storage calculations and to provide constraints of key inputs into the
groundwater flow model. This chapter summarizes the methods and results associated
with the determining the hydrologic conditions of the meadow during the course of this
study.
4.2 Methods
Shallow piezometer installation
Four shallow piezometers (PZ-1 through PZ-4) were installed in the meadow to
monitor the shallow water table in late September, 2008, prior to the beginning of the
2008-2009 water year on October 1, 2008. The piezometers were constructed using 2.5
cm diameter galvanized steel pipe. The pipe was drilled with a series of 0.65 cm
67
diameter holes, spaced approximately every 30 cm, along the portion of each pipe that
was advanced into the subsurface. The holes were drilled to allow groundwater to freely
flow through the pipe following installation. Piezometers were advanced manually using
a posthole driver until bedrock was encountered. The typical installation of a piezometer
(example is PZ-1) is included as Figure 4-1.
Each piezometer was developed following installation using a peristaltic pump
equipped with a flow regulator. The purpose of development was to remove any fine
grained material that may have collected inside the pipe during installation. The pump
was regulated to a relatively low flow rate (approximately 500 mL/min) to limit the
potential for additional formational sediment to be drawn into the pipe. Each piezometer
was purged until the extracted groundwater appeared to be free of fines and of low
overall turbidity.
The top of each piezometer was surveyed using a Leica TOTAL Surveying
Station. The purpose of this exercise was to obtain piezometer elevations that were
significantly more accurate (cm scale) than using elevations obtained from the DEM (m
scale) at the location of each piezometer. Utilizing accurate elevations at these points
becomes increasingly important when determining the direction and magnitude of the
groundwater gradient in the meadow subsurface. Surveying was conducted using a fixed
reference benchmark (base of a large boulder) within the meadow. The elevation of the
benchmark was obtained from the DEM, which was determined to be the most accurate
method of establishing a reference elevation for comparison of the surveyed piezometers.
Piezometer locations PZ-1 and PZ-4 were selected to provide water table
68
monitoring points near the inflow and outflow areas of the meadow, respectively.
Piezometer locations PZ-2 and PZ-3 were selected to provide points within the central
portion of the meadow to provide representative monitoring of the depth to water
throughout the meadow. Piezometer locations are shown on Figure 4-2. The total depth
of the piezometers below ground surface ranged from approximately 1.2 m (PZ-4) to 4.2
m (PZ-1). Construction details and elevations of each piezometer are included in Table
4-1.
Monitoring of hydraulic head
Following installation of each piezometer, unvented Solinst LevelLogger pressure
transducers were suspended within each pipe to log the pressure head at discrete intervals
over the course of the 2008-2009 water year (approximately one year).
Prior to
installation, the transducers were programmed to record the pressure in the water column
at 15-minute intervals. At the time of installation, each transducer was lowered into the
pipe to the point that they were completely submerged in the water column. As the
pressure transducers were unvented to the atmosphere, a barometric pressure transducer
was installed within the meadow boundaries to record ambient air pressure for barometric
correction. The barometric transducer was clipped to a tree branch in the vicinity of PZ-3
to ensure that it was not tampered with or damaged during monitoring. The barometric
transducer was also pre-programmed to record air pressure at 15-minute intervals.
Monitoring was completed in October 2009 and data were downloaded and processed
using manufacturer provided software. Daily 15-minute data were grouped and the mean
was calculated for each of the piezometers as well as the barometric pressure.
69
During numerous field visits to the meadow, the depth to water was measured
manually in each piezometer using a Solinst electric water level meter. The depth to
water was measured from a reference point marked on the top of each pipe, and the date,
time, and depth to water were recorded. Depths to water obtained from these visits were
subsequently compared to the calculated depth to water from the daily mean transducer
readings, corrected for barometric pressure.
Advancement of soil borings and collection of soil samples
As previously discussed in Chapter 3, soil borings were advanced in the meadow
for two purposes, the first being to determine the depth to bedrock for sediment volume
calculations. The second purpose was to visually determine the stratigraphy of the
meadow subsurface and to collect soil samples for offsite analysis. A total of eight
borings (HA-1 through HA-3 and HA-5 through HA-9) were manually advanced using a
hand auger equipped with a 9 cm diameter bucket approximately 30 cm in length. The
locations of the borings are shown on Figure 4-2. Borings HA-2 and HA-3 were
advanced directly adjacent to PZ-2 and PZ-3 to determine the lithology in this locality of
the meadow as these two monitoring points were designed to establish representative
conditions of groundwater fluctuations in the meadow. The remaining six borings were
distributed through out the meadow to determine any variability in the nature and
distribution of the subsurface sediment.
During boring advancement, continuous samples were collected directly from the
hand auger bit. Soil samples collected by this method were disturbed and primarily used
to log the stratigraphy of the subsurface visually and to collect bulk samples for offsite
70
grain size analysis (discussed in a later section of this Chapter). Soil samples were
visually logged in the field in general accordance with the methods outlined in ASTM D
2488-06 (ASTM, 2006). Soil properties logged included color variations, approximate
moisture content, estimated relative density, estimated grain size distribution, and any
additional observations such as the presence of organics. A total of 23 bulk samples were
collected from select intervals from each boring. The selection of these samples was
based on field observations of significant changes in grain size distribution and physical
properties. These bulk samples were commonly collected over intervals of similar
characteristics that were less than 30 cm (the length of the hand auger bucket), however
similar characteristics were occasionally encountered over intervals up to 100 cm. In
these cases, samples were homogenized in the field in an effort to provide a sample
representative of the entire interval.
Disturbed samples such as bulk samples are useful for visual logging and for
offsite analysis of grain size distribution but are significantly limited for analyses
requiring that the samples be undisturbed and the configuration of the pores between
grains be maintained during sampling, such as specific yield analysis (Johnson, 1967). In
an effort to collect samples suitable for this analysis (discussed in a later section of this
Chapter), a split-spoon sampler was attached to the end of the hand auger rod and
advanced ahead of the actual auger bucket.
Utilization of this attachment allowed for
collection of relatively undisturbed soil samples in stainless steel tubes measuring 15 cm
in length and 3.8 cm in diameter. This method proved to be severely limited in the
meadow as in many of the boreholes, undisturbed soil samples could not be collected
71
from below the water table because the soil lacked the necessary cohesion and would not
remain suspended in the tube. A total of two split-spoon samples were collected and
capped on both ends and was kept upright for transport to an offsite soils lab for analysis.
Soil samples were visually logged using similar procedure outlined for the hand auger
borings.
Laboratory Grain Size Analysis and Classification
Bulk soil samples collected from the hand auger borings were analyzed in an
offsite soils lab for grain size distribution. Determination of this soil property allowed for
greater refinement in the distribution and classification of the subsurface sediment. Grain
size analysis was conducted using sieving techniques in general accordance with the
methods detailed in ASTM D 422-07 (ASTM, 2007) with the notable exception that the
amounts of clay and silt were not individually separated out from the sample using a
hydrometer but rather grouped a whole under the size range of “fines”. Prior to sieving,
each sample was dried in an oven at a constant temperature of 38 oC for a minimum of 24
hours. Samples were then weighed utilizing a scale accurate to 0.01 g to determine the
initial weight of the entire sample. The sample was then placed onto a stack of sieves
arranged from largest to smallest beginning with a #4 mesh followed by, #10 mesh, #40
mesh, and finally a #200 mesh. This series of sieves was selected as each individual
sieve defines the division of gravel, coarse to fine sand, and fines as noted in the ASTM
method. After the stack was assembled, the sieves were placed on a mechanical Rototap
(sieve shaker) for 10 minutes. The samples from each sieve were then weighed and
totaled. To ensure any sample loss during sieving was minimized, the final weight of
72
selected sieved samples were totaled and compared with the initial weight. An error of
less than 2% was considered acceptable. The mass of the sample portion retained on
each sieve was subsequently plotted by which the mean diameter of the sample (D50)
could be determined.
The results of the D50 analysis were used to classify the soil samples according to
the ASTM method, i.e. a sample with a D50 value of 0.40 mm was classified as a fine
grained sand. Samples were also classified based on the USDA Soil Textural Triangle
available online at http://soils.usda.gov and the United Soil Classification System (USCS)
in accordance with ASTM D 2488-06 (ASTM, 2006). The purpose of using this
additional classification method was for comparison of meadow specific hydrologic
parameters with those determined by other researchers that elected to use the USDA
method as their preferred or only method of classification. The USDA method does not
account for any gravel sized grains as part of the classification but is solely based on the
percentages of sands, silts, and clays. To account for this difference, the portion of each
sample that was determined to be of the gravel size range by the ASTM method was
separated out and the total and relative percentages of the reminder of the sample was
recalculated.
Using the USDA classification method as part of this study was limited in that the
percentages of clays and slits were not individually determined as part of grain size
analysis, which is typically needed to use this method. Therefore, the classification was
conducted using only the percent sand. This allowed for determination of a range of
classifications based on only this grain size component. Results of these classifications
73
are discussed in the following section.
Determination of Hydraulic Conductivity
Slug tests were conducted in piezometers PZ-2 and PZ-3 to quantify the hydraulic
conductivity of the surrounding meadow sediments. The method for this test follows the
guidelines described by Hvorslev (1951) for an unconfined aquifer and a fully penetrating
piezometer. This method compares the instantaneous change from static water level and
the subsequent recovery of the water column to time using the following equation:
K
r 2 ln Le / R 
2 Le t 37
where:
K
r
R
Le
t37
= hydraulic conductivity (cm/s);
= radius of the piezometer or well (cm);
= radius of the screened interval, including sand pack (cm);
= length of screened interval (cm); and
= duration of time for water level to rise or fall to 37% of the initial change (s)
Since the piezometers did not have a sand pack installed, r and R were input as
the same value. Due to the assumed permeable nature of the subsurface sediment in the
meadow, groundwater levels were monitored during testing using a pressure transducer
that was pre-programmed to record at 0.5-second intervals. The instantaneous change in
static water level was induced by rapidly lowering a 1.2 cm diameter metal slug into the
water column.
The data were plotted graphically to determine the relationship of
drawdown over time and ultimately determine the hydraulic conductivity of the
sediments in the vicinity of the piezometer.
Although slug testing can be an effective method for determining the hydraulic
74
conductivity of sediment, there were only two locations to perform this testing within the
meadow. Given this limitation, a USDA operated software program known as Rosetta
Lite v. 1.1 was researched for values of conductivity for comparison. This software uses
hierarchical pedotransfer functions to report values of the saturated conductivity of
various soil types with varying properties (Schaap, et al., 2001). The program allows the
user to query a database of peer reviewed values using information as general as the
USDA classification of the soil. The hierarchal design of the program allows for
additional known inputs to added to the query (bulk density, soil moisture retention
characteristics, etc…) to increase the degree of confidence in the reported value (Schaap
and Bouten, 1996 and Schaap et al., 1998). The conductivity values are calculated and
input into the database based on modeled relationships between the soil water content at
various pressure heads and the saturated hydraulic conductivity of a medium as described
by van Genuchten (1980).
The results of USDA soil classification ranges for the
meadow sediment were input in the software and results were compared with values
obtained from slug testing. Additionally, the reported values from both methods were
compared with expected ranges of hydraulic conductivity based on soil type as cited by
Fetter, 2001.
Determination of Specific Yield
Specific yield is defined as the ratio of the volume of water that a saturated soil
will yield by gravity to the total volume of soil (Johnson, 1967). Determination of this
variable is a key component of this study as it describes the amount of water that is
capable of being stored in the meadow sediment that is not retained under capillary forces
75
between individual grains but rather is available for release from storage as described by
Fetter (2001).
Gravity drainage methods described by Johnson (1967) were used as a basis for
determining the specific yield of the meadow sediment. As previously discussed, the
accurate and representative determinations of this variable require soil samples that are
undisturbed. Two such samples were collected in sleeves using a split-spoon attachment
from hand auger boring HA-9 at intervals of 46 to 61 cm and 76 to 91 cm. These
samples were transported to an offsite soils laboratory operated by the USGS at the
Sacramento State University campus. Each sample was initially placed vertically in
shallow water pan and sealed in a vacuum chamber for a period of approximately 24
hours. The purpose of utilizing the vacuum chamber was to ensure complete saturation of
the sample. Prior to saturation, the total sample volume was calculated for each sample
in units of cm3 using laboratory grade calipers to determine the length and radius of the
cylindrical sample sleeve.
Immediately following saturation, the sample was removed from the vacuum
chamber and placed on a scale to determine the initial saturated weight of the soil.
Samples were then inverted and suspended using a metal calipers attached to vertical
steel pole with a weighted base to allow unrestricted gravity drainage. The suspended
samples were placed in a sealed cooler along with several saturated toilettes as well as
pans of water in an effort to maintain elevated levels of relative humidity and minimize
evaporation. A moistened piece of gauze was placed over the top exposed end of each
sample to minimize potential surface evaporation. Each sample was weighed (in g)
76
periodically with a greater frequency of measurements obtained during the first 24 hours
of drainage. The incremental and cumulative drainage was determined following each
measurement by subtracting the current weight of the sample from the initial saturated
weight. The change in volumetric water content from initial saturation was determined
by dividing the cumulative drainage (in g) by the total volume of the sample (in cm3).
The total change in volumetric water content is the specific yield of the sample.
As was found with slug testing to determine the hydraulic conductivity of the
meadow sediments, the number of meadow specific points to calculate specific yield was
limited. Therefore, additional sources of specific yield values were researched based on
the classification of the meadow sediments. In addition to providing values of saturated
hydraulic conductivity, the Rosetta Lite v. 1.1 software operated by the USDA also
provides parameters to determine the soil moisture retention curve based on the following
equation developed by van Genuchten (1980):
  r 
 s   r 
1  hn m
where: 
 soil water content (cm3/cm3);
r residual water content (cm3/cm3);
s saturated water content (cm3/cm3);
h = pressure head (cm);
and n are curve fitting parameters provided from the software; and
m = 1 - 1/n
The values of the saturated and residual water content and the curve fitting parameters
were obtained from the software by inputting the USDA soil classification of the meadow
soils. The soil water content was plotted as a function of h and the specific yield
77
equivalent was determined by subtracting the soil water content at pressure h from the
saturated water content. It should be noted that the saturated water content (s) is the
equivalent of total porosity, a common variable in hydrological studies, and the residual
water content (r) is often referenced as the point at which vegetation is no longer able to
extract water from the soil pores and begins to deteriorate. This value is commonly
referred to in soil studies as the wilting point and the equivalent pressure head (h) at this
point is referenced as -15,000 cm (van Genuchten, 1980).
These values were compared
with the results of the laboratory analysis of specific yield as well as compiled values by
Johnson (1967) based on common soil types analyzed for specific yield worldwide.
4.3 Results
Monitoring of hydraulic head
The pressure transducers functioned as intended, recording pressure readings in
each of the four piezometers at 15 minute intervals from the time period of September 19,
2008 to October 17, 2009. The barometric pressure transducer that was installed in the
meadow was programmed to record barometric pressure at the same frequency to correct
the groundwater levels for atmospheric fluctuations. However, following the download of
the recorded data, it was determined that elevation of the placement of the transducer was
set prior to installation at mean sea level (msl), when in actuality it should have been set
at the elevation of the meadow (2016 m above msl). This oversight resulted in the entire
data set of barometric readings being found to be not representative of meadow
conditions (all values were actually negative) and being discarded.
Fortunately, this problem was rectified by utilizing barometric pressure readings
78
obtained as part of a concurrent study conducted by the Sacramento State Department of
Geology at Bear Meadow. As shown on Table 2-1, this meadow is located approximately
52 km to the northwest of the meadow at an elevation of 1560 m above msl. As shown on
Table 4-2, the error between transducer readings uncorrected for barometric pressure and
manual electric readings was up to 1 m in some instances. Using the barometric
correction from Bear Meadow resulted in a significant decrease in the error (typically less
than 5 cm). As shown on Figure 4-3, using uncorrected transducer readings over the time
scale of this study results in a significant overestimation of groundwater elevations.
Hydraulic head fluctuations
The results of the groundwater elevations (corrected for barometric pressure) in
the four piezometers from the period of September 29, 2008 to October 17, 2009 are
plotted on Figure 4-4. As shown on the figure, groundwater elevations recorded for in
PZ-1 (near the inflow area of the meadow) are typically 6 to 7 m higher than those for
PZ-4 (near the outflow area of the meadow).
The distance between these two
piezometers is approximately 420 m which results in an approximate groundwater
hydraulic gradient of 0.015 which is slightly lower than the topographical gradient of the
meadow of 2%. Piezometers PZ-2 and PZ-3 generally show the same overall trend of
elevations with slightly higher elevations noted in PZ-2. This results in an overall
groundwater flow direction to the southwest with a minor component of flow to the
southeast.
Figure 4-5 depicts the depth to water as measured from the meadow surface in
piezometers PZ-2 and PZ-3. The overall trend is consistent for both monitoring points
79
with groundwater levels near ground surface for much of the late fall through late spring.
Water levels begin to drop in both piezometers beginning in early June 2009 and reach
depths of approximately 1 m below land surface by the end of September 2009. It is
noted that many of the depth to water measurements during monitoring were reported to
be negative or above the ground surface, which indicates surface ponding may
occasionally occur in the meadow. This was particularly noted for water levels recorded
for piezometer PZ-3. Additionally, considerable daily fluctuations were noted to
periodically occur during monitoring, primarily during the winter and early spring.
Visual classification of soils
Soil samples collected from the meadow were visually classified in the field based
on observed grain size contents. As shown on the boring logs for the eight hand auger
borings (included in Appendix B) the most common classification of meadow soil was of
sand with silt and of silt with sand. Also included in these classifications was the
occasional presence of gravel and clay. This is consistent with observations by previous
researchers as discussed in Chapter 2. From review of the boring logs, the meadow
sediment generally coarsens downward with some exceptions. Boring HA-5 is one
example of this exception where a predominately sandy interval near the surface was
underlain by intervals of silt and some clay. This occurrence was also noted in HA-1
where gravels were noted to overlie sands with silt. Overall, the sediment in the meadow
was observed to consist of poorly sorted and discontinuous intervals of sand with varying
proportions of gravels and fines. This is somewhat expected due to the assumed glacial
origin of much of the meadow sediment as described by Harden, 2004.
80
Grain Size Analysis
The data generated from sieve analysis of selected meadow soil samples are
presented on Table 4-3. This table summarizes the portion (by weight) of the sample that
was retained on each of the four sieves utilized during analysis. The analyses were all
considered of good quality based on the amount of loss from initial to final weights being
less than 2%. Table 4-4 presents the percent finer by weight for each sieve. This data
shows a wide range of values for the samples analyzed. The amount of soil passing the
no. 200 or 0.075 mm sieve (division between fine sand and fines, Table 4-5) ranged from
less than 2% in up to 62%. The amount of soil retained on the # 4 or 4.75 mm mesh
(division between gravel and coarse sand, Table 4-5) ranged from null to 77 %. The
majority of the sample grain size distribution was in the medium to fine grained sand
range as shown on the grain size distribution plots included in Appendix C.
The D50 values obtained from the plots in Appendix C and the resultant soil
classification (as defined in Table 4-5) are summarized in Table 4-6. The majority of the
23 samples analyzed were classified as fine or medium sands (USCS Group Symbol SM)
(16 samples) with the remaining samples classified as gravel (USCS Group Symbol GP)
(1 sample), silts (USCS Group Symbol ML or MH) (4 samples), and coarse sands (USCS
Group Symbol SP) (2 samples).
As discussed previously in this Chapter, samples were also classified using the
USDA Textural Triangle using only the % sand and ignoring any portion of the sample in
the gravel range. The results of this classification are summarized on Table 4-7. The
mean sand content of all samples analyzed was determined to be approximately 78 %.
81
As shown on Figure 4-6, plotting this sand percentage indicates that the soils in the
meadow subsurface range from loamy sands to sandy loams.
Hydraulic Conductivity
Slug testing in piezometers PZ-2 and PZ-3 proved to be an effective method of
estimating the conductivity of the meadow sediments. Figures 4-7 and 4-8 show the
instantaneous change in static water levels in PZ-2 and PZ-3, respectively.
It was
determined that the slug-in method, or falling head test, for both locations was the most
effective in providing instantaneous changes from static conditions. Maximum initial
displacements were approximately 35 cm for PZ-2 and 25 cm for PZ-3. Normalized
plots of the change in head over time and the duration of time required for the head to fall
to 37% of the initial change are shown on Figures 4-9 (PZ-2) and 4-10 (PZ-3). Water
level recovery to 37% of initial displacement in PZ-2 was significantly shorter in duration
than PZ-3, 9.75 s for PZ-2 and 130 s for PZ-3.
Results of the hydraulic conductivity (K) values calculated from the slug tests are
summarized on Table 4-8. K values from this method ranged from 1.92E-04 to 2.03E-03
cm/s. Also included on this table are values of K from the USDA Rosetta Lite database
for a Sandy Loam and a Loamy Sand (based on classifications of the meadow sediment
using the textural triangle). Values ranged from 4.42E-04 to 1.22E-03 cm/s from this
method. The results for this method and slug testing are within approximately one order
of magnitude of each other and the mean for all K values is 0.837 m/d or 9.69E-04 cm/s.
This value is within the range for silty sands and fine sands (1E-05 to 1E-03 cm/s) as
described by Fetter (2001).
82
Specific Yield
Results of the laboratory analysis of two samples collected from the meadow are
summarized on Table 4-9. Samples were allowed to drain over a period of 68 days upon
which the testing was stopped due to it being evident that equilibrium conditions were not
going be reached in a reasonable period of time. As shown on Figure 4-11, the changes in
volumetric water content (vwc) during the early portion of the test were fairly rapid. The
assumption that the samples would exhibit this early behavior and then “level off” in the
later portion of the test proved to be incorrect as the slope of the change in vwc appears to
be continuing downward. Johnson (1967), notes that in many cases where samples have a
significant fine grained component, the duration of the test may be highly variable
(periods of up to two years have been required in some instances for extremely fine
grained samples).
At the time of termination of the test, the specific yield was
approximately 8.8% for soil sample HA-9-46 to 61 cm and 12.5% for soil sample
HA-9-76 to 91 cm. These values are well below the average values established by
Johnson, 1967 for fine and medium sands (21% and 26%, respectively), which are the
dominant soil types in the meadow based on grain size analyses.
The results of the parameters required for the determination of soil moisture
retention curves (obtained from the Rosetta Lite database) for loamy sands and sandy
loams are summarized on Table 4-10. The saturated water content (s) is nearly the same
for both soil types and the residual water content (r) is also similar. Graphical plots of
the soil moisture retention curves for both soil types are shown on Figures 4-12 (sandy
loam) and 4-13 (loamy sand). The plots show that the soil water content decreases at a
83
variable rate depending on pressure conditions in the subsurface. The maximum potential
change in water content from total saturation to the residual saturation for both soil types
was 0.34 cm3/cm3 (Table 4-11). The maximum value was reached at an approximate
pressure head value of -15,000 cm or the wilting point. This variable is equivocal to
specific yield as it quantifies the amount of water that is extractable from the pore spaces
in soils. The values estimated from this method are significantly higher than the values
estimated by Johnson (1967) for similar soil types.
4.4 Discussion
The use of pressure transducers to monitor groundwater fluctuations near the
inflow and outflow points of the meadow and within the meadow appears to be a useful
method to conduct monitoring when access is limited. This was certainly the case with
this meadow due to the accumulated snow pack in and around the meadow that restricts
access for approximately 7 months of the year. An error in the programming of the
barometric transducer caused some initial problems in correcting groundwater elevations.
However, the pressure readings obtained from another study area proved to be effective
in providing proxy data for corrections. It should be noted that it was later determined
from the transducer manufacturer that the elevation input is critical as the barometric
transducers are highly sensitive to the elevation at which they are programmed to record.
It is also noted for future work, this input should be accurate to within a few meters, if
possible, to obtain the most accurate data.
To investigate the occasional extreme fluctuations in reported water table levels,
incremental precipitation data was plotted in conjunction with water table readings
84
(Figure 4-14). It is apparent from this graphical relationship that the extreme fluctuations
are correlated with precipitation events that often exceed 3 or 4 cm daily.
These
precipitation events also occur when the meadow is inundated by significant snow. This
probably indicates that pressure transducers may be sensitive to high runoff events, such
as rain on snow, and may temporarily record water levels that are not representative of
subsurface conditions. It is noted that the water levels during these events are not only
overestimated compared to the previous readings, but also underestimated. This probably
indicates that the transducers may temporarily record readings out of their manufacturer
calibrated range when influenced by a melting overburden snow pack. Over the course
of the monitoring, these fluctuations are a relatively minor subset of the total observations
and should not impact the conclusions of this study but may provide useful information
for consideration for future researchers.
Comparing the results of direct measurements (slug tests) of hydraulic
conductivity (K) in the meadow subsurface with empirical data appears to indicate that
the values are representative and show good repeatability. Based upon the results of
grain size analyses and visual observations, finer grained sediments are interfingered with
coarser grained sediments throughout the meadow. There does not appear to be clearly
defined contacts between soil types that would warrant using discrete values of
conductivity in modeling the flow beneath different areas of the meadow. Although the
values of K were found to range up to an order of magnitude of each other, using an
average value of K in modeling applications may mitigate the variability of the
subsurface sediment. At minimum, the average value can be considered a conservative
85
estimate as empirical values in some cases did not consider the upper and lower ends of
the grain sizes found in the meadow (gravel and clay). These extremes were examined
more thoroughly during sensitivity analysis of the model (Chapter 6).
Laboratory analysis of specific yield was determined to be ineffective for the
meadow sediments, due the length of time apparently needed to reach equilibrium. This
is due in part to the limitations of soil sampling in the meadow. As shown on Figure 4-5,
groundwater levels in the meadow are commonly within less than 1 meter of the ground
surface throughout the summer months when the meadow is accessible and free from
snow. Soil sampling beneath the water table was not effective in collecting undisturbed
samples in most cases.
It is assumed that if some of the coarser grained material
(medium and coarse sands) could have been collected properly, the duration of specific
yield analysis may have been significantly shorter. Another limiting factor is the poorly
sorted nature of the meadow sediments as Johnson (1967) cites that well sorted sediments
commonly drain up to 50% of the total drainage in the first hour of testing.
Based upon the limitations of laboratory analysis discussed above, empirical data
from gravity drainage analyses as well as soil moisture retention characteristics were used
in conjunction with grain size analyses to determine an appropriate range of values for
the meadow sediment. Utilizing gravity drainage to determine specific yield is a well
established method of determining the amount of water that is available to be released
from storage (Fetter, 2001). However, this method assumes that soils will drain to the
extent that capillary forces will not inhibit additional drainage regardless of other factors
such as soil moisture use by plants (van Genuchten, 1980).
86
Results of the soil moisture retention curve analysis appears to indicate that
specific yield estimates have the potential to be significantly underestimated and soils
found in the meadow may have a much greater potential to release water from storage
beyond the limits of capillary forces. This however assumes that the pressure potential
near the wilting point are reached in the meadow. Based upon subsurface observations
during field visits, the soils throughout the meadow appear to be moist even in the middle
of summer (see boring logs in Appendix B). This would indicate that the wilting point is
not reached in the meadow and that pressure potentials in the subsurface are significantly
less than this extreme. As shown on Figures 4-12 and 4-13, the water content decreases
as negative pressure potentials increase. Since the water table levels in the meadow reach
a maximum depth of approximately 1 m below surface, it is assumed that the maximum
negative pressure in the subsurface soils is -100 cm. The results of the specific yield
estimates using these constraints are summarized on Table 4-12. The reported saturated
water content (total porosity) is also shown on this table. The average value for specific
yield from the above analyses and reported values from Johnson (1967) was 21%. This
value was used in all subsurface storage calculations and as in initial input into the model.
Comparing this value with the total porosity (39%) indicates that approximately half of
the total pores in the meadow sediments will drain under the influence of gravity, while
the reminder of the pores appear to retain water. This is based on hydrologic conditions
observed for a single water year and this value may have the potential to be lower or
higher in future years depending on meadow conditions. As with hydraulic conductivity,
the extremes of possible specific yield values were investigated as part of model
87
sensitivity analysis.
Combining the results of specific yield analysis the three-dimensional
configuration of the meadow discussed in Chapter 3 results in a groundwater storage
capacity of 27,000 m3 or 22 acre-ft when the meadow sediments are fully saturated.
Figure 4-15 depicts the average depth to water in the meadow based on monitoring data
from piezometers PZ-2 and PZ-3. The purpose of averaging the water table levels from
these two monitoring points was to define the baseline conditions for water table levels
throughout the meadow for change in storage calculations. This assumption appears to
be justified based on the meadow hydrologic conditions observed during numerous field
visits as well as the water table levels encountered during various subsurface
investigation activities.
Based on the average of these measurements, the meadow is fully saturated for
much of the year, likely from significant runoff/recharge from precipitation and
snowmelt.
This indicates that any potential water loss from lodgepole pine tree
transpiration is exceeded by recharge for much of the year.
From review of the
monitoring data, the beginning of the decline in meadow water table levels during the
2008-2009 water year was found to be on June 5, 2009 (Figure 4-15). The meadow water
table reached a maximum depth of 1.13 m below land surface on September 28, 2009.
This time period was used as the basis for determining transpiration estimates of
lodgepole pine trees (discussed in Chapter 5).
88
4.5 Tables and Figures
Piezometer ID
Elevation of Top
of Pipe (m)
Top of Pipe to
Ground
Surface (m)
Total Depth*
(m)
Screened
Interval (m)
PZ-1
2020.61
0.42
4.27
0.42 to 4.27
PZ-2
2016.70
0.93
3.05
0.93 to 3.05
PZ-3
2015.64
0.22
1.83
0.22 to 1.83
PZ-4
2012.85
0.29
1.52
0.29 to 1.52
Table 4-1: Summary of piezometer construction details and elevations. * - The total
depth of each piezometer was measured from the top of the pipe to the bottom of the
piezometer.
89
Piezometer
ID
PZ-1
PZ-2
PZ-3
PZ-4
Date
Electronic
Sounding
DTW (from
top of Pipe)
Uncorrected
Transducer
DTW (from
top of Pipe)
Difference
Corrected
Transducer
DTW (from
top of Pipe)
Difference
10/26/08
2.04
1.02
1.02
1.92
0.12
06/13/09
1.69
0.86
0.83
1.67
0.01
07/10/09
1.85
1.05
0.80
1.90
-0.05
07/24/09
1.91
1.16
0.75
2.02
-0.10
08/21/09
2.04
1.14
0.90
2.01
0.03
10/17/09
1.90
1.08
0.82
1.94
-0.05
10/26/08
1.55
0.59
0.96
1.49
0.06
06/13/09
0.95
0.21
0.74
1.02
-0.07
07/10/09
1.30
0.42
0.88
1.28
0.03
07/24/09
1.50
0.55
0.95
1.41
0.10
08/21/09
1.85
0.89
0.96
1.76
0.09
10/17/09
1.16
0.21
0.95
1.08
0.08
10/26/08
0.58
-0.46
1.04
0.44
0.14
06/13/09
0.22
-0.54
0.76
0.27
-0.05
07/10/09
0.48
-0.28
0.75
0.58
-0.10
07/24/09
0.73
-0.12
0.85
0.74
-0.01
08/21/09
0.95
0.15
0.80
1.02
-0.07
10/17/09
0.24
-0.50
0.73
0.37
-0.13
10/26/08
1.17
0.09
1.07
0.99
0.18
06/13/09
0.73
0.09
0.65
0.90
-0.17
07/10/09
0.95
0.21
0.74
1.07
-0.12
07/24/09
1.14
0.29
0.85
1.15
-0.01
08/21/09
1.41
0.48
0.93
1.35
0.06
10/17/09
1.08
0.16
0.92
1.02
0.05
(All Measurements in Meters)
Table 4-2: Summary of depth to water measurements based on manual electronic and
recorded transducer readings. Transducer readings corrected for barometric pressure were
determined to significantly reduce the differences in manual and recorded readings.
90
Sample ID
Borehole
Initial
Weight
(g)
Weight Retained by Sieve Size (g)
Final
Weight
(g)
Final Weight
of Sands and
Fines Only (g)
% Error (Loss
of Initial
Sample)
(Initial Weight Final
Weight)/Initial
Weight
Depth
(cm)
Prior to
Sieving
4 Mesh
no. 10
no. 40
no. 200
Base Pan
Following
Sieving
Following
Sieving
0-46
65.43
1.04
5.04
20.71
27.68
10.89
65.36
64.32
0.11
46-76
52.52
4.72
20.91
18.22
8.52
52.37
52.37
0.29
76-137
196.30
7.48
17.34
68.52
74.09
28.47
195.90
188.42
0.20
137-168
202.12
20.19
15.53
65.68
71.26
27.07
199.73
179.54
1.18
168-198
--
1.78
7.96
41.56
48.60
15.06
114.96
113.18
--
198-229
--
10.32
37.28
91.42
57.85
15.92
212.79
202.47
--
0-46
42.36
0.68
17.01
24.28
41.97
41.97
0.92
46-91
71.09
0.03
2.88
29.31
37.90
70.12
70.12
1.36
91-107
--
16.15
15.11
39.49
35.74
8.76
115.25
99.10
--
107-122
--
152.84
41.77
89.63
45.70
8.64
338.58
185.74
--
0-55
--
24.90
2.45
14.90
18.94
14.40
75.59
50.69
--
55-76
202.88
55.34
19.32
54.79
54.24
19.09
202.78
147.44
0.05
107-122
--
115.18
32.45
75.61
72.07
22.86
318.17
202.99
--
55-152
--
0.34
4.08
29.90
38.75
73.07
73.07
--
HA-1
HA-2
HA-3
HA-5
Table 4-3: Summary of grain size analysis data and error analysis.
91
Sample ID
Initial
Weight
(g)
Weight Retained by Sieve Size (g)
Final
Weight
(g)
Final Weight
of Sands and
Fines Only (g)
% Error (Loss
of Initial
Sample)
(Initial Weight Final
Weight)/Initial
Weight
Depth
(cm)
Prior to
Sieving
4 Mesh
no. 10
no. 40
no. 200
Base Pan
Following
Sieving
Following
Sieving
76-98
--
0.32
0.48
2.23
19.42
36.66
59.11
58.79
--
98-122
321.14
83.91
40.22
95.44
79.97
21.21
320.75
236.84
0.12
122-152
--
348.59
30.90
41.83
23.33
8.04
452.69
104.10
--
152-168
--
45.26
38.73
95.10
92.26
30.82
302.17
256.91
--
168-198
--
39.76
88.37
165.45
73.91
14.99
382.48
342.72
--
198-244
--
1.85
3.88
11.42
35.97
34.52
87.64
85.79
--
HA-7
61-76
--
0.52
6.34
15.90
31.51
6.50
60.77
60.25
--
HA-8
46-61
--
160.92
40.47
69.17
49.96
25.49
346.01
185.09
--
HA-9
91-107
--
3.27
9.39
85.25
99.57
39.14
236.62
233.35
--
Borehole
HA-6
Table 4-3 (Continued)
92
Sample ID
% Finer by Weight
Depth
(cm)
4 Mesh
(4.75
mm)
no. 10
(2.00
mm)
no. 40
(0.425
mm)
no. 200
(0.075
mm)
0-46
98.41
90.70
59.01
16.66
46-76
100.00
90.99
51.06
16.27
76-137
96.18
87.33
52.35
14.53
137-168
89.89
82.12
49.23
13.55
168-198
98.45
91.53
55.38
13.10
198-229
95.15
77.63
34.67
7.48
0-46
100.00
100.00
98.38
57.85
46-91
100.00
99.96
95.85
54.05
91-107
85.99
72.88
38.61
7.60
107-122
54.86
42.52
16.05
2.55
0-55
67.06
63.82
44.11
19.05
55-76
72.71
63.18
36.16
9.41
107-122
63.80
53.60
29.84
7.18
55-152
100.00
99.53
93.95
53.03
76-98
99.46
98.65
94.87
62.02
98-122
73.84
61.30
31.54
6.61
122-152
23.00
16.17
6.93
1.78
152-168
85.02
72.20
40.73
10.20
168-198
89.60
66.50
23.24
3.92
198-244
97.89
93.46
80.43
39.39
HA-7
61-76
99.14
88.71
62.55
10.70
HA-8
46-61
53.49
41.80
21.81
7.37
HA-9
91-107
98.62
94.65
58.62
16.54
Borehole
HA-1
HA-2
HA-3
HA-5
HA-6
Table 4-4: Percent finer by weight results based on grain size analysis.
93
Size Class
Grain Size Range (mm)
Gravel
> 4.75
Coarse Sand
4.75 to 2.00
Medium Sand
2.00 to 0.425
Fine Sand
0.425 to 0.075
Fines (Silts and Clays)
< 0.075
Table 4-5: Grain size classification as defined by ASTM D 2488-06. These grain size
ranges were utilized to classify the soil type of each sample based on D50 values
summarized in Table 4-6.
94
D50 Value
Size Class (2)
Depth
(cm)
Grain Size
(mm)
ASTM D 2488-06
0-46
0.29
Fine Sand
46-76
0.40
Fine Sand
76-137
0.38
Fine Sand
137-168
0.44
Medium Sand
168-198
0.35
Fine Sand
198-229
0.78
Medium Sand
0-46
0.055 (1)
Silt
46-91
0.065 (1)
Silt
91-107
0.71
Medium Sand
107-122
3.3
Coarse Sand
0-55
0.65
Medium Sand
55-76
0.95
Medium Sand
107-122
1.6
Medium Sand
55-152
0.068 (1)
Silt
76-98
0.040 (1)
Silt
98-122
1.2
Medium Sand
122-152
50 (1)
Gravel
152-168
0.69
Medium Sand
168-198
1.3
Medium Sand
198-244
0.12
Fine Sand
HA-7
61-76
0.27
Fine Sand
HA-8
46-61
3.7
Coarse Sand
HA-9
91-107
0.30
Fine Sand
Sample ID
Borehole
HA-1
HA-2
HA-3
HA-5
HA-6
(1) = Extrapolated D50 Value
(2) = Based on D50 Value
Table 4-6: D50 values obtained from grain size analysis results and ASTM classification
of soil type. Graphical plots used to determine the D50 values are included in Appendix
C.
95
Sample ID
Borehole
HA-1
HA-2
HA-3
HA-5
% Sands and Fines (excluding
Gravel from Total Sample
Weight)
USDA Soil Classification
Range
Depth (cm)
% Sand (4.75
to 0.075 mm)
% Fines (<0.075
mm)
Based on % Sand Only
0-46
83.07
16.93
Loamy Sand to Sandy Loam
46-76
83.73
16.27
Loamy Sand to Sandy Loam
76-137
84.89
15.11
Loamy Sand to Sandy Loam
137-168
84.92
15.08
Loamy Sand to Sandy Loam
168-198
86.69
13.31
Loamy Sand to Sand
198-229
92.14
7.86
Sand
0-46
42.15
57.85
Silt Loam to Clay
46-91
45.95
54.05
Sandy Loam to Sandy Clay
91-107
91.16
8.84
Loamy Sand to Sand
107-122
95.35
4.65
0-55
71.59
28.41
55-76
87.05
12.95
Sand
Loamy Sand to Sandy Clay
Loam
Loamy Sand to Sand
107-122
88.74
11.26
Loamy Sand to Sand
55-152
46.97
53.03
Silt Loam to Sandy Clay
76-98
37.64
62.36
Silt Loam to Clay
98-122
91.04
8.96
Loamy Sand to Sand
122-152
92.28
7.72
Loamy Sand to Sand
152-168
88.00
12.00
Loamy Sand to Sand
168-198
95.63
4.37
Sand
198-244
59.76
40.24
Sandy Loam to Sandy Clay
HA-7
61-76
89.21
10.79
Loamy Sand to Sand
HA-8
46-61
86.23
13.77
Loamy Sand to Sandy Loam
HA-9
91-107
83.23
16.77
Loamy Sand to Sandy Loam
MIN
37.64
4.37
Silt Loam to Clay
MAX
95.63
62.36
Sand
MEAN
78.58
21.42
Loamy Sand to Sandy Loam
HA-6
Table 4-7: Classification of soil samples based on the UDSA Textural Triangle. As the
percent of silts and clays were not separated out from each sample, the use of only the
percent sand resulted in a range of soil types.
96
Data Source
K (m/d)
K (cm/s)
Pedo-Transfer Function
from USDA Rosetta Lite v.
1.1 (Sandy Loam)
3.82E-01
4.42E-04
Pedo-Transfer Function
from USDA Rosetta Lite v.
1.1 (Loamy Sand)
1.05E+00
1.22E-03
Slug Testing PZ-2
1.75E+00
2.03E-03
Slug Testing PZ-3
1.66E-01
1.92E-04
Mean
8.37E-01
9.69E-04
Table 4-8: Summary of hydraulic conductivity (K) values of the meadow sediments.
Multiple methods were used to determine this variable including slug testing and a
pedo-transfer function developed by the USDA.
97
Sample ID
Borehole
Depth
(cm)
HA-9
46-61
Brass Sleeve
Receptacle #1
Total
Sample
Volume
(cm3)
155.72
Initial
Saturated
Weight (g)
Weight of
Sample with
Retained
Water (g)
Incremental
Amount of
Drained
Water (g)
Cumulative
Drainage (g)
Calculated
Specific Yield
(cm3/cm3)
Elapsed
Time
(days)
Includes soil
sample and
sleeve
Includes soil
sample and
sleeve
Previous wt.
- Current wt.
of retained
water
sample
From initial
saturated
weight
Cumulative
Drainage/Total
Sample Volume
0.00
341.86
--
0.00
0.00
0.0%
0.03
--
340.78
1.08
1.08
0.7%
1.04
--
340.11
0.67
1.75
1.1%
2.03
--
339.42
0.69
2.44
1.6%
5.95
--
338.36
1.06
3.50
2.2%
8.06
--
337.37
0.99
4.49
2.9%
15.99
335.12
2.25
6.74
4.3%
23.06
333.60
1.52
8.26
5.3%
43.06
331.14
2.46
10.72
6.9%
68.06
328.17
2.97
13.69
8.8%
Date, Time, and Elapsed
Time of Measurement
Date and
Time
1/13/10
12:10
1/13/10
12:50
1/14/10
13:10
1/15/10
13:00
1/19/10
11:00
1/21/10
13:30
1/29/10
12:00
2/5/10 13:30
2/25/10
13:30
3/22/10
13:30
Table 4-9: Results of specific yield laboratory analysis.
98
Sample ID
Borehole
Depth
(cm)
HA-9
76-91
Date and
Time
1/13/10
12:20
1/13/10
12:45
1/14/10
13:20
1/15/10
13:10
1/19/10
11:00
1/21/10
13:30
1/29/10
13:30
Plastic Sleeve
Receptacle #2
Total
Sample
Volume
(cm3)
Initial
Saturated
Weight (g)
Weight of
Sample with
Retained
Water (g)
Incremental
Amount of
Drained
Water (g)
Cumulative
Drainage (g)
Calculated
Specific Yield
(cm3/cm3)
Elapsed
Time
(days)
Includes soil
sample and
sleeve
Includes soil
sample and
sleeve
Previous wt.
- Current wt.
of retained
water
sample
From initial
saturated
weight
Cumulative
Drainage/Total
Sample Volume
0.00
266.15
--
0.00
0.00
0.0%
264.55
1.60
1.60
1.2%
Date, Time, and Elapsed
Time of Measurement
129.29
2/5/10 13:30
2/25/10
13:30
3/22/10
13:30
Table 4-9 (Continued)
0.02
1.04
--
262.36
2.19
3.79
2.9%
2.03
--
261.34
1.02
4.81
3.7%
5.94
--
259.27
2.07
6.88
5.3%
8.05
--
258.67
0.60
7.48
5.8%
16.05
256.68
1.99
9.47
7.3%
23.05
255.42
1.26
10.73
8.3%
43.05
253.06
2.36
13.09
10.1%
68.05
250.05
3.01
16.10
12.5%
99
van-Genuchten parameters
Soil Type
theta r
3
3
theta s
3
3
alpha
n
(m /m )
(m /m )
1/cm
(-)
Loamy Sand
0.0485
0.3904
0.0347
1.7466
Sandy Loam
0.0387
0.3870
0.0267
1.4484
theta s = Saturated water content
theta r = Residual water content at wilting point (h ~ -15,000 cm)
alpha and n = Curve fitting parameters after van-Genuchten (1980)
Table 4-10: Soil moisture retention curve parameters obtained from the USDA Rosetta
Lite v. 1.1 Database. The two most common soil types (loamy sand and sandy loam)
identified during grain size analysis were input to determine the overall moisture
retention characteristics of the meadow sediments.
100
Loamy Sand
Sandy Loam
Pressure
Head, h (cm)
Water
Content
Specific Yield (1)
Water
Content
Specific Yield (1)
-1.00E+00
0.390
0.00
0.386
0.00
-1.00E+01
0.370
0.02
0.372
0.01
-1.00E+02
0.177
0.21
0.248
0.14
-1.00E+03
0.073
0.32
0.118
0.27
-1.00E+04
0.053
0.34
0.067
0.32
-1.00E+05
0.049
0.34
0.049
0.34
-1.00E+06
0.049
0.34
0.042
0.34
(1) Specific Yield determined by subtracting the calculated water content at the
given pressure head from the saturated water content
Table 4-11: Calculated specific yield values based on soil moisture retention
characteristics of the meadow sediments. As the negative pressure potentials in the soil
increased, the amount of water retained by the soil decreased.
101
Data Source
Porosity (n) (%)
Specific Yield (Sy)
(%)
Pedo-Transfer
Function from
USDA Rosetta Lite
v. 1.1 (Sandy Loam)
38.7
14
Pedo-Transfer
Function from
USDA Rosetta Lite
v. 1.1 (Loamy Sand)
39
21
Johnson, 1967
(Fine Sand)
--
21
Johnson, 1967
(Medium Sand)
--
26
Mean
39
21
Table 4-12: Summary of specific yield values of the meadow sediments. Based on the
limitations of laboratory analysis, empirical values were referenced based on grain size
classifications and then were averaged to provide a singular and representative value.
Comparing the mean values of porosity (39%) and specific yield (21%) appears to
indicate that, during the course of this study, approximately half of the total pores in the
meadow sediments have the potential to store water that will drain under the influence of
gravity.
102
Figure 4-1: Photo of a typical piezometer installation in the meadow. Note that a steel
cap was affixed to the top of the piezometer as to limit any infiltration of debris or
tampering.
103
Figure 4-2: Location of piezometers and soil borings in the meadow. Note that
piezometers PZ-1 and PZ-4 are located near the inflow and outflow areas of the meadow,
respectively while PZ-2 and PZ-3 are within the central portion of the meadow. The
majority of the soil borings were advanced near the perceived limits of the meadow
sediments to assist in defining the extent of the meadow shallow aquifer.
104
PZ-1 Uncorrected and Corrected Groundwater Elevations
2021.00
2020.50
Groundwater Elevation (m)
2020.00
2019.50
PZ-1
Uncorrected
2019.00
PZ-1
Corrected
2018.50
2018.00
2017.50
2017.00
2016.50
9/28/2009
10/12/2009
9/14/2009
8/31/2009
8/3/2009
8/17/2009
7/6/2009
7/20/2009
6/8/2009
6/22/2009
5/25/2009
5/11/2009
4/27/2009
4/13/2009
3/30/2009
3/2/2009
3/16/2009
2/2/2009
2/16/2009
1/5/2009
1/19/2009
12/8/2008
12/22/2008
11/24/2008
11/10/2008
10/27/2008
9/29/2008
10/13/2008
2016.00
Date
Figure 4-3: Comparison of calculated groundwater elevations using transducer readings
uncorrected and corrected for fluctuations in barometric pressure. When compared with
manual measurements of groundwater elevations, the corrected transducer readings were
determined to better reflect water table conditions in the meadow as opposed to
uncorrected readings, which were found to significantly overestimate water table levels.
105
PZ-1 to PZ-4 Corrected Groundwater Elevations
2020.00
2019.00
Groundwater Elevation (m)
2018.00
2017.00
PZ-1
2016.00
PZ-2
2015.00
PZ-3
2014.00
PZ-4
2013.00
2012.00
2011.00
9/28/2009
10/12/2009
9/14/2009
8/31/2009
8/3/2009
8/17/2009
7/6/2009
7/20/2009
6/8/2009
6/22/2009
5/25/2009
5/11/2009
4/27/2009
4/13/2009
3/30/2009
3/2/2009
3/16/2009
2/2/2009
2/16/2009
1/5/2009
1/19/2009
12/8/2008
12/22/2008
11/24/2008
11/10/2008
10/27/2008
9/29/2008
10/13/2008
2010.00
Date
Figure 4-4: Corrected groundwater elevations recorded from the piezometers installed in
the meadow for the entire duration of monitoring (September 29, 2008 to October 17,
2009). Data were collected from pressure transducers programmed to record at 15minute intervals. Daily 15-minute data were averaged to result in a singular daily value
for groundwater elevations for each piezometer.
106
PZ-2 to PZ-3 Depth to Water Below Ground Surface
-1.00
Depth to Water Below Land Surface (m)
-0.50
0.00
0.50
PZ-2
1.00
PZ-3
1.50
2.00
2.50
9/28/2009
10/12/2009
9/14/2009
8/31/2009
8/3/2009
8/17/2009
7/6/2009
7/20/2009
6/8/2009
6/22/2009
5/25/2009
5/11/2009
4/27/2009
4/13/2009
3/30/2009
3/2/2009
3/16/2009
2/2/2009
2/16/2009
1/5/2009
1/19/2009
12/8/2008
12/22/2008
11/24/2008
11/10/2008
10/27/2008
9/29/2008
10/13/2008
3.00
Date
Figure 4-5: Depth to water from the meadow surface in piezometers PZ-2 and PZ-3. The
trend is similar for both monitoring points with water levels at or near the surface for
much of the year with declining levels noted during the time period of late spring to late
summer.
107
Soil classification range
based on 78% sand content
Content
Figure 4-6: Soil sample classification based on USDA soil textural triangle. As the
amount of silts and clays were not separated out from each sample, the mean sand content
of 78% for all samples was used to determine the range of potential classifications. The
individual percent sand content for each soil sample as well as the overall mean is
summarized on Table 4-7.
108
PZ-2 Slug In - Hydrauic Head vs. Time
190.00
185.00
180.00
Level (cm)
175.00
170.00
165.00
160.00
155.00
150.00
145.00
140.00
-6
-4
-2
0
2
4
6
8
10
12
14
16
Elapsed Time (s)
Figure 4-7: Initial displacement of the water column in piezometer PZ-2 due to slug
testing. The slug was lowered at time zero and resulted in a nearly instantaneous
displacement of approximately 35 cm.
109
PZ-3 Slug In - Hydrauic Head vs. Time
175.00
170.00
Level (cm)
165.00
160.00
155.00
150.00
145.00
-10
0
10
20
30
40
50
60
70
80
90
100 110 120 130 140 150 160 170
Elapsed Tim e (s)
Figure 4-8: Initial displacement of the water column in piezometer PZ-3 due to slug
testing. The slug was lowered at time zero and resulted in a nearly instantaneous
displacement of approximately 25 cm.
110
PZ-2 Slug In - Normalized Recovery Plot
1.00
h/ho
t37
PZ-2 Slug In
0.10
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
Elapsed Tim e (s)
Figure 4-9: Normalized plot of the recovery of the hydraulic head in PZ-2 during slug
testing. h/ho represents the initial displacement of the water column from static condtions
divided by the level of the water column above static condtions over time. t37 is the time
at which the water column has recovered to 37% of the initial displacement as required
by the Hvorslev (1951) method of analysis.
111
PZ-3 Slug Out - Normalized Recovery Plot
1.00
h/ho
t37
PZ-3 Slug In
0.10
0
20
40
60
80
100
120
140
160
180
Elapsed Tim e (s)
Figure 4-10: Normalized plot of the recovery of the hydraulic head in PZ-3 during slug
testing. See Figure 4-9 for an explanation of variables described on this plot.
112
Change in Volumetric Water Content over Time
0.00
3
3
Change in Volumetric Water Content (cm/cm )
-0.02
-0.04
HA-9 (46-61
cm)
HA-9 (76-91
cm)
-0.06
-0.08
-0.10
-0.12
-0.14
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
Elapsed Time (days)
Figure 4-11: Plot of the change in volumetric water content over time for two soil
samples collected from the meadow. The total change in volumetric water content
represents the total drainage from the samples due to gravity or the specific yield. This
analysis was discontinued after approximately 70 days as the change in water content did
not appear to reach equilibrium which indicates that a representative value for specific
yield was not reached.
113
Sandy Loam Soil Retention Curve
- 1.00E+06
r
- 1.00E+05
Pressure Head (cm)
- 1.00E+04
- 1.00E+03
Sandy Loam
- 1.00E+02
- 1.00E+01
s
- 1.00E+00
0.00
0.10
0.20
0.30
0.40
0.50
Water Content (cm 3/cm 3)
Figure 4-12: Soil moisture retention curve for soils classified as sandy loams. The
parameters used to determine the relationship between pressure head and water content
were obtained from the USDA Rosetta Lite database and are summarized in Table 4-10.
Individual values of the water content over a range of pressure heads are summarized in
Table 4-11. s is the saturated water content or total porosity, r is the residual water
content or the wilting point of the soil. This chart was used to determine the specific
yield of the meadow sediments by subtracting the water content at a pressure head of
-100 cm from the saturated water content. The pressure head value of -100 cm is
equivalent to the maximum depth of the water table below the meadow land surface
(approximately 1 m). Specific yield values are summarized in Table 4-12.
114
Loam y Sand Soil Retention Curve
- 1.00E+06
r
- 1.00E+05
Pressure Head (cm)
- 1.00E+04
- 1.00E+03
Loamy Sand
- 1.00E+02
- 1.00E+01
s
- 1.00E+00
0.00
0.10
0.20
0.30
0.40
0.50
Water Content (cm 3/cm 3)
Figure 4-13: Soil moisture retention curve for soils classified as loamy sands. An
explanation of the parameters and values used to construct this plot are described in
Figure 4-12.
115
PZ-2 and PZ-3 Depth to Water Below Ground Surface and Incremental Precipitation
20.00
-1.00
16.00
14.00
0.00
12.00
10.00
0.50
8.00
1.00
6.00
Daily Incremental Precipitaiton (cm)
Depth to Water Below Land Surface (m)
18.00
-0.50
Precipitation
PZ-2
PZ-3
4.00
1.50
2.00
0.00
9/28/2009
10/12/2009
9/14/2009
8/31/2009
8/3/2009
8/17/2009
7/6/2009
7/20/2009
6/8/2009
6/22/2009
5/25/2009
5/11/2009
4/27/2009
4/13/2009
3/30/2009
3/2/2009
3/16/2009
2/2/2009
2/16/2009
1/5/2009
1/19/2009
12/8/2008
12/22/2008
11/24/2008
11/10/2008
10/27/2008
9/29/2008
10/13/2008
2.00
Date
Figure 4-14: Plot of water table levels below land surface and incremental precipitation.
Water levels appear to be above the ground surface and periodically fluctuate widely
during the winter months. These occurrences appear to be correlated with significant
rainfall events when the meadow is inundated by snow. Potential causes may be the
sensitivity of the pressure transducers to storm events or temporary ponding of water in
the meadow.
116
Average Depth to Water in Meadow During 2008-2009 Water Year
-1.00
Period of Water Table
Decline (6-5-09 to 9-28-09)
Depth to Water Below Land Surface (m)
-0.50
PZ-2
PZ-3
0.00
PZ-2 and PZ-3
Average
``
0.50
1.00
9/28/2009
10/12/2009
9/14/2009
8/31/2009
8/3/2009
8/17/2009
7/6/2009
7/20/2009
6/8/2009
6/22/2009
5/25/2009
5/11/2009
4/27/2009
4/13/2009
3/30/2009
3/2/2009
3/16/2009
2/2/2009
2/16/2009
1/5/2009
1/19/2009
12/8/2008
12/22/2008
11/24/2008
11/10/2008
10/27/2008
9/29/2008
10/13/2008
1.50
Date
Figure 4-15: Average depth to water below the meadow surface during the 2008-2009
water year. Water levels are near or above the ground surface for much of the fall
through late spring and then begin to decline to a maximum depth of approximately 1.1 m
below ground surface in late summer.
117
Chapter 5
TRANSPIRATION ESTIMATES OF LODGEPOLE PINE
5.1 Introduction
Quantifying the amount of water utilized by vegetation is often one of the most
complex portions of any hydrologic study. This variable is often estimated based on
performing water balancing of inputs and outputs to a hydrologic system and any deficits
are considered to be from surface evaporation and transpiration by vegetation (Fetter,
2001). This method is limited and potentially inaccurate based on the controls of the
other inputs and outputs. For the purposes of this study, the determination of the rate at
which lodgepole pine trees transpirate water is critical as it provides a basis for the
amount of water that will be placed back into storage should the trees be removed.
Methods were researched in an effort to determine the most accurate means to determine
this rate while considering the scope and constraints of this study.
Many methods have been cited for use in determining the transpiration of various
types of vegetation, both direct and indirect. Allen et al. (1998), discusses that one of the
most common direct methods is the use of a lysimeter where a sample of the vegetation is
grown in a specially designed pan filled with the same soil as is found in the area of
study. The loss of water from the pan is recorded and replenished at varying frequencies
to give a potential rate of transpiration. This method is commonly used in conjunction
with a land pan to separate any evaporation from the surface of the soil in the lysimeter.
This is extremely time consuming but is a useful method for small agricultural crops such
as grass and alfalfa (Howell and Evett, 2002), but has limitations with larger vegetation
118
such as conifers. Waring and Schlesinger (1985), cite that a direct method applicable to
conifers is the use of tracers to monitor sapwood conductance from the root system to the
stomata in the leaves (where transpirated water exits the tree). This method is highly
complex and expensive to monitor and was not considered appropriate for this study.
The most common method to quantify the rate of transpiration in hydrologic
studies is to use indirect methods as outlined in a meadow restoration study by
Hammersmark et al. (2008). Indirect methods use established relationships of climatic
variables and physical characteristics to estimate, through a series of equations, the rate
of transpiration by the vegetation of interest. This chapter discusses the methods selected
to quantify the rate of transpiration exhibited by lodgepole pines within the meadow, the
data obtained to conduct the calculations, and the results and limitations of the selected
methods.
5.2 Methods
Selection of method
As discussed above, many methods have been used historically to quantify the
transpiration rate of specific vegetation types. A study conducted in 1977 by the Food
and Agriculture Organization of the United Nations (FAO Drainage Paper No. 24,
Doorenbos and Pruitt, 1977) evaluated a number of various methods to determine the
most appropriate for evaluating transpiration needs of agricultural crops. The results of
this study indicated that a method initially developed by Penman (1948) and later
modified by Montieth (1973) was the most accurate indirect method when compared with
site-specific field data. The equation associated with this method is commonly referred
119
to in recent literature as the Penman-Montieth equation. Based on conversations with
representatives from the Eldorado National Forest, this method has been previously used
in a variety of forest hydrological applications (Koler, 2009). Modifications of the initial
equation have been derived by many researchers and the most current and widely
accepted version in the scientific realm of agriculture is outlined in FAO Drainage Paper
No. 56 (FAO No. 56, Allen et al., 1998).
The basic premise of the Penman-Montieth (P-M) equation is that variations in
climatic variables such wind speed, solar radiation, relative humidity, latent heat, and
vapor pressure deficits control the rate at which water vapor is conducted through the
stomata of plants from the soil surrounding the plant root system (Howell and Evett,
2002). A key limitation of the P-M method as described in FAO No. 56. is that the
equation associated with this method was specifically derived to develop a widely
applicable method for estimating crop water requirements, which does not appear to
include conifer tress.
However, researchers have developed specific equations for
estimating the water use of conifers trees for forestry applications (Waring and
Schlesinger, 1985). For the purposes of this study, the modified P-M equation developed
by Waring and Schlesinger (1985) was selected as the basis for quantifying the
transpiration rate of lodgepole pines within the meadow.
Relationships of climatic
variables described in FAO No. 56 were used to provide constraints on the various
required inputs to the equation.
120
Conifer specific method for determining the transpiration rate
The conifer specific modified P-M equation derived by Waring and Schlesinger
(1985) is shown below:
Et 
k 2 k3
D  g s  LAI
k1k 4
where:
Et = transpiration rate (cm/s);
k1 = latent heat of vaporization (cal/g);
k2 = specific heat of air (cal/g * oC);
k3 = density of air (g/cm3);
k4 = psychromatic constant (mb/ oC);
D = vapor pressure deficit (mb);
gs = stomatal conductance (cm/s); and
LAI = Leaf Area Index (unitless)
It should be noted that the original form of the P-M equation (as formulated to determine
crop water use) required known inputs for the variables of wind speed and solar radiation.
The necessity of these two variables was based on the morphology of crops that typically
have relatively significant leaf widths that have the ability absorb significant amounts of
radiation and be affected by wind (FAO No. 56). The modified P-M shown above does
not include these variables as leaf widths are commonly less than a few millimeters in
width and therefore these variables are often ignored (Waring and Schlesinger, 1985).
The following sub-sections summarize the methods used to quantify or estimate the
required variables of the equation above.
121
k variables

k1 (the latent heat of vaporization) can be quantified using the expression as
described in Lee (1978):
k1 = 597 – 5/9 * T, where T is the ambient air temperature in oC. In accordance with
FAO No. 56, the ambient daily air temperature observed at the meadow was given by
the expression:
where:
Tmax = the maximum daily temperature reading over a period of 24 hours; and
Tmax = the minimum daily temperature reading over a period of 24 hours;

k2 (specific heat of air) is a constant that is equal to 0.24 cal/g * oC (Lee, 1978).

k3 (the density of air) is considered a constant for this equation at a reference
temperature of 20 oC and 1000 mb of pressure (FAO No. 56). At this temperature
and pressure, this variable is equal to 1.19E-03 g/cm3 (Lee, 1978).

k4 (the psychromatic constant) is a constant that relates the partial pressure of water
vapor to temperature.
Lee (1978) provides a constant value of 0.66 mb/ oC to
effectively estimate this relationship.
Vapor Pressure Deficit (D)
The vapor pressure deficit is defined by Waring and Schlesinger (1985) as the net
difference between the saturated vapor pressure (es) at a given temperature and the actual
observed vapor pressure (ea) at the same temperature. This term is quantified based on
122
relationships of temperature and relative humidity discussed in FAO No. 56 and
described below.

The vapor pressure deficit = es – ea , where es is given by the expression:
and
where:
eo (Tmax) = the saturated vapor pressure at the maximum daily temperature
reading over a period of 24 hours;
eo (Tmin) = the saturated vapor pressure at the minimum daily temperature
reading over a period of 24 hours;
RHmax = the maximum daily relative humidity reading over a period of 24
hours; and
RHmin
= the minimum daily relative humidity reading over a period of 24
hours.
This method is also noted to be effective using the averages of daily maximum and
minimum temperatures and relative humidity values over a period of months and
potentially years.

The saturated vapor pressure (in kPa) at a given temperature T (in oC) is given
by the expression:
Results of calculations using the above expression were multiplied by 10 to convert
kPa to mb.
Stomatal Conductance (gs)
Stomatal conductance is often a variable that can be difficult to determine
123
utilizing indirect methods as values vary based on the species as well as atmospheric
conditions. However, Waring and Schlesinger (1985) determined that relationships exist
between stomatal conductance and vapor pressure deficit for a number of different plant
types (Figure 5-1). Included in the plant types are two types of conifers, Douglas fir
(Pseudotsuga menziessi) and western hemlock (Tsuga heterophylla). During review of
the literature for this study, an established relationship utilizing vapor pressure deficit (or
any other climatic variable for that matter) to determine stomatal conductance was not
found for lodgepole pine trees. Based on discussions by Waring and Schlesinger (1985)
regarding the conductance of conifers in general, it appears that there is not considerable
variation in conductance rates between species. This low variability is shown on Figure
5-1 as Douglas fir and hemlock values generally follow the same trend with hemlock
conductance values slightly higher than Douglas fir. Therefore, it was assumed that the
average of the calculated conductance values for these two species would be
representative of conifers in general and could be used as a proxy for lodgepole pine
trees.
In order to quantify discrete values of stomatal conductance based on varying
vapor pressure deficits, the relationship between these two variables was recreated
graphically for both Douglas fir and hemlock using a series of discrete points from Figure
5-1. Subsequently, a number of different regression line types (linear, exponential, and
polynomial) were then fit to the points to determine which type resulted in the best fit
(highest correlation coefficient). Following review of the results of this analysis, it was
determined that a 4th order polynomial regression provided the best fit to the discrete
124
points obtained from Figure 5-1. Figures 5-2 and 5-3 depict the re-creation of the plots
for Douglas fir and hemlock, respectively. The resultant polynomial equations as well as
the correlation coefficients (R2) are also shown on Figures 5-2 and 5-3.
Leaf Area Index (LAI)
Leaf Area Index (LAI) is defined as the projected area of canopy foliage per unit
area of ground surface. This value is highly variable and is dependant on the type and the
overall canopy density (Howell and Evett, 2002). LAI values typically deviate from low
values during time of dormancy to maximums during the height of the growing season,
typically in mid-summer (Lee, 1978).
As with many of the variables previously
discussed, there are both direct and indirect methods of determining this highly sitespecific variable. Direct methods typically consist of measuring the amount of light that
will pass through canopy per unit of surface area.
Highly sensitive photography
equipment is a relatively common method to perform direct measurements using light.
Historical methods have also used manual measurements of the amount of leaf litter
distributed over a grid system defining a bounded area around the canopy. This method is
highly time consuming and invasive and is becoming less common with the availability
of other and more accurate direct methods (Waring and Schlesinger, 1985 and Tian et al.,
2002).
Many of the recent techniques of determining LAI indirectly have come in the
form of remote sensing. This technique allows for the quantification of site- specific
values of LAI at varying resolutions (commonly ranging from 250 m to 1 km, Tian et al.,
2002). Research was conducted for the existence of such data for the area including and
125
surrounding the meadow and it was determined to be available through the Aqua satellite
launched in 2002 by the National Aeronautics and Space Administration (NASA).
Sensors on this satellite include a Moderate Resolution Imaging Spectroradiometer
(MODIS) that uses surface reflectance to quantify many attributes of vegetation cover,
including LAI.
Data were available at 1 km resolution through the Oak Ridge National
Laboratory Distributed Active Archive Center (ORNL DAAC, 2010). The latitude and
longitude of the meadow were input to define the center pixel through an online interface
available at http://daac.ornl.gov/MODIS. It was determined after input of the center
coordinates that the minimum area for which data sets were available consisted of 3 km
by 3 km areas, or a total of nine-1 km pixels.
Each pixel was designated with a
vegetation class based on observations from the satellite (Figure 5-4). LAI daily readings
were automatically composited over 8-day averaging periods for the duration of
requested observations.
LAI data were post-processed at ORNL DAAC using filters to remove any
readings that were questionable based on excessive cloud cover or sensor errors. Data
was retained from each pixel that matched the vegetation class of the center pixel
(classified as an Evergreen Needleleaf Forest). Additionally, the standard deviation over
each 8-day averaging period was included with the data set. The period of requested
observations was input from January to October 2009, in order to determine the time of
LAI increase in the meadow and surrounding areas as well as to include the period of
observed water table decline (June to September, 2009).
This was important in
126
determining the growing season LAI and to define the time period of highest canopy
density in the meadow.
Determination of Temperature and Relative Humidity
Based on the method discussed above to determine lodgepole transpiration rates,
the only variables required to determine fluctuations are changes in daily maximum and
minimum air temperatures (T) and relative humidity (R.H.). As shown on Table 2-1, the
closest available weather station that records maximum and minimum T is located
approximately 0.5 km to the north of the meadow. The closest available weather station
that records maximum and minimum R.H is located approximately 40 km to the
northeast.
Data sets were obtained from each of these stations from the time period of June
5, 2009 to September 28, 2009 (Appendix D). Daily maximum and minimum values for
both T and R.H. were then averaged by month to provide representative monthly average
values of climatic conditions in the meadow. Using monthly averaging periods assumes
that daily fluctuations in climatic condtions during these specific months will not have a
significant overall effect on calculated transpiration rates. To determine the validity of
this assumption, the highest and lowest ranges between daily maximum and minimum
observations of both T and R.H. were input into the transpiration equation.
5.3 Results
Determination of Leaf Area Index
The results of the MODIS derived LAI for 3 km by 3 km area surrounding the
meadow over the time period of January to October 2009 are shown on Figure 5-5. LAI
127
values are lowest in the winter months and are highest in the summer months. The time
of LAI increase appears to occur in May. Relatively high values are observed through
September, with a gradual decrease noted to begin in October. Table 5-1 summarizes the
LAI data set during the period of May through September, which represents the 8-day
average LAI values from the onset of tree growth through the end of the period of water
table decline. 8-day averages range from 6.2 in the early part of May to 21.8 in late
August. The mean of all observations from May to September is 16.1 and the mean
standard deviation is 2.8.
Because these data include not only the meadow but also the surrounding
environment, the mean value above may be a slight overestimation of LAI based on
observed differences in tree density between the meadow and surrounding hill slopes.
Lodgepole pine stands were noted to be more densely grouped on the hill slopes
surrounding the meadow and were generally less densely grouped in the meadow.
Therefore, the lodgepole pine LAI for the meadow was calculated by subtracting the
mean standard deviation from the overall mean. The resultant value that was used in all
transpiration rate calculations was 13.3 (Table 5-1). Performing this modification should
provide a conservative estimate of the LAI for the lodgepoles in the meadow.
Transpiration rates
The average daily transpiration rates for June to September were calculated using
the average LAI value discussed above and average minimum and maximum daily values
of T and R.H. (Table 5-2). Average daily minimum temperatures ranged from 3.7 oC in
June to 7.0 oC in September and average daily minimum R.H. values ranged from 14 %
128
in July and September to 28 % in June. Average daily maximum temperatures ranged
from 16.5 oC in June to 23.5 oC in July and average daily maximum R.H. values ranged
from 35 % in August to 70 % in June.
Table 5-3 summarizes the calculated daily average transpiration rates for each
month. Rates calculated from the modified P-M equation were reported in units of cm/s.
These values were converted to units of mm/d and were multiplied by the number of
sunlight hours observed in the meadow during the summer months (assumed to be 12
hours). This is consistent with observations by Waring and Schlesinger, 1985 regarding
the influence of sunlight on stomatal conductance. Specifically, he notes that the stomata
of conifers will be fully open at 5 to 20% of total sunlight. The assumed daily value of
12 hours of transpiration appears to be valid based on this observation.
Daily rates ranged from 12.96 mm/d in June to 17.32 mm/d in July. Table 5-4
summarizes the results of comparing the transpiration rates using an average of daily
temperatures (Table 5-3) with rates calculated using the highest and lowest daily ranges
of temperature observed for each month. It should be noted that values of R.H. were
fixed during this analysis. The percent difference between these two methods ranged
from 2.1 % to 41.8 %. It should be noted that the 41.8 % difference resulted from
utilizing the lowest range of maximum and minimum temperatures for June (minimum
1.7 oC and maximum 4.8 oC). This maximum temperature is noted to be significantly
lower than the reminder of maximum temperatures observed from June to September
(Appendix D). The remaining percent differences were determined to be below 6 %.
129
Table 5-5 summarizes the results of a similar analysis for R.H. values while temperature
values were fixed. The percent difference was calculated to range from 0.3 % to 8.6 %.
5.4 Discussion
The use of climatic data appears to be an effective method of quantifying
transpiration rates indirectly as these data are typically one the most readily available data
sets when conducting any hydrologic study. The location of the nearest temperature
recording weather station was determined to be 0.5 km in distance from the meadow.
Based upon this close proximity it is appropriate to assume weather station observations
of temperature reflect meadow conditions throughout the year. Observations of the
maximum and minimum temperatures in the meadow indicate that there are wide daily
fluctuations in temperature at this location. This may be expected based on the elevation
of the meadow (2016 m) and overall Sierra Nevada climate at this location.
The nearest weather station to record consistent observations of relative humidity
was located nearly 40 km in distance from the meadow near the northeast shore of Lake
Tahoe. This distance is significantly greater than the temperature station and less overall
credence is given to the representativeness of these data for the meadow. However, the
weather station does sit at an elevation similar to the meadow (1950 m) and it appears
reasonable to assume that significant climatic trends observed at the station would also be
observed at the meadow. Use of proxy data in this manner is a prime example of using
the best available source of remote observations to estimate condtions in the area of
interest.
Based upon review of the potential error associated with averaging daily values of
130
temperature and relative humidity rather than using discrete daily values, it appears that
using the monthly average does not have a significant effect on overall transpiration rate
results. An exception was the percent difference calculated for June using the minimum
range of daily temperatures. This result is considered to be an outlier as the result was
based on temperatures that did not reflect the remainder the period of interest.
The use of MODIS data to determine LAI values remotely appeared to be an
effective method of determining the canopy density of conifers in proximity to the
meadow. Other methods of determining LAI were considered, however many were
determined to be cost prohibitive for the scope of this study. Tian et al. (2002) does note
that a limitation of using MODIS to determine this variable is that values can often be
overestimated is the vegetation cover is high heterogeneous. Based on observations of
the meadow, the surrounding hill slopes are predominately conifer (lodgepole) dominated
with little understory vegetation and thus any overestimation is unlikely. The use of this
method to determine this variable was determined to be a use of the best available
technology.
As previously discussed, LAI is highly site-specific and correlation between stands
is often discouraged (Tian et al., 2002). Specific estimates of LAI values for lodgepole
pine were not found in the literature however, the range of typical values for pine tree
species in general were reviewed to determine if values from MODIS appear to reflect
typical values for conifers. Waring and Schlesinger, 1985 cite values of growing season
LAI as low as 7 for some stands of Ponderosa Pine and as high as 17 for stands of White
Pine. Although there are differences in the morphology (and therefore potential canopy
131
densities) of various species of pines, the value of 13.3 selected for the meadow
lodgepoles appears to be reasonable.
The results of the analysis discussed in this chapter provide key constraints on the
amount of water the encroaching lodgepole pine tress extract from meadow storage
throughout the summer months.
The rate at which these trees were determined to
transpire water was used in groundwater model simulations of tree removal, which is
discussed in detail in Chapter 6.
132
5.5 Tables and Figures
Date
05/01/09
05/09/09
05/17/09
05/25/09
06/02/09
06/10/09
06/18/09
06/26/09
07/04/09
07/12/09
07/20/09
07/28/09
08/05/09
08/13/09
08/21/09
08/29/09
09/06/09
09/14/09
09/22/09
09/30/09
8-Day
MEAN
LAI
6.2
9.1
14.4
14.4
12.4
6.8
18.4
20.6
19.4
19.0
20.6
19.8
19.6
17.9
21.8
18.4
16.5
15.4
17.6
14.3
Calculated Mean
8-Day
Standard
Deviation
1.0
1.8
2.5
2.1
1.7
3.0
3.2
6.8
1.7
2.4
1.7
1.9
2.3
1.5
4.1
1.9
3.6
2.3
4.7
4.3
16.1
Mean Standard
2.8
Deviation
Adjusted Mean
(Calculated Mean 13.3
Mean Standard
Deviation)
Notes:
LAI values calculated from all pixels with the same Land Cover Class (Evergreen Needleleaf Forest) as the
Center Pixel
Nine 1 km x 1km pixels were included in the coverage area and eight were found to be the same vegetation
class as the Center Pixel
1 km x 1km Center Pixel includes the entire extent of the meadow study area
Table 5-1: Summary of Leaf Area Index (LAI) values obtained from the Aqua satellite
operated by NASA for the meadow vicinity. Based on the large 9 km2 overall coverage
area of the data when compared with the size of the meadow (~0.1 km2), the adjusted
mean was used in all calculations for meadow leaf area index of encroaching lodgepole
pine.
133
Month
Average of
Daily Minimum
Temperatures
(oC)
Average of Daily
Maximum
Temperatures
(oC)
Average of Daily
Minimum
Relative
Humidity (%)
Average of
Daily
Maximum
Relative
Humidity (%)
June
2009
3.7
16.5
28
70
July
2009
6.9
23.5
14
52
August
2009
5.9
22.1
17
35
September
2009
7.0
23.2
14
52
Table 5-2: Summary of average daily minimum and maximum temperature and relative
humidity for June to September 2009. Daily minimums and maximums for each month
were compiled and averaged to provide singular representative values of these two
variables for use in transpiration calculations.
134
Month
Calculated Transpiration Using
Average of Daily Maximum and
Minimum Temperatures and
Relative Humidity (mm/d)
June 2009
12.96
July 2009
17.32
August 2009
17.02
September 2009
17.12
Table 5-3: Summary of calculated daily transpiration rates for June to September 2009.
Transpiration rates were calculated in part by using average daily values of temperature
and relative humidity as summarized in Table 5-2.
135
Month
Largest
Daily Range
Between
Min and
Max
Readings
(oC)
Max
Temp
(oC)
Calculated
Transpiration
Using Largest
Daily Range
Values of
Temperature
(mm/d)
Calculated
Transpiration
Using Average of
Daily Max and
Min Temps and
R.H. (mm/d)
Min
Temp
(oC)
%
Difference
(mm/d)
June
2009
18.9
0.0
18.9
13.97
12.96
7.8
July
2009
21.7
1.1
22.8
16.88
17.32
2.5
August
2009
20.6
0.0
20.6
16.21
17.02
4.8
September
2009
21.7
5.0
26.7
17.69
17.12
3.3
Month
Lowest Daily
Range
Between
Min and
Max
Readings
(oC)
Min
Temp
(oC)
Max
Temp
(oC)
Calculated
Transpiration
Using Lowest
Daily Range
Values of
Temperature
(mm/d)
Calculated
Transpiration
Using Average of
Daily Max and
Min Temps and
R.H. (mm/d)
%
Difference
(mm/d)
June
2009
2.8
1.7
4.4
7.54
12.96
41.8
July
2009
13.9
11.7
25.6
17.76
17.32
2.5
August
2009
12.2
6.0
18.2
16.05
17.02
5.7
September
2009
11.6
8.9
20.5
16.76
17.12
2.1
Notes:
Fixed values of Relative Humidity at 0.28 (min) and 0.70 (max) for June 2009 Error Estimates
Fixed values of Relative Humidity at 0.14 (min) and 0.52 (max) for July 2009 Error Estimates
Fixed values of Relative Humidity at 0.17 (min) and 0.35 (max) for August 2009 Error Estimates
Fixed values of Relative Humidity at 0.14 (min) and 0.52 (max) for September 2009 Error Estimates
Table 5-4: Summary of the estimated error due to utilizing average values of temperature
in transpiration calculations. Error estimates were determined by comparing transpiration
rates calculated using the highest and lowest daily temperature ranges recorded in the
meadow for June to September 2009 with rates calculated using an average of daily
maximum and minimum temperatures for the same time period. Values of relative
humidity were fixed during these comparisons.
136
Month
Largest
Daily
Range
Between
Min and
Max
Readings
(%)
Min
Relative
Humidity
(%)
June
2009
61
July
2009
Max
Relative
Humidity
(%)
Calculated
Transpiration
Using Largest
Daily Range
Values
(mm/d)
Calculated
Transpiration
Using Average
of Daily Max
and Min R.H
and Temps
(mm/d)
%
Difference
(mm/d)
26
87
12.41
12.96
4.2
73
12
85
16.92
17.32
2.3
August
2009
61
14
75
16.43
17.02
3.5
September
2009
60
5
65
17.28
17.12
0.9
Month
Lowest
Range
Between
Min and
Max
Readings
Min
Relative
Humidity
(%)
Max
Relative
Humidity
(%)
Calculated
Transpiration
Using Lowest
Range Values
(mm/d)
Calculated
Transpiration
Using Average
of Daily Max
and Min R.H
and Temps
(mm/d)
Difference
(mm/d)
June
2009
24
25
49
14.08
12.96
8.6
July
2009
17
18
35
17.38
17.32
0.3
August
2009
16
10
26
17.38
17.02
2.1
September
2009
18
5
23
17.64
17.12
3.0
Notes:
Fixed values of Temperature at 3.7 (min) and 16.5 (max) oC for June 2009 Error Estimates
Fixed values of Temperature at 6.9 (min) and 23.5 (max) oC for July 2009 Error Estimates
Fixed values of Temperature at 5.9 (min) and 21.8 (max) oC for August 2009 Error Estimates
Fixed values of Temperature at 7.0 (min) and 22.5 (max) oC for September 2009 Error Estimates
Table 5-5: Summary of the estimated error due to utilizing average values of relative
humidity in transpiration calculations. Error estimates were determined by comparing
transpiration rates calculated using the highest and lowest daily relative humidity ranges
recorded in the meadow for June to September 2009 with rates calculated using an
average of daily maximum and minimum relative humidity for the same time period.
Values of temperature were fixed during these comparisons.
137
Figure 5-1: Graphical plot of the relationship between the stomatal conductance of
various species and vapor pressure deficit as described by Waring and Schlesinger
(1985). Line 1 shown in the figure relates these two variables for Douglas fir species and
line 2 is for hemlock species.
138
Maximum Stomatal Conductance (cm/s)
Douglas Fir
0.5
0.45
y = -3E-07x 4 + 4E-05x 3 - 0.0016x 2 + 0.0105x + 0.4113
R2 = 0.9933
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
10
20
30
40
50
60
70
Vapor Pressure Deficit (mb)
Figure 5-2: Graphical recreation of the relationship between stomatal conductance and
vapor pressure deficit for Douglas fir species. This plot was generated using discrete
points obtained from Figure 5-1 and a 4th order polynomial regression line, which was
determined to provide the best fit of the points. The equation of the regression line and
correlation coefficient (R2) is also shown.
139
Hemlock
Maximum Stomatal Conductance (cm/s)
0.6
y = -1E-06x 4 + 7E-05x 3 - 0.0009x 2 - 0.0216x + 0.5601
R2 = 0.9916
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
25
30
35
40
45
Vapor Pressure Defict (mb)
Figure 5-3: Graphical recreation of the relationship between stomatal conductance and
vapor pressure deficit for hemlock species. This plot was generated using discrete points
obtained from Figure 5-1 and a 4th order polynomial regression line, which was
determined to provide the best fit of the points. The equation of the regression line and
correlation coefficient (R2) is also shown.
140
Center l-km by
1- km pixel that
includes the
meadow
Figure 5-4: Landcover classification generated from the Aqua satellite associated with
obtaining leaf area index data. The figure depicts the nine 1-km by 1-km pixels (each
pixel includes four subpixels) that were used to determine the leaf area index (LAI)
remotely. During data post-processing, LAI values that were recorded from subpixels
that did not match the center pixel (Evergreen Needleleaf Forest) were discarded.
141
May
2009
June
2009
July
2009
August
2009
Sept
2009
Figure 5-5: Plot of leaf area index (LAI) values obtained from the Aqua satellite for
January to October 2009. LAI (unitless) is plotted on the y-axis at a scale factor of 0.1 in
the upper portion of the plot. LAI values from May to September 2009 were used in
transpiration calculations as this time period corresponded with the observed decrease in
meadow groundwater elevations. Points in the center of each box and whisker plot
represent the average LAI value of all pixels that were determined to have acceptable
quality following data post-processing. Points away from the center of each box and
whisker plot represent the LAI value for the center pixel only. The percentage of pixels
that were determined to have acceptable quality following data post-processing is plotted
on the y-axis in the lower portion of the plot (vertical lines). All post-processing was
conducted at the Oak Ridge National Laboratory Distributed Active Archive Center
(ORNL DAAC, 2010) prior to the data being made available for public use.
142
Chapter 6
SIMULATIONS OF TREE REMOVAL
6.1 Introduction
To simulate the influence of tree removal on water table depths, a groundwater
flow model was constructed. Modeling conducted as part of studies by Hammersmark et
al., 2008 and Smerdon et al., 2009 has been shown to be an effective tool in describing
current hydrologic conditions in settings similar to the study area as well as providing a
predictive tool of modified conditions.
The results of the physical and hydrologic
characterizations were used to define the physical constraints of the model as well as
provide the constraints of inputs to the model.
To maximize the “model fit”, the model was refined to the point that it best
represented the geological and hydrological conditions observed during field visits and
more importantly, the groundwater elevations recorded in the meadow through
monitoring. This chapter discusses the specific methods used to define the three
dimensional extent of the model, the boundary conditions, the sensitivity of key
parameters, and the results of tree removal simulations.
6.2 Methods
Selection of modeling method
The model was constructed utilizing Visual MODFLOW 4.0 (VMod), a
three-dimensional numerical finite difference modeling program based on code
developed by the USGS (Harbaugh et al., 2000). VMod is a graphical user interface
(GUI) developed by Waterloo Hydrologic that allows for manipulation of model
143
properties graphical and allows users that are not familiar with code manipulation to
effectively model subsurface conditions. This software and MODFLOW in general is
widely used in the hydrogeologic modeling community (Fetter, 2001) and was assumed
to be appropriate for the simulation of groundwater flow beneath the meadow.
Grid design and boundary condtions
The horizontal model grid domain was defined based on the surficial extent of the
meadow. Based on the results of grain size analysis discussed in Chapter 4, the model
was constructed as a single layer with homogeneous properties. The key assumption
associated with this method of modeling is that the model is isotropic in the x and y
directions. Although this condition is rarely observed in the field, the overall distribution
of mean grain sizes as well as the relatively consistent hydraulic conductivity results
indicates that this is an appropriate assumption for the purpose of model simulations for
this study.
The model was constructed of 35 rows and 48 columns consisting of individual
cells of 10 x 10 m. This cell size was selected to agree with the horizontal resolution of
the DEM used for surface topography. DEM values were imported into VMod to define
the model surface. The interpolated subsurface elevations obtained from seismic surveys
and hand auger borings were imported to define the bottom of the model. The bottom of
the model was assumed to be a no flow boundary due to the low permeability of the
underlying meadow bedrock. The meadow perimeter was also defined as no flow
boundary based on a similar rationale. The model as defined by the meadow boundary
was considered the active domain of model flow.
144
Boundary conditions are required for any simulation of groundwater flow
(Anderson and Woessner, 1992) and the input of multiple boundaries was needed for
defining the constraints of the meadow aquifer. Constant head boundaries were assigned
at the locations of piezometers PZ-1 near the inflow point to the meadow and PZ-4 near
the outflow point to the meadow.
Groundwater elevations at these points were
continuously monitored over the course of this study and these data were used to
represent the hydraulic head (head) as it enters and leaves the meadow aquifer. It should
be noted that the constant head boundaries not only included the model cell that
contained piezometer PZ-1 or PZ-4, but also the adjacent cells. This assumption appeared
to be valid based on the relatively flat meadow topography from northwest to southeast,
which is generally perpendicular to the meadow gradient.
Tells Creek was defined as a drain boundary. The use of this boundary type
requires input of the bottom elevation of the boundary across the entire horizontal extent
of the drain. Based on multiple measurements of channel depths, the bottom of the drain
was assigned at 0.32 m below the surface elevation of the model. When groundwater
elevations are below the bottom elevation of the drain, they are no longer impacted by the
drain and remain a part of subsurface flow in the model. The use of this boundary to
approximate the influence of Tells Creek on the shallow aquifer appears to be appropriate
as the creek was noted to have flowing water in the early part of June and was dry near
the middle of July. The locations and extents of the various boundary conditions
discussed above are shown on Figure 6-1.
145
Initial recharge estimates
As the meadow aquifer is unconfined, it is assumed that some measure of
recharge is added to the meadow flow budget throughout the year. During the winter and
spring months this recharge comes in the form of precipitation and snowmelt. However,
during the summer months, the source is not as clearly defined. Wood (1975) concluded
that many mountain meadows commonly have some component of recharge from
seepage from adjacent hill slopes and/or underlying fractured bedrock. He based his
conclusion in part on observed increases in meadow water table levels prior to any
notable precipitation events. This condition was also observed in Timothy Meadow as
water table elevations begin to rise following September 28, 2009 in the absence of any
recorded precipitation events. This appears to support the conclusion by Wood (1975)
that some measurable amount of recharge is added to the meadow throughout the year,
regardless of the timing of precipitation events. Based on this rationale, an arbitrary
initial recharge value of 0.001 m/d was input over the entire active model extent. The
entire model area was selected as to establish a baseline for determining the areas of the
meadow where recharge may be more of a factor when compared with others.
Steady-State simulations
As discussed in Chapter 5, daily average transpiration rates were determined for
the time period including the months of June, July, August, and September. This time
period was selected to agree with the observed decline in water table levels during the
2008-2009 water year as previously discussed.
As such, a steady-state model was
constructed to simulate the groundwater flow in the meadow under existing conditions
146
and then incorporate these rates into the model to simulate the effect of tree removal on
heads in the meadow. Existing conditions were simulated using constant head boundary
conditions on June 15th with input values obtained from monitoring data collected at PZ-1
and PZ-4. It should be noted that the 15th of June was arbitrarily selected and not as a
result of any particular observed condition on that day.
A steady-state model was
selected over a transient simulation due to the overall poor controls in recharge areas and
magnitudes throughout the meadow and more importantly, due to the limited number of
calibration points within the meadow (discussed below).
Model calibration
Due to monitoring points PZ-1 and PZ-4 being utilized to define the constant head
boundaries of the model, the two remaining piezometers (PZ-2 and PZ-3) were used to
calibrate the model. Oberserved heads at these points on the 15th of June were input as
calibration data to determine the “goodness of fit” of the steady-state model. The model
was determined to be of a good fit when the root mean squares (RMS) value was
minimized relative to RMS values from previous model runs (as described in Anderson
and Woessner, 1992).
Model parameters and sensitivity analysis
To effectively simulate the flow in the meadow subsurface, representative values
of hydraulic conductivity are needed as inputs to the model (Fetter, 2001). Based on the
results of hydraulic conductivity analysis discussed in Chapter 4, a singular value was
input for both the x and y directions. Specifically, Kx = Ky = 0.837 m/d. Kz was input at
10% of Kx or 0.0837. Also discussed in Chapter 4 were the results of the determination
147
specific yield. The average result of this analysis was determined to be 21%, which was
input into the model as an initial value. As part of the specific yield analysis, the
saturated water content was also determined for the most frequent grain sizes observed in
the meadow. This parameter is equivalent to total porosity and the result from this
analysis was input to the model at a value of 39%.
To determine the model sensitivity to variations of hydraulic conductivity (K)
when compared to recharge, input values of K were varied in the model over the range of
minimum and maximum values identified during the determination of average values
discussed above. As K values were varied, values of recharge were varied concurrently
to determine the increase or decrease of this parameter required to maintain model
calibration. The range of values of K used in this analysis was an order of magnitude
below and above the previously determined average value of 0.837 m/d. The average
value was representative of the silty sands and sandy silts that were most commonly
found in the meadow. The occasional discrete zones of clay/silt and gravel observed
during subsurface investigations were represented by values of 0.0837 m/d (minimum)
and 8.37 m/d (maximum), respectively.
To determine the model sensitivity to variations in specific yield, inputs were
varied in the model over the range of minimum and maximum values identified during
the determination of average value of 21%. The observed and calculated heads for the
calibrated model were used as a basis for comparison to determine the sensitivity of this
parameter. The range of specific yield values used in this analysis were 14% and 34%,
respectively.
148
Simulation of lodgepole pine tree removal
Following model calibration of the steady-state model under the range of values
of K and recharge, the daily average transpiration rate calculated for each month was
input into the model as recharge. The recharge boundary was used in this manner in order
simulate groundwater being placed back into the system due to the absence of tree
transpiration. Recharge rates due to tree removal were the same as transpiration rates
summarized on Table 5-3 and as follows: June (0.013 m/d), July (0.0173 m/d), August
(0.017 m/d), and September (0.0171 m/d). The areas of the model for these recharge
rates were defined by the extent of tree encroachment into the meadow (Figure 6-2).
Steady-state simulations using this range of values represent the response of the water
table to tree removal.
The calculated heads from the model calibrations (existing
conditions with trees in place) provided baseline data for quantifying the amount of the
change in head during these simulations.
6.3 Results
Determination of initial recharge and model calibration
As discussed above, the constraints of recharge to the meadow were initially
unknown during model construction and an arbitrary value of 0.001 m/d was input over
the entire active extent of the meadow to provide insight into the model response to this
variable. Model runs using this recharge value were found to significantly overestimate
heads by up to 2 m with RMS values of a similar magnitude at the locations of
piezometers PZ-2 and PZ-3 (calibration points).
Also notable during these initial
simulations was the presence of “dry cells” along the northeastern and southwestern
149
boundaries of the model. Dry cells in VMod represent calculated heads that are below
the bottom of the model. Based on field observations during June, these dry cells were
not representative of water table depths in these areas of the meadow. Therefore, in
addition to adjusting the recharge rate to the entire model, recharge zones (subsequently
referred to as the NE zone and SW zone) were defined along these areas of dry cells
(Figure 6-3).
Multiple model simulations were conducted to achieve initial model calibration
by varying values of recharge in the NE and SW zones as well as the entire active model
domain while other parameters remained constant (K = 0.837 m/d and Sy = 21%). The
model domain recharge value that was determined to provide the lowest RMS value for
the initial calibration was 0.0002 m/d when combined with variations in recharge values
input in NE and SW zones (0.008 m/d and 0.004 m/d, respectively). Table 6-1
summarizes the initial calibration results (observed and predicted heads) for the model.
The RMS value for this initial calibration was calculated to be 0.102 m. Heads were
slightly underestimated at calibration point PZ-2 and overestimated at PZ-3 during
simulations for the steady-state model.
Sensitivity analysis
The results of the sensitivity analysis performed for hydraulic conductivity (K)
when compared to recharge is summarized on Table 6-2 and depicted on Figure 6-4.
Varying K by an order of magnitude above or below the value used for initial calibration
(0.837 m/d) required an increase or decrease of the model domain recharge value of over
an order of magnitude, respectively, to maintain model calibration. Variations in K also
150
required a slight (less than an order of magnitude) increase or decrease in recharge values
for the NE and SW recharge zones to achieve a model fit similar to the initial calibration.
RMS values for model simulations using values of K an order of magnitude above and
below the initial value and utilizing the revised recharge values were similar to the value
for the initial calibration. Results of this analysis are summarized on Table 6-2.
The results of the sensitivity analysis performed for specific yield are summarized
on Table 6-3. The graphical relationship between the observed heads and the calculated
heads from variations in specific yield are shown on Figure 6-5. Variations in specific
yield did not result in any change in predicted heads when compared with those predicted
from utilizing the initial value of 21 %.
Simulation of lodgepole pine tree removal
The results of the simulations of lodgepole pine tree removal using the
steady-state model calibrated over a range of K values are summarized on Table 6-4.
Individual simulations were conducted using the various K values, calibrated by varying
recharge, and inputting the range of transpiration rates calculated for the meadow
lodgepole pines. The mean simulated change in head at the calibration points (PZ-2 and
PZ-3) ranged from 0.012 m for a K value of 8.37 m/d and a transpiration rate of 12.96
mm/d to 0.834 m for a K value of 0.0837 m/d and a transpiration rate of 17.32 mm/d.
Multiplying the range of changes in head by the surface area of the meadow (64,000 m2)
resulted in range of the change in meadow groundwater storage of approximately 160 m3
to 11,000 m3.
151
6.4 Discussion
The results of the initial calibration as well as subsequent calibrations of the
steady-state model indicate that reasonable approximations of actual condtions were
obtained. The significant lowering of the RMS values when compared to initial runs
appears to represent a good model fit on steady-state scale. Because this model was
simulated as steady-state, solutions are non-unique, but represent a first-order
approximation of observed condtions.
Based on results of the sensitivity analysis, the model was significantly sensitive
to hydraulic conductivity as it directly affected the amount of recharge needed to balance
the inputs and outputs to the model.
Decreasing the conductivity by an order of
magnitude resulted in a significantly higher predicted increase in heads due to lodgepole
pine removal when compared with higher values. This is likely a function of a decrease
in the ability of groundwater to leave the meadow aquifer due to a lower overall
permeability. The variability in changes in head and subsequently, changes in storage,
reflects the potential uncertainty associated with simulations of removal using the
methods described. Since the rates used to simulate the removal of lodgepole pines were
constant during simulations using various scenarios of K and recharge, it is apparent that
the amount of increased storage is highly dependent on the permeability of the subsurface
sediments for a given meadow.
In summary, additional constraints on the variations of K, which directly relates to
potential heterogeneity of the meadow sediments, would add greatly to future modeling
efforts. Most importantly, the need for additional points of calibration was apparent
152
throughout the modeling process. As is the often case with model simulations in general,
increases in field observations can greatly enhance model refinement for future
applications.
153
6.5 Tables and Figures
Observation
Point
Observed Heads
(m)
Calculated Heads
(m)
Difference (m)
PZ-2
2015.630
2015.541
-0.089
PZ-3
2015.360
2015.474
0.114
RMS Value (m)
0.102
Table 6-1: Summary of initial calibration data for the steady-state groundwater flow
model. Piezometers PZ-2 and PZ-3 were used as points in the model to determine the
differences between observed and calculated heads. The modeling software used (Visual
Modflow) calculated the root mean square (RMS) values for each model based on these
differences. The “goodness of model fit” was based on minimizing RMS values
compared to previous model runs.
154
K
(m/d)
0.837 (1)
0.0837
8.37
Model Domain NE Zone SW Zone
Observed
Observation
Calculated Difference
Recharge Recharge Recharge
Heads
Point
Heads (m)
(m)
(m/d)
(m/d)
(m/d)
(m)
2.0E-04
5.0E-06
3.0E-03
8.0E-03
1.3E-03
1.8E-02
PZ-2
2015.630 2015.541
-0.089
PZ-3
2015.360 2015.474
0.114
PZ-2
2015.630 2015.548
-0.082
PZ-3
2015.360 2015.462
0.102
PZ-2
2015.630 2015.550
-0.080
PZ-3
2015.360 2015.467
0.107
4.0E-03
RMS
Value
(m)
0.102
1.6E-03
0.093
8.0E-03
0.094
(1): K value as summarized on Table 4-8 and used in initial model calibration (average of slug testing and
soil moisture retention profiles)
Sy was fixed at 21% during all simulations for recharge sensitivity
Observed heads from June 15, 2009 were used for all model calibrations
Table 6-2: Summary of the sensitivity of recharge due to variations in hydraulic
conductivity (K). Values of K were varied by an order of magnitude above and below
the value used for initial model calibration (0.837 m/d) to determine the change in
recharge values required to maintain model calibration.
155
Predicted Heads
Observation Point
Observed Heads
Sy = 21% (1)
Sy = 14% (2)
Sy = 34 % (3)
PZ-2
2015.630
2015.541
2015.541
2015.541
PZ-3
2015.360
2015.474
2015.474
2015.474
(1): Sy value as summarized on Table 4-12 (average of empirical data and soil moisture retention
profiles)
(2): Minimum value noted on Table 4-12 for fine grained soils (Sandy Loam)
(3) : Maximum value noted on Table 4-11 based on soils reaching their wilting point
K was fixed at 0.837
Table 6-3: Summary of sensitivity analysis of specific yield (Sy). Minimum and
maximum Sy values obtained from soil moisture retention analysis of the meadow
sediments were input to the model to determine the change in predicted heads values
based on variation of this variable. All other model parameters were fixed during this
analysis.
156
Change in Head due to Tree Removal (m) (1)
K
(m/d)
Observation
Point
Calculated
Heads With
Trees in Place
Et = 12.96
mm/d
Et = 17.02
mm/d
Et = 17.12
mm/d
Et = 17.32
mm/d
PZ-2
2015.541
0.087
0.121
0.125
0.126
PZ-3
2015.474
0.129
0.180
0.185
0.186
Mean (m)
0.108
0.151
0.155
0.156
Change in
Storage(2) (m3)
1,400
2,000
2,100
2,100
PZ-2
2015.548
0.509
0.653
0.656
0.663
PZ-3
2015.462
0.769
0.990
0.994
1.004
Mean (m)
0.639
0.822
0.825
0.834
Change in
Storage(2) (m3)
8,600
11,000
11,000
11,000
PZ-2
2015.550
0.010
0.013
0.013
0.014
PZ-3
2015.467
0.014
0.019
0.019
0.020
Mean (m)
0.012
0.016
0.016
0.017
Change in
Storage(2) (m3)
160
210
210
230
0.837
0.0837
8.37
(1): Tree removal simulated by using the range of calculated transpiration estimates (Et) as
recharge inputs to the model in the area of existing tree encroachment
(2): Change in storage calculated by multiplying the mean change in head from tree removal
simulations by the surface area of the meadow (64,000 m2) and by a specific yield of 0.21
Table 6-4: Summary of model results of tree removal simulations for the steady-state
groundwater flow model. The range of calculated lodgepole pine transpiration rates (Et)
were input as recharge to the model over various values of K that the model was
calibrated for during recharge sensitivity analysis (Table 6-2). Piezometers PZ-2 and
PZ-3 were used as points in the model to determine the change in head by removing all of
encroaching lodgepole pine trees. Discrete values from these points were averaged to
represent the change in head throughout the meadow and were then multiplied by the
surface area of the meadow and the specific yield to determine the net change in storage.
157
Constant
Head
Drain
(Tells Creek)
N
Constant
Head
Meadow
Perimeter
Figure 6-1: Boundary conditions assigned in the model to simulate groundwater flow in
the meadow subsurface. The outline in the central portion of the figure represents the
meadow perimeter, outside of which model cells are inactive. Constant head boundaries
were assigned near the inflow (northeast portion) and outflow (southwest portion) of the
meadow. Tells Creek (perennially flowing from the northeast to southwest across the
meadow) was assigned as drain boundary.
158
Figure 6-2: Extent of model areas assigned as recharge zones to simulate tree removal.
The hatched areas represent the limits of tree encroachment into the meadow, which were
used as the basis for defining removal recharge zones in the model.
159
NE Recharge
Zone
SW Recharge
Zone
N
Meadow
Perimeter
Figure 6-3: Location of recharge zones assigned to the model during calibration. Two
areas (shown as darkened cells along the NE and SW portions of the meadow perimeter)
were utilized as areas of groundwater recharge to achieve model calibration.
160
1.0E-01
Model Domain Recharge (m/d)
1.0E-02
1.0E-03
K vs Recharge
1.0E-04
1.0E-05
1.0E-06
1.0E-07
0.0837
0.8370
8.3700
K (m/d)
Figure 6-4: Results of sensitivity analysis of hydraulic conductivity (K) compared to
recharge. Values of K were varied by an order of magnitude above and below the value
used for initial model calibration (0.837 m/d) to determine the change in recharge values
required to maintain model calibration.
161
2015.70
2015.65
Observed
Predicted
Groundwater Elevation (m)
2015.60
2015.55
2015.50
PZ-2
2015.45
PZ-3
2015.40
2015.35
2015.30
2015.25
2015.20
Observed
21%
14%
34%
Sy (%)
Figure 6-5: Results of sensitivity analysis of specific yield (Sy). Groundwater elevations
observed in the field and predicted by the model were compared based on variations in
Sy. Calibration points PZ-2 and PZ-3 were used as reference points in the comparisons.
162
Chapter 7
ANALYSIS AND CONCLUSIONS
7.1 Summary of Results
Numerous methods and techniques were utilized to test the hypothesis of this
study. The hypothesis tested was that the meadow water table was lowered as a result of
encroaching lodgepole pine tress and their removal can be effective in increasing
available groundwater storage. Results of the geological characterization show that the
current meadow configuration is the result of numerous geologic events ranging from the
tectonic scale emplacement of the Sierra Nevada batholith to meadow scale glacial
processes and alluvial deposition.
The hydrologic characterization of the meadow
vicinity indicates the meadow sits in a dynamic climatic environment with annual
occurrences of significant precipitation events of snow and rain.
The results of the physical characterization of the meadow indicate the meadow
subsurface is comprised of 130,000 m3 of unconsolidated sediment with average depths
of approximately 2 m. The surface area of the meadow is approximately 64,000 m2 as
determined by the surrounding topography. The meadow sediments are predominantly
composed of silty sands with occasional gravels. Average hydraulic conductivity and
specific yield values were determined based on combination of field methods and grain
size analysis with mean values of 0.837 m/d and 21%, respectively. Results of meadow
water table monitoring over the approximate extent of the 2008-2009 water year depicted
a meadow that is generally fully saturated from the late fall to the late spring followed by
an approximate 4 month period of water table declines to a maximum depth of 1.13 m
163
below the meadow surface. This drop in water level indicates that the meadow has
declined to 44% of total storage capacity when the water table reaches its lowest depth.
The specific period of water table decline was determined to be from June 5, 2009 to
September 28, 2009, which was used as the basis for determining transpiration estimates
of the encroaching lodgepole pines.
The results of quantifying the rate at which encroaching trees transpirate stored
groundwater from the meadow subsurface indicate daily rates of 12.96 mm/d for June,
17.32 mm/d for July, 17.02 mm/d for August, and 17.12 mm/d for September. These
rates were calculated utilizing a modified version of the Penman-Montieth equation,
which was specifically derived for coniferous plant species (Waring and Schlesinger,
1985). The determination of these rates using average monthly values of temperature and
relative humidity indicated a maximum error of 7.8 % when compared with using the
daily extremes in ranges of these variables. This estimate excludes a single outlier from
June of 41.8 % that was determined to not reflect overall meadow conditions over the
summer months.
The simulation of tree removal using a steady-state groundwater flow model, that
was calibrated by varying recharge while utilizing hydraulic conductivity (K) values an
order of magnitude above and below the average value (8.37 m/d and 0.0837), indicates
that the removal of encroaching trees results in an increase in water table levels ranging
from less than 2 cm to greater than 80 cm. This equates to an increase in groundwater
storage in the range of approximately 200 to 11,000 m3 (Table 6-4).
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7.2 Analysis
To determine the overall impact to meadow groundwater storage over the 20082009 water year due to tree removal, the increase in water table levels were used to
calculate the change in storage compared to the total storage capacity of the meadow.
The total storage capacity was calculated by multiplying the specific yield value of 21%
(as determined from multiple methods of analysis) by the total volume of sediment,
resulting in a total capacity of 27,000 m3. Increases in storage ranging from 200 to
11,000 m3 are equivalent to an additional 1 to 40 percent of the total meadow storage
being utilized.
The high variability in the overall range in change in storage estimates reflects the
model simulations of tree removal using the range of expected K values in the meadow
sediments (Table 6-4).
The average K value of 0.837 m/d was intended to be
representative of the silty sands and fine grained sands that were most commonly found
in the meadow sediments during field investigations and grain size analyses. Model
simulations of tree removal using this value resulted in an increase in storage of
approximately 2,000 m3. However, interfingered finer and coarser grained sediment
intervals (clays/silts and coarse sands with gravel) were also found during field
investigations and were verified by grain size analysis. The presence of this variability in
the meadow sediments necessitated that K values reflective of these two grain size endmembers be utilized in model simulations to evaluate the sensitivity of this variable to
tree removal simulations.
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Silt/clay intervals were reflected by a K value of 0.0837 m/d or an order of
magnitude below the average K value. Fetter (2001) equates this value in the range of K
values expected for sandy silts, clayey sands and till, which is consistent with the finer
grained intervals encountered in the meadow subsurface.
Gravel and coarse sand
intervals were reflected by a K value of 8.37 m/d or an order of magnitude above the
average K value. Fetter (2001) equates this value in the range of K values expected for
well-sorted sands, which is inconsistent with the poorly sorted nature of the meadow
sediments as encountered in the field and described by Coyle (1993) and Mitchell et al.,
(1993). This indicates that the value of 8.37 m/d may not be reflective of the coarser
grained intervals and is likely overestimated.
Change in storage values using the K values above when compared to the
change in storage using the meadow-specific average value are considerably higher
(~11,000 m3 using the value for clays/silts) or lower (~200 m3 for gravel and coarse
sands). However, due to the steady-state model being constructed as isotropic in the x
and y directions, simulations using these K values assume that the entire model
subsurface is the same (i.e. entirely consisting of clays/silts or coarse sands/gravels).
Based on subsurface investigation and grain size analysis, only a portion of the meadow
sediments consists of these intervals when compared to the silty sands and sandy silts that
were most commonly encountered.
It is evident that variability in K has a significant impact on the results of model
simulations of tree removal, but greater credence is given to the change in storage model
results using the average value, which was calculated using a combination of field
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analysis and empirical data based on the mean grain size distribution of the meadow
sediments.
As discussed above the change in storage result for the silts/clays in the
meadow is more reflective of actual field conditions when compared with that for gravels
and coarse sands. Therefore the change in storage resulting from tree removal is more
likely in the range from 2,000 to 6,500 m3 (or an additional 7 to 24 percent of the total
meadow storage being utilized). The upper end of this range assumes that the clay/silt
intervals are no more than 50% of the meadow sediments which is considered to be a
conservative estimate based on field investigations.
7.3 Conclusions
As this study demonstrates, lodgepole pine trees have the ability to remove
significant amounts of groundwater from a mountain meadow shallow aquifer through
transpiration. The rate and distribution at which transpiration occurs is based on
subsurface and climatic conditions. Given the relatively shallow water table depths
through out the year (typically less than 1 m) in Timothy Meadow, groundwater is
potentially available for tree uptake year round within the meadow boundary. It should
be noted that transpiration rates in this meadow during the winter and spring months are
likely to be significantly lower due to the existing snow pack and the cold temperatures.
Based on the time period of the observed decline of water table levels, any potential
transpiration during the winter or spring is offset by seasonal recharge from precipitation
and snowmelt.
Based on the results of modeling the removal of lodgepole pines over the range of
expected hydraulic conductivity values for the meadow sediments, additional constraints
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on the distribution and magnitude of this variable are needed to further refine estimates of
changes in storage due to tree removal. Although the modeling results indicate the range
of change in storage estimates span nearly two orders of magnitude, the use of field data
collected during this study allows for significant narrowing of this range. The collection
of additional hydraulic conductivity data should result in model simulations that are the
most reflective of actual meadow groundwater conditions, should tree removal be
conducted in the future.
The apparent relationship between storage losses and the encroachment of pine
tress observed at Timothy Meadow has the potential to be applied to other meadows at
similar elevations, including those with deeper seasonal water tables. Root systems
associated with the general species of Pinus contorta have been noted to reach depths of
up to 3.3 meters in boreal forests (Canadell, 1996) which represents a conservative
estimate of the maximum depth that this species can remove water from a system.
Additionally, root systems of Ponderosa Pine (Pinus ponderosa), which have been cited
as an encroaching species in Pacific Northwest meadows, have the ability to reach depths
on the order of tens of meters with 20 to 30 m being the upper end (Richards, 1986).
Conversely, the observations at Timothy Meadow may not be applicable to all
mountain meadows in general due to potential hydrologic limitations of groundwater
storage. Timothy Meadow is an example of a meadow that is hydrologically functional
and degradation such as incised channels, increased sedimentation, and overall poor
vegetation health was not observed. This indicates that groundwater storage in this
particular meadow is not limited by constraints such as dewatering due to incised stream
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channels. However, many meadows have been observed by researchers to be limited by
some or all of these factors. In these cases, the restoration alternative of tree removal may
not provide the storage benefits observed in Timothy Meadow. Tree removal may have a
localized effect on water tables in these meadows, but any meadow dewatering due to
degradation will likely continue be a limiting factor in maximizing storage volumes.
These factors should be considered when appropriating resources for meadow restoration.
Established methods of meadow enhancement such as channel restoration have also been
shown to be effective in increasing the overall hydrologic functionality of meadows.
In summary, the results of this study support the hypothesis that the removal of
encroaching lodgepole pine trees can be an effective method of increasing the available
groundwater storage in a mountain meadow over a time period when the majority of
meadows in similar environments exhibit storage losses. The additional storage that
results from the implementation of this method can provide a significant source of
recharge to downstream catchments when other sources of recharge such as precipitation
and snowmelt are limited or non-existent. The results of this study are intended to
provide forest managers with a restoration alternative to be considered when a meadow
appears to be functioning below its potential. It is recommended that the overall costs
and potential meadow impact associated with tree removal (such as clear-cutting) be
evaluated should this management alternative be considered.