QUANTIFYING HYDROLOGIC INTERACTIONS IN TUOLUMNE MEADOWS
______
A University Thesis Presented to the Faculty
of
California State University, East Bay
______
In Partial Fulfillment
of the Requirements for the Degree
Master of Science in Geology
______
By
Marcelino J. Vialpando III
March, 2018 ABSTRACT
High elevation meadows in the Sierra Nevada of California, USA represent mixing zones between surface water and groundwater. Quantifying exchanges between stream water and groundwater, and the residence time of water stored in meadow sediments allows for examination of a possible buffer effect groundwater has on meadows and streams during droughts and as climate change alters recharge conditions. Unraveling the complex hydrologic dynamics associated with these basins and quantifying input components is crucial for identifying sensitive influences on contributions to and from these alpine resources. This in turn has implications for the resilience of the ecosystems as well as the downstream communities that are dependent upon runoff for water supply.
Using interdisciplinary methods, this study applied multiple tracers, geochemical signals, and isotopic signatures to investigate the age, source, and evolution of groundwater discharge within the meadow. Chloride concentrations, which were found to be distinct between late season surface water and groundwater, were used to identify end member mixing components. 3H and 35S data provided estimates of mean water residence times, which were generally found to be less than two years for meadow groundwater and much longer for the deep system hosted by fractured bedrock.
Analyzing the high fidelity geochemical signal from deep bedrock sources, fractured flow contributions were calculated. The severe drought conditions in California at the time of sampling allowed for the quantification of deep fractured bedrock flow contribution, which was found to be a small percentage (<2%), and would otherwise be unlikely to have been calculated under normal hydrologic conditions. Radon analysis combined with
ii stream gauge data were used to identify segments of the river where water was gained or lost, including groundwater inflow ‘hot spots’ that coincide with stream geomorphic features. Seasonal variations were noted by comparing results from samples collected in
Fall 2014 and Summer 2015, and remarkably similar patterns suggest that stream morphology, rather than hydrologic conditions dictate surface water-groundwater exchange.
The comprehensive examination of hydrologic properties involved in this study confirmed the high level of exchange between river water and groundwater taking place within the meadow. Using two component mixing analysis, >50% of the total flow exiting the meadow was revealed to be sourced from groundwater. Further, assessing the nature of the groundwater and surface water components indicated that very recent recharge dominates the groundwater and seasonal precipitation moves through the system at a rapid rate.
iii
iv
ACKNOWLEDGMENTS
I would like to express my deep gratitude and great appreciation to Dr. Jean
Moran. I cannot express enough thanks for the seemingly endless patience she possessed during the journey of completing this thesis. Her invaluable technical support and advise, along with the supervisors and mentors at LLNL, allowed me to pursue this research and gain experience which otherwise would have been beyond my grasp. I received much generosity in the form of time, encouragement, and advise from Dr. Ate Visser and Dr.
Brad Esser whose mentorships played an integral role in this research. I am particularly grateful for their assistance along with Dr. Stephanie Uriostegui; the extensive laboratory instruction, learning opportunities, and technical experience provided to me is irreplaceable. A special thanks goes out to Amanda Lee Deinhart along with the NSF
RAPID program; the aid provided by both, though very different, were equally vital in supporting this study. I dedicate this thesis to my mother Loretta Vialpando, I am eternally grateful for your boundless love and emotional support.
v
TABLE OF CONTENTS
ABSTRACT…………………………………………………………………...... ii
ACKNOWLEDGEMENTS………………………………………………...... v
LIST OF FIGURES………………………………………………………...... vii
LIST OF TABLES………………………………………………………………...... x
INTRODUCTION………………………………………………………………………...1
BACKGROUND………………………………………………….………...... 10 Site description…….……………………………………………………...... 10 History………….…………………………………………….…………...... 13 Climate………….…………………………………………….…………...... 18 Hydrology……………………………………………….……………………….20 Water Quality and Diversions……………………………….…………………...24 Geology……………………………………………………....…………...... 26 Topography………………………………………………….…………………...31 Seismicity and Faulting…………………………………………………………..34
METHODS………………………………………………………………………...... 35 Field Methods……………………………...……………………………...... 35 Lab and Analysis Methods………………………...……………………...... 41
SAMPLING LOCATIONS AND ID’S………………………………………………….49
RESULTS AND DISCUSSION…………………………………………………………54 Radon…………………………………………………………………………….54 Stable Isotopes…………………………………………………………………...59 Anions……………………………………………………………………………66 Mixing……………………………………………………………………………72 Contribution from Deep Fracture Flow………………...………………………..79
CONCLUSION…………………………………………………………………………..81
REFERENCES…………………………………………………………………………..83
vi
LIST OF FIGURES
Figure 1 Photo of Tuolumne River in Tuolumne Meadows…………..…….……...2
Figure 2 Yosemite Map……………………………………………….……...……..3
Figure 3 Hydrograph of Tuolumne River…………………………….……...……...6
Figure 4 Monitoring well locations…………………………………….…...………7
Figure 5 Tuolumne River tributaries within the meadow…………....……...... ….11
Figure 6 Hetch Hetchy Project………………………………………………...…..12
Figure 7 Tuolumne Meadows cross section…………………………………....….13
Figure 8 Historic sheep grazing photo………………………………....……….....14
Figure 9 Vegetation of east Tuolumne Meadows...... 16
Figure 10 Photo of channel widening………………………………………..……...18
Figure 11 Average monthly temperatures of Tuolumne Meadows…………..……..19
Figure 12 Average monthly precipitation of Tuolumne Meadows…………….…...19
Figure 13 Yearly hydrographs of Tuolumne River flow at Tuolumne Meadows
a 2014………………………………………………………………………20
b 2015…………………………………………………………….………...21
c 2016……………………………………………………………....………21
d 2017……………………………………………………………….....…...22
Figure 14 Monitoring well transects in Tuolumne Meadows ...... 23
Figure 15 Tuolumne River water diversions…………………..……………………25
Figure 16 Tuolumne Intrusive Suite plutonic emplacement….…………….………27
Figure 17 Photo Cathedral Peak Granodiorite….…………………………………..28
vii
Figure 18 Photo Johnson Granite Porphyry……………………….………………..29
Figure 19 Geologic map of Tuolumne Meadows………..………………………….30
Figure 20 Topography of Tuolumne Meadows……..………………………………32
Figure 21 Primary sample locations along Tuolumne River…………..……………36
Figure 22 River discharge measurement method………..………………………….39
Figure 23 Helium isotope sampling apparatus……...... …………………………….41
Figure 24 Radon volatilization along a stream…………..………………………….43
Figure 25 Evolution of stable isotopes of water in precipitation……….…………..45
Figure 26 August 2013 sampling locations…………………………………………50
Figure 27 November 2013 sampling locations……………………………………...51
Figure 28 October 2014 sampling locations………………………………………...52
Figure 29 June 2015 sampling locations……………………………………………53
Figure 30 Graph of radon concentrations…………………………………………...54
Figure 31 Graph of 18O vs. distance………………………………………………...60
Figure 32 Graph of 2H vs. distance…………………………………...…………….61
Figure 33 Graph 18O vs. 2H for October 2014………..……………………………..63
Figure 34 Graph 18O vs. 2H for June 2015…………………………….……………64
Figure 35 Graph 18O vs. 2H for river samples October 2014………………….……65
Figure 36 Graph 18O vs. 2H for river samples June 2015………..………………….66
Figure 37 Graph Chloride by sample location ID……….………………………….69
Figure 38 Graph Chloride vs. Sulfate………………..……………………………...70
Figure 39 Graph Sulfate by sample location ID…………………..………………...72
viii
Figure 40 Graph of percent groundwater influx along the Tuolumne River...... 75
ix
LIST OF TABLES
Table 1 Sample location information, August and November 2013…...…………37
Table 2 Sample location information, October 2014 and June 2015…..…………38
Table 3 Radon concentrations (pCi/L)..…………………………………………..55
Table 4 Concentrations of stable isotopes of water (‰)………………...…..……62
Table 5 Chloride concentrations (mg/L)……………………………..………..….67
Table 6 Sulfate concentrations (mg/L)…………………………...……….………68
Table 7 Stream flow measurements (cfs)……………………...………...………..76
Table 8 35S concentrations (mBq/L)………………..……………………….……77
Table 9 Age calculation comparison using Tritium and 35S………………….…..77
Table 10 Tritium concentrations (pCi/L)…………………………………….…….78
x
1
INTRODUCTION
The biological integrity of Tuolumne Meadows is sustained by the Tuolumne
River [FIGURE 1] and, when combined with tributary areas of Dana Meadows and Lyell
Fork, comprises one of the most extensive Sierra complexes of riparian habitats and subalpine meadows (Cooper et al., 2006; NPS, 2014; SFPUC, 2007; SFPUC, 2008).
These meadows house a great diversity of species and highly productive plant life
(Naiman, Bunn, Nilsson, Petts, Pinay & Thompson, 2002; Ratliff, 1985; Verner & Boss,
1980). This scientifically important refuge, being located in a designated wilderness
[FIGURE 2], has some protection for the ecological integrity of its systems. However, the transition in these communities toward more xeric plant species is believed to have resulted from anthropogenic actions such as construction of roads and drainage ponds, extensive sheep grazing and more recently, heavy foot traffic across the meadows
(Babalis, Stromberg, Schaible & Torgerson, 2007; Cooper et al., 2006; Loheide &
Gorelick, 2007; Ratliff, 1985).
2
Figure 1: The Tuolumne River meandering within Tuolumne Meadows as seen from Medlicott Dome (NPS, 2014).
3
Figure 2: Map showing location of Tuolumne Meadows within Yosemite National Park (NPS, 2014).
To maintain long-term health of Tuolumne Meadows, restoration of the natural hydrologic processes must be considered so as to sustain the diversity of the interconnected ecosystems (Babalis, et al., 2007; NPS, 2014; Ratliff, 1985). The annual pattern of snowmelt and streamflow provides groundwater recharge that maintains the meadow (Loheide, Deitchman, Cooper, Wolf, Hammersmark & Lundquist, 2009; Wilson
& Guan, 2004). Changes to the annual groundwater dynamics render the meadows susceptible to effects that otherwise would not be significant (Lowry, Deems, Loheide,
Steven & Lundquist, 2010). A subtle shift in the timing, magnitude and duration of
4
annual spring runoff exemplifies stresses attributed to global climate change (Mote,
Hamlet, Clark & Lettenmaier, 2005; Stewart, Cayan & Dettinger, 2005; Viers et al.,
2013). Periods of low precipitation, especially when combined with ground disturbances by gophers, voles and heavy foot traffic in sensitive meadow habitats, produces less infiltration, decreased permeability, exacerbates habitat fragmentation and the expanse of areas of bare ground (Babalis et al., 2007; NPS, 2014). Further disruptions to the
“natural flow regime” will impact the function of processes upon which many native plants and animals are dependent (Cooper et al., 2006; Loheide & Gorelick, 2007; Ratliff,
1985). Changes to the annual flow pattern will result in deviations from natural hydrologic, geomorphic, and ecological processes which can jeopardize the reproductive success of species that have adapted to the usual seasonal variations experienced by the region (Lytle & Poff, 2004; Naiman et al., 2002; Nilsson & Svedmark, 2002).
The majority of the state’s precipitation (95%) falls during winter in the form of snow with the warm, dry summers having little to no precipitation. The availability of shallow groundwater for meadow vegetation is crucial and the reason these “wet meadows” are classified as groundwater-dependent-ecosystems (Boulton, 2005; Loheide et al., 2009). The relative aridity of the state makes water supply management problematic. Over thirty percent of California’s water is derived from underground sources, which provides drinking water for 43 percent of the state’s population, making it the single largest user of groundwater in the nation (CDWR, 2003). Exacerbating this issue is the state’s climatic variability which causes dramatic deviations to the average water supply conditions via floods and multiyear droughts. Examples of such droughts
5
have been recorded in 1912-13, 1918-20, 1922-24, 1929-34, 1947-50, 1959-61, 1976-77,
1987-1992 (CDWR, 1998; NPS, 2014). Groundwater usage during dry years can raise to over 60%. Study of the paleoclimate record found 3-year drought intervals with fairly persistent regularity, showing that multiple dry years are not outside the range of natural variability. However, the drought from 2012 – 2014 was exceptionally severe in the context of the last millennium with 2014 having had the greatest accumulated moisture deficit of at least the last 1200 years (Griffin & Anchukaitis, 2014).
Since primary recharge results from captured orographic precipitation being transported from mountainous watersheds, attempting to understand the intricacies of any regional aquifer system and its susceptibility to climate change requires investigation of the adjacent mountain recharge zones (Manning & Solomon, 2003; Singleton & Moran,
2009). The general lack of groundwater data, with regard to alpine environments, is largely due to the absence of wells in these sparsely populated areas where accessibility presents challenges for drilling. Arguably, the most difficult hydrologic parameter to quantify with any confidence is groundwater recharge (Voeckler, 2012; Lerner, Issar &
Simmers, 1990; Scanlon, Healy & Cook, 2002; NRC, 1996). Though recharge on the watershed-scale is the primary source of alpine groundwater, recharge within the meadow also occurs from local and stream infiltration (Lowry et al., 2010). This localized interaction between surface and groundwater responds to diurnal fluctuations caused by daily pulses of snow melt (Loheide & Lundquist, 2009) [FIGURES 3 & 4]. The resulting oscillations in the water table are found to be highly correlated with biota type and are the most important factor in determining the type of vegetation community (Loheide et al.,
6
2009; Ratliff, 1985). The availability of water and its resulting effect on the root system drives the biotic processes responsible for the distribution and domination of xeric (dry tolerant) versus mesic (water dependent) vegetation (Loheide & Gorelick, 2007; Viers et al., 2013). Though depth to the water-table was found to be an important factor in controlling plant species, additional controls are established by the period or duration of the elevated water-table, its rate of decline, and its final depth when the growing season ends (Lowry & Loheide, 2010; Nilsson & Svedmark, 2002).
Figure 3: Hydrograph of Tuolumne River (Cooper et al., 2006).
7
Figure 4: Monitoring well locations (Cooper et al., 2006).
While water availability is the primary controlling factor for vegetation, water temperature was found to be the most important factor in predicting mortality in river ecosystems, closely followed by flow or habitat-related mortality (e.g., dewatering, bed scouring) (Jager, 2000; Lytle & Poff, 2004). Female spawners do not migrate if the water temperature has significantly deviated from an identified temperature threshold. Egg mortality was found to be affected not only by extreme temperatures but also loss of habitat associated with extreme flows. Manifesting in dewatering and scouring of redds
(nests of gravel where eggs are deposited and incubate) all of which are associated with prolonged deviations from the natural hydrologic processes due to climate change
(Nilsson & Svedmark, 2002; Stewart et al., 2005). With such a low tolerance to elevated stream temperatures, high flow rates during late spring and early summer are essential for
8
certain species that can only endure a slow rise in river temperatures (Čada, Deacon, Mitz
& Bevelhimer, 1997; Kope & Botsfort, 1990; Williams & Matthews, 1995). Late season flux of cool groundwater into streams is likewise critically important for moderating stream temperature. Also, shifts toward higher fall flows followed by winter reductions in flowrate will compound the mortality (Jager, 2000; Lytle & Poff, 2004). In addition to controlling migration and stimulating reproduction, water temperature and flowrate also affect fish respiratory efficiency, metabolism, growth rates and feeding behavior in addition to chemistry of the river water including its dissolved oxygen concentration
(SFPUC, 2007; Jager, 2000). Although most rivers have biotic communities well adapted to the area’s characteristic hydrologic regime, prolonged deviations to the cyclic flow intervals experienced by the area on a daily, monthly, seasonal, annual, and interannual basis, places additional stress on the ecosystems (Naiman et al., 2002; Ratliff,
1985).
Cycles are most evident when observing changes in the surface water; diurnal fluctuations such as temperature and flow rate are easiest to observe in the river. These daily changes are directly correlated with soil temperatures and remaining snow pack amount (Flint, L., Flint, L. E. & Dettinger, 2008; Loheide & Lundquist, 2009; Lowry et al., 2010; Lundquist, Dettinger & Cayan, 2005). Flow rates decrease as remaining snow pack reduces. Surface temperatures increase as soil becomes more exposed, raising the temperature of the river. Near surface soil temperatures were found to mirror variations in surface temperature, however the signal is noticeably damped (Cey, Hudson, Moran &
Scanlon, 2009; Singleton & Moran, 2010). Study of these oscillations provide insight to
9
the physical, volumetric transformations or conversions taking place in the system.
Quantification of the chemical interactions and exchanges may only be gleaned through laboratory analysis. By providing measurable representations of otherwise imperceptible interactions, geochemical and isotopic assessments are invaluable hydrologic tools.
10
BACKGROUND
Site Description
Located at an elevation of 8,600 ft (2,622 m) above sea level, Tuolumne
Meadows is the largest subalpine meadow complex in the Sierra Nevada (Babalis et al.,
2007; Matthes, 1930) and is where the two principle sources of the Tuolumne River, the
Dana and Lyell Forks, converge [FIGURE 5]. The Dana Fork drains the west facing slopes of Mount Dana while the Lyell Fork begins at the base of the glacier on Mount
Lyell which is Yosemite’s highest peak at 13,120 ft (3999 m) elevation. The forks then convene on the east end of the meadow. The 155 mi (249.4 km) long Tuolumne River drains an area of approximately 1,900 mi2 (4,921 km2) as it flows into the Lower San
Joaquin River roughly 8 mi (12.9 km) upstream of the Stanislaus River confluence and is the largest of the three major tributaries of the San Joaquin River (SFPUC, 2008;
SWRCB, 2016) [FIGURE 6].
11
Figure 5: Map of Tuolumne Meadows showing tributaries and previous study locations (Cooper et al., 2006).
12
Figure 6: Map of the Hetch Hetchy Project, which in 1934 began to deliver water 167 mi (268.8 km) west to San Francisco from headwaters beginning near Tuolumne Meadows (Wikipedia).
After the meandering Tuolumne River flows through the meadow, it continues through the Grand Canyon of the Tuolumne, exhibiting stair-step river morphology, which transitions from calm stretches to spectacular cascades, repeatedly. After descending 5,100 ft (1,554 m) in elevation from the meadow, the river enters Hetch
Hetchy Reservoir where it is then diverted to provide municipal water supply and hydroelectric power (SFPUC, 2008; SWRCB, 2016).
Tuolumne Meadows itself is a broad alluvial plain owing its existence to a level valley through which the ancestral Tuolumne River flowed. A relatively low gradient allowed the sediment laden river to take on a lazy manner, depositing the glacial detritus acquired from higher reaches of the watershed. The resulting sediment deposition built up over time, becoming the broad alluvial plane that is the foundation of the meadow and provides the area’s greatest storage potential for groundwater [FIGURE 7]. Though the
13
variable depth of the meadow sediments constrains groundwater storage: its gentle slope
(less than 15%) contributes to prolonged inundation and soil saturation, essential during the growing season for the native plant communities (NPS, 2014; Reed, 1982;
USDA.NRCS, 2007). The organic material, comprising the thin topsoil layer of the meadow, resulted from early successional vegetation (Babalis et al., 2007; Ratliff, 1985).
Figure 7: Vertically exaggerated cross-sectional schematic representing subsurface stratigraphy along a line of monitoring wells transecting the Tuolumne River in a south to north orientation, near Budd Creek Tributary (modified from Cooper et al., 2006).
History
Historically, anthropogenic influences are believed to be predominantly responsible for hydrologic changes and effects to ecosystems within the meadow (Cooper et al., 2006; Loheide & Gorelick, 2007; Ratliff, 1985). Beginning with the development of what is now Tioga Road, this trail introduced rare access and passage through the
Sierra wilderness as far back as 4000 BCE (Babalis et al., 2007; NPS, 2014). Primarily used for trade, the first significant increase in traffic took place upon discovery of rich ore deposits during the mid-17th century. This discovery spurred visitation and business ventures by individuals hoping to exploit the arrival of transients and fortune seekers
14
who, themselves, hoped to discover the land’s relatively untapped resources. One of the first enterprises to capitalize on the new influx of visitors was the livestock industry; it arguably had the greatest and most lasting effect on the meadow.
Figure 8: Historic photo of sheep grazing in Tuolumne Meadows during late 1800’s (Cooper et al., 2006).
Rapidly increasing herds of sheep, running out of lush pasturage, were being driven ever deeper into the mountains [FIGURE 8]. This after a severe drought in 1864 decimated the prolific cattle herds which, up until then, had dominated the industry. The superior mobility of sheep left the herders and their flocks in a position to easily relocate and seek out the fertile vegetation of the high country. In their wake, the fragile riparian
15
habitats were denuded, and the ecosystems strained under the influences of the trampling, grazing masses of sheep.
Upon observing the devastation and potential lasting impacts the sheep were having on the meadow, a young Scotsman became affected by the beauty of Tuolumne
Meadows. The conclusions John Muir began to form during his summer as a hired hand in 1869 were solidified upon returning in 1889 where he and his friend Robert
Underwood Johnson witnessed the degraded environment with exposed barren dirt and eroded stream banks resulting from intensive grazing. Muir eventually helped found the
Sierra Club, a mountaineering group whose conservation efforts played a direct role in the safeguarding of Tuolumne Meadows by taking physical ownership of the meadow in
1912, becoming the third and last private owner the land would have before federal acquisition in 1973.
A desire to take advantage of the park’s dramatic views and increase accessibility while minimizing potential ecological impacts was a great consideration during the development and later realignment of Tioga Road. After the road was opened to the public in 1915 through the efforts of a Sierra Club alumnus, Stephen Mather, this created the first highway to traverse the mountains, opened up outdoor recreation opportunities and forever changing the demographic of visitors to the area. By this time, large scale grazing had essentially stopped within the meadow and though this allowed for the rejuvenation of much of its plant life, a new threat soon became apparent.
Granting public access with an ease never seen before, Tioga Road owes its existence to the desire of increasing appreciation for the wilderness and its natural
16
systems. Ironically, as the popularity of camping increased, so too had the land and its systems began showing signs of the demands associated with the new visitation it experienced. The soil compaction propagated by trampling hooves was replaced by tire treads; the degradation associated with defoliation by sheep now resulted from the actions and pollution of campers who were left unchecked and allowed to camp and roam at their leisure. The principles of selective placement and limitation were used to reduce human intrusions on the surrounding environment. By placing restraints on campers, vehicles and limiting their access to sensitive areas, the hope was to mitigate negative effects on the fragile vegetation and potentially allow restoration actions to begin (Cooper et al.,
2006; NPS, 2014) [FIGURE 9].
Figure 9: Map of east portion of Tuolumne Meadows showing effects on vegetation resulting from informal trails (NPS, 2014).
17
The first restoration activities were a part of the 1934 recommendations put forth by architect John Wosky in his development plan for Yosemite National Park. In his plan, Wosky envisioned limiting development and human intrusion by maintaining well- defined areas to consolidate human activity. The plan proposed obliterating all other physical development so as to restore the areas to natural conditions (Babalis et al.,
2007). However, the intention behind this initial restoration attempt was largely motivated by aesthetics and a desire to hinder future anthropogenic-related degradation.
Subsequent ecological restoration actions focused on restoring the natural meadow hydrology through reestablishing riparian vegetation along riverbanks and within the meadow (NPS, 2014). Disruption of the seasonal sheetflow across the meadow produced eroded cuts, incised channels, and ponded areas where surface flows are intercepted and become channelized. Habitat fragmentation and erosion in excess of natural rates was found occurring on the outer (cut) sides of meanders of the river within the meadow, resulting in channel widening (NPS, 2014) [FIGURE 10]. To abate conifer encroachment, manual removal of lodgepole pine saplings occurred for over 60 years by
NPS staff and volunteers. From 2006 to 2007 alone, over 70,000 saplings were removed, however removal ceased as of 2010, pending further research of the local ecology (NPS,
2014). The aim was to find the most effective way of restoring vegetation composition, stability, soil-forming processes, and belowground biomass (Cooper et al., 2006).
The meadow reach of the Tuolumne River has also been directly affected by diversions from the Dana Fork for the water supply for employees and visitors, which amounts to approximately 10% of the flow during the peak visitor season. Sometimes
18
called “the world’s most scenic sewage treatment ponds,” the hydrologic system is also affected by the wastewater treatment facility at Tuolumne Meadows, which includes the lined ponds visible to the north of the river on Figure 1.
Figure 10: Channel widening occurring on the outer (cut) side of the meandering Tuolumne River (NPS, 2014).
Climate
Situated at a high elevation, temperatures in Tuolumne Meadows are typical of mountainous regions and vary from a mean annual low of 10°F (-12.2ºC) December -
February, to a high of 72°F (22.2ºC) in July [FIGURE 11] (NPS, 2017). The bulk of precipitation received by the Sierra Nevada occurs during the winter months as snow, averaging 4.5 in (114.3 mm) per month from December-February, with little rain during summer, averaging 0.8 in (22.3 mm) per month from June-September [FIGURE 12].
Average annual precipitation in Tuolumne Meadows is 27.5 in (698.5 mm) (NPS, 2017).
There was a large degree of inter-annual variability in snow water equivalent (SWE) over the course of the study, with maximum SWE ranging from 5 in (12.7 cm) in water year
19
2014-15 to 19 in (48.3 cm) in 2015-16. In water year 2016-17 maximum SWE amounted to 46 in. (116.8cm). This weather pattern results from the Mediterranean climate experienced by California and is characterized by cold wet winters and warm dry summers. The Pacific storm track dominates the climate of California where storms originating from the Pacific Ocean migrate over land and release immense amounts of precipitation caused by orographic lifting as the clouds meet the mountains. This precipitation falls mostly on the western slopes of the Sierra Nevada and the other mountain ranges of California, leaving a rain shadow on most of the eastern slopes.
Snowmelt and rain falling in the mountains flows into the streams and rivers that ultimately feed municipal water supplies and primarily propels meadow hydrology.
Figure 11: Average monthly maximum and minimum temperatures of Tuolumne Meadows (NPS, 2017).
Figure 12: Average monthly precipitation of Tuolumne Meadows (NPS, 2017).
20
Hydrology Average annual flow (unimpaired) of the Tuolumne River at its confluence with the San Joaquin River is 1.85 maf (2.28 x 1012L) (SWRCB, 2016). Peak runoff is in May with high runoff observed from March to June. Minimum flows occur from August to
November [FIGURE 13]. Alpine meadows and valleys, such as Tuolumne Meadows, consisting of accumulated sediment, allow water to percolate into the ground through a relatively porous medium. The interaction between surface and groundwater inextricably links both in the hydrologic cycle (Lowry et al., 2010). Even though this link is common knowledge, the extent of the interactions, as well as the locations of stream loss or gain, have not been extensively studied for much of the Sierra Nevada. Reaches may switch between gaining and losing depending upon the season, hydrologic year type as well as human influences. a)
21
b)
c)
22
d)
Figure 13: Hydrographs of the Tuolumne River discharge at the top of Tuolumne Meadows during a) 2014, b) 2015, c) 2016, and d) 2017 showing peak flow due to snowmelt runoff occuring in early summer (CDWR, 2018). (Periods of no data appear as straight lines.)
Focusing on human-related impacts, Lundquist and others examined the hydrology of Tuolumne Meadows, concentrating on water year 2006 (Cooper et al.,
2006). During their study, the major contributions to Tuolumne River (Budd Creek,
Lyell, and Dana forks) were monitored in addition to water level measurements from the series of monitoring wells transecting the river [FIGURE 14]. The data were then used to identify discharge from different elevations and periods throughout the summer. During the first half of the summer a diurnal fluctuation is observed and well-coordinated with water level measurements in wells close to the river [FIGURES 3 & 4]. Water elevations in wells at further distances from the river still reflect the diurnal fluctuations but are out of phase. Subsurface water transport times were the suggested cause of the phase shift
23
(Cooper et al. 2006). When the tributaries, Budd and Unicorn Creeks, went dry during the late summer (September), a corresponding change in groundwater levels at the wells was observed. The diurnal fluctuations stopped, and groundwater levels dropped at much faster rates. This confirmed that meadow groundwater levels in the areas near the confluences are controlled by recharge from both creeks and are very sensitive to climatic shifts that might cause tributaries to dry out earlier (Cooper et al., 2006; Lowry &
Loheide, 2010).
Figure 14: Topographic map of Tuolumne Meadows showing location of monitoring wells transecting the Tuolumne River (Cooper et al., 2006). Wells able to be sampled for this thesis are found along transects T3, T2, and T1b.
24
Water Quality and Diversions
Downstream from the meadows, approximately 244 - 300 taf (3 - 3.7 x 1011L) are diverted from the Tuolumne River’s unimpaired flow for the municipal water supply and hydroelectric power of San Francisco each year (SFPUC, 2008; SWRCP, 2016). At
Hetch Hetchy Reservoir, these water diversions account for approximately 15 - 33% of the river’s unimpaired flow. The remainder flows into Lake Don Pedro where the New
Don Pedro Dam, the major dam on the Tuolumne River, provides hydroelectric power, irrigation, recreation, and flood control. [FIGURE 6]. Two major water diversions exist on the Tuolumne, below Hetch Hetchy [FIGURE 15]. The Turlock Irrigation District and the Modesto Irrigation District receive 537,685 af (6.6 x 1011L) and 315,912 af (3.9 x
1011 L) per year, respectively. In addition to irrigating the two districts, water is also supplied for municipal use for the city of Modesto (SWRCP, 2016).
25
Figure 15: Schematic diagram showing flow diversions and reservoirs in the Tuolumne River watershed (SFPUC, 2007).
The Environmental Impact Report from the Planning Department of San
Francisco Water System Improvement Program (WSIP) classified the water from the upper Tuolumne River basin as excellent in quality. The drainage from high-altitude granite and minimal human influences produces water very low in total dissolved solids and free of contaminants. This adds to the high-quality water in Hetch Hetchy Reservoir, which has a total dissolved solids concentration of less than 10 mg/L (SFPUC, 2008).
The headwater region of the river, including the meadows, is therefore critically important for the water supply for millions of people and for agriculture in the eastern
San Joaquin valley.
26
Geology
The geology of Yosemite is dominated by granite and granodiorite that underlies and is exposed throughout most of the landscape in the park (Bateman et al., 1983;
Huber, 1987). Granite, an igneous intrusive rock, crystallizes deep underground while cooling after being emplaced as magmatic bodies in the Earth’s crust. These large plutons can cover areas over 400 mi2 (1036 km2) and were the result of previously active adjacent subduction zones. Of the seven intrusive suites in Yosemite National Park, the
Tuolumne Intrusive Suite occupies almost 1/3 of the total park area and is the youngest and most extensive (Bateman, 1983; Huber, 1987; Matthes, 1930) [FIGURE 16]. The suites are members of the large plutonic amalgamation that is the Sierra Nevada
Batholith, which was emplaced during the Jurassic and Cretaceous periods between 85 and 120 Ma.
27
Figure 16: Plutonic emplacement of the Tuolumne Intrusive Suite along with visible outcrops near Tuolumne Meadows after subsequent uplift and erosion (Huber, 1987)
The headwaters of the Tuolumne River descend from Mount Lyell and Mount
Dana. The waters from the Lyell fork wind through the Half Dome Granodiorite and its two facies (porphyritic and equigranular) as well as the Granodiorite of Kuna Crest which is the oldest rock unit of the suite.
The oldest visible outcrops within the meadow are part of the Cathedral Peak
Granodiorite (Kcp) ca. 88.1 Ma. This medium grained, hornblende-biotite granodiorite has conspicuous blocky megacrysts of K-spar measuring 2-5 cm across [FIGURE 17] and
28
underlies the whole of Tuolumne Meadows and most of the surrounding region
(Bateman, 1983; Huber, 1987; Matthes, 1930). Outcrops may be seen throughout the meadow in upland areas including Lembert and Pothole Domes.
Figure 17: Example of bedrock in Tuolumne Meadows (Cathedral Peak Granodiorite) showing porphyritic texture and potassium feldspar phenocrysts (Huber, 1987).
29
Figure 18: Example of bedrock in Tuolumne Meadows (Johnson Granite Porphyry) showing potassium feldspar phenocrysts (Huber, 1987).
The other lithologies present in the meadow are part of the Johnson Granite
Porphyry (Kjp) ca.85.4 Ma [FIGURE 18]; It is the youngest and smallest rock body of the Tuolumne Intrusive Suite and may be the youngest granitic rock in the park
(Bateman, 1983; Huber, 1987). The unit outcrops primarily on the eastern half of the meadow, on the north side of the river [FIGURE 19].
30
Figure 19: Portion of the Tuolumne Meadows quadrangle, mapping the geology of the meadow and surrounding area. Outcrops of exposed bedrock (Kcp and Kjp) are visible within the meadow (Bateman, 1983).
Overlying the granite is approximately 3 to 9 ft (1 to 3m) of Quaternary alluvium
(Qal), which is composed chiefly of sand and gravel and varies in depth across the meadow. This porous layer provides the main medium for groundwater storage and subsurface flow (Ciruzzi & Lowry, 2017). Finally, at the surface, an organic, carbon-rich soil composed of clays, silts, and organic debris, approximately 12 to 16 in (30 to 40 cm) thick sustains the highly productive and diverse riparian habitats associated with this
31
subalpine meadow (Babalis et al. 2007; Bateman, 1983; Cooper et al., 2006;
USDA.NRCS, 2007).
Topography
The landscape of Tuolumne Meadows is a geomorphic anomaly; vertical rock walls and granitic outcrops imply a degree of bedrock resilience that is in contrast to the erodibility indicated by the broad, flat segment of the river housed within the meadow
(Becker, Tikoff, Riley & Iverson, 2014) [FIGURE 20]. Given its elevation, the portion of the river within the meadow is exceptionally flat and broad. The whole meadow and much of the region is underlain by a single lithology [FIGURE 19]. The presence of these geomorphic differences signifies erosional variability which ultimately implies the greatest control on landscape evolution is not host lithology but rather the degree of bedrock fracturing (Becker et al., 2014).
32
a)
b)
Figure 20: Topography of Tuolumne Meadows a) Portion of the fifteen-minute quadrangle of Tuolumne Meadows and b) LIDAR map showing the geomorphic distinction of the meadow juxtaposed against the surrounding high relief topography.
33
Existing outcrops such as Lembert Dome and Pothole Dome are relatively free of fractures, resulting in their geologic resilience and topographical prominence (Matthes,
1930). The open topography of the meadow is believed to contain a high concentration of tabular fracture clusters making the rock particularly susceptible to erosion. A study by Becker and others (2014) correlated erosion rates with orientation and concentration of bedrock fractures. Though the Cathedral Peak Granodiorite is one of the least erodible of Yosemite’s lithologies, within Tuolumne Meadows, its erosion is among the greatest.
Becker concluded that a short-lived fracturing event was caused by fluid release from the Johnson Granite Porphyry during its Cretaceous emplacement and subsequent crystallization. This caused the fracture clusters found only in the surrounding Cathedral
Peak Granodiorite, predisposing segments of it to high erodibility. A prominent
“knickpoint” separating the Grand Canyon of the Tuolumne from Tuolumne Meadows is another example of resilience accompanying the absence of fracture clusters which are concentrated near the middle of the meadow. Although direct observation of the bedrock is obscured by Quaternary sediments, there are indications that fracture clusters are abundant throughout Tuolumne Meadows. Concentrations of fracture clusters are visible in bedrock slopes to the north and south, and strike into the meadow from both sides.
Porosity in the meadow is therefore dominated by glacial-floodplain alluvium, with some potential secondary porosity from fractures in the granite.
34
Seismicity and Faulting
Tuolumne Meadows is located in a relatively inactive seismic area. The closest documented fault to Tuolumne Meadows is the Silver Lake fault which lies approximately 9.3 mi (15 km) east of the study area. This high-angle normal fault was found to have a late Quaternary vertical slip rate of 0.02 in/yr (0.5 mm/yr) by Clark and others (1984) who measured a vertical offset of Tahoe-aged moraines of 98 – 164 ft (30 –
50 m). However, Bryant (1984) stated that this middle to early-late Pleistocene rate is not representative of the Holocene rate, which is slower and similar to other faults in the region, citing a lack of offset of latest Pleistocene and Holocene deposits. The most recently active faults are located approximately 34 mi (55 km) southeast of the study area and form the Hilton Creek fault zone where in May 1980 there were four Mw 6+ earthquakes along this zone. Though the zone is considered to be active, the faults are not likely to be conduits for groundwater flow in the meadow. Fractures and faults along the hillside, however, may allow for deep circulation of meteoric water which then emerges as spring discharge (Clow, Mast, & Campbell, 1996).
35
METHODS
Field Methods
Surface water samples were collected along the 3.1 mi (5 km) stretch of the
Tuolumne River located within the meadow at 14 predetermined locations spaced roughly 0.19 mi (0.3 km) apart. Groundwater was primarily drawn from 3 monitoring wells spaced roughly 0.6 mi (1 km) apart with the first well located approximately 0.6 mi
(1 km) from the first surface water sampling point [FIGURE 21]. The Lawrence
Livermore National Laboratory LLNL Groundwater Ambient Monitoring and
Assessment (GAMA) program protocols were adhered to during collection and handling of all water samples (Belitz, Dubrovsky, Burow, Jurgens & Johnson, 2003; Beller et al.,
2005; Visser, Moran, Singleton & Esser, 2014). Groundwater samples were drawn using a peristaltic pump and all sample locations had field parameter measurements taken using a Thermo Scientific multi-probe meter after stabilization. Multi-meter measurements include: pH, oxygen-reduction potential (mV), conductivity (µS/cm), dissolved oxygen
(mg/L), barometric pressure, and temperature (C°). Tables 1 and 2 list information about sample locations, including GPS coordinates and analytes.
36
Figure 21: Google Earth image of Tuolumne Meadows showing primary sample locations of surface (TM-01 to TM-14) and groundwater (Wells 23, 48d, 70, and Soda Spring) along the Tuolumne River (red).
37
Table 1: Listing sample location ID’s, names, collection dates and other information including analytes for August and November 2013.
LLNL Sample Location Collection Sample ID Name Date Latitude Longitude
Elevation (m) Time Radon Stable Isotopes Tritium Anions NGMS 35 Sulfur Copper Tubes NGMIMS Multiprobe Discharge 110383 TR-07 8/24/2013 37.88683 -119.38807 2610 X X 110384 TR-08 8/24/2013 37.87620 -119.35467 2622 X X 110385 TR-09 8/24/2013 37.87823 -119.36432 2615 X X 110386 TR-10 8/24/2013 37.87334 -119.37110 2613 1147 X X X 110387 TR-11 8/24/2013 37.87429 -119.37429 2612 1237 X X 110388 TR-12 8/24/2013 37.87607 -119.38425 2612 1321 X X 110482 Dana Fork 11/5/2013 37.87725 -119.33955 2630 1033 X X 110483 S of TM-01 bridge 11/5/2013 37.87575 -119.35455 2607 1109 X 110503 Snow 11/5/2013 37.87620 -119.35541 2623 X X X 110488 Soda Spring 11/5/2013 37.87863 -119.36664 2623 1356 X X X X X 110487 TM-01 11/5/2013 37.87631 -119.35503 2622 1243 X X 110504 TM-01 11/5/2013 37.87631 -119.35503 2622 X X 110490 TM-01.5 11/5/2013 37.87740 -119.35703 2618 1254 X X 110485 TM-02 11/5/2013 37.87835 -119.35960 2617 1306 X X 110491 TM-02.5 11/5/2013 37.87770 -119.36143 2615 1317 X X 110481 TM-03 11/5/2013 37.87845 -119.36324 2615 1327 X X 110489 TM-04 11/5/2013 37.87726 -119.36610 2615 1338 X X 110486 TM-05 11/5/2013 37.87732 -119.36727 2615 1424 X X 110501 TM-06 11/5/2013 37.87593 -119.36845 2614 X X X 110484 TM-07 11/5/2013 37.87422 -119.36830 2613 1454 X X 110492 TM-08 11/5/2013 37.87334 -119.37110 2613 X 110493 TM-08 11/5/2013 37.87334 -119.37110 2613 1445 X X 110500 TM-09 11/5/2013 37.87429 -119.37429 2612 1434 X X 110499 TM-10 11/5/2013 37.87622 -119.37809 2612 1419 X X 110498 TM-11 11/5/2013 37.87617 -119.38448 2611 1400 X X 110497 TM-12 11/5/2013 37.87884 -119.38474 2611 1344 X X 110496 TM-13 11/5/2013 37.88312 -119.38360 2610 1326 X X 110494 TM-14 11/5/2013 37.88613 -119.38709 2610 1256 X X 110495 TM-14 11/5/2013 37.88613 -119.38709 2610 1311 X X 110480 Well #1 11/5/2013 37.87703 -119.34383 2647 859 X X X X X X X
38
Table 2: Listing sample location ID’s, names, collection dates, and other information including analytes for October 2014 and June 2015.
LLNL Sample Location Collection Sample ID Name Date Latitude Longitude
Elevation (m) Time Radon Stable Isotopes Tritium Anions NGMS 35 Sulfur Copper Tubes NGMIMS Multiprobe Discharge 111125 Dana Fork 10/19/2014 37.87725 -119.33955 2630 1600 X X X X X X X X X 111121 Soda Spring 10/19/2014 37.87863 -119.36664 2623 1127 X X X X X X X X 111124 TM-01 10/19/2014 37.87631 -119.35503 2622 1450 X X X X X X X X 111123 TM-02 10/19/2014 37.87835 -119.35960 2617 1400 X X X X X X 111120 TM-03 10/19/2014 37.87845 -119.36324 2615 1300 X X X X X 111119 TM-04 10/19/2014 37.87726 -119.36610 2615 1106 X X X X X X X X 111118 TM-05 10/19/2014 37.87732 -119.36727 2615 1050 X X X X X 111117 TM-06 10/19/2014 37.87593 -119.36845 2614 1035 X X X X X 111116 TM-07 10/19/2014 37.87422 -119.36830 2613 909 X X X X X X X X X X 111115 TM-08 10/19/2014 37.87334 -119.37110 2613 847 X X X X X 111112 TM-09 10/18/2014 37.87429 -119.37429 2612 1430 X X X X X X X X X 111113 TM-10 10/18/2014 37.87622 -119.37809 2612 1600 X X X X 111114 TM-11 10/18/2014 37.87617 -119.38448 2611 1700 X X X X X X X 111108 TM-12 10/18/2014 37.87884 -119.38474 2611 1030 X X X X X X X X 111109 TM-13 10/18/2014 37.88312 -119.38360 2610 1148 X X X X X X 111110 TM-14 10/18/2014 37.88613 -119.38709 2610 1220 X X X X X X X X X 111122 Well 23 10/19/2014 37.87699 -119.36705 2615 1230 X X X X X X X 111111 Well 48d 10/18/2014 37.87406 -119.37460 2612 1500 X X X X X X X X 111107 Well 70 10/18/2014 37.87876 -119.38508 2611 952 X X X X X X X X 111513 Budd Creek 6/24/2015 37.87370 -119.38155 2613 1105 X X X 111512 Unicorn Creek 6/23/2015 X X X 111499 TM-01 6/23/2015 37.87631 -119.35503 2622 1530 X X X X X X X 111500 TM-02 6/24/2015 37.87835 -119.35960 2617 950 X X X 111501 TM-03 6/24/2015 37.87845 -119.36324 2615 935 X X X 111502 TM-04 6/22/2015 37.87726 -119.36610 2615 1852 X X X 111503 TM-06 6/23/2015 37.87593 -119.36845 2614 1500 X X X 111504 TM-07 6/23/2015 37.87422 -119.36830 2613 1545 X X X X 111505 TM-08 6/22/2015 37.87334 -119.37110 2613 X X X 111506 TM-09 6/22/2015 37.87429 -119.37429 2612 X X X X 111507 TM-10 6/23/2015 37.87622 -119.37809 2612 1455 X X X 111508 TM-11 6/23/2015 37.87617 -119.38448 2611 1435 X X X X X 111509 TM-12 6/23/2015 37.87884 -119.38474 2611 X X X 111510 TM-13 6/23/2015 37.88312 -119.38360 2610 1250 X X X 111511 TM-14 6/23/2015 37.88613 -119.38709 2610 1325 X X X X 111514 Well 23 6/22/2015 37.87699 -119.36705 2615 1820 X X X X 111516 Well 46d 6/22/2015 1700 X X X X X 111517 Well 48d 6/22/2015 37.87406 -119.37460 2612 1600 X X X X X X 111518 Well 69 6/23/2015 1115 X X 111515 Well 70 6/23/2015 37.87876 -119.38508 2611 1240 X X X X X X X
Discharge rates were measured using a velocity-area approach [FIGURE 22].
After selecting channel locations, a cross-sectional grid was developed, establishing rectangular subsections 1 ft (0.33 m) in width. Each subsection was marked along a line anchored on each stream bank and stretched just above the water level. Using a current-
39
meter (Global Water FP111), average velocities were measured in each subsection by moving the meter smoothly at a steady pace, up and down from riverbed to water surface
5 times during measurement. Flow in each cross-sectional area was calculated by multiplying width and depth of each subsection and multiplying by the average measured velocity. Total discharge was then calculated by combining the measurements from each subsection.
Figure 22: Cartoon illustrating discharge calculation method using sectional velocity measurements (USGS, 2018).
Radon activity analysis was performed on unfiltered samples collected using a curved syringe and injected into 25 mL glass bottles containing 10 mL of mineral oil.
The water sample was injected below the oil and stored upright on ice so as to minimize volatilization of the dissolved radon gas. Samples were then refrigerated until analyzed
(within 2-3 days) at LLNL.
40
Stable isotope analysis was performed on unfiltered samples collected in 30 mL square clear glass bottles with Qorpak™ polyseal-lined caps. Sample bottles were triple rinsed prior to sample collection and stored at room temperature until analysis took place at California State University, East Bay (CSUEB).
Tritium (3H) analysis was performed on unfiltered samples collected in 1 L
Pyrex™ glass bottles, triple rinsed and sealed with a polypropylene seal cap. Samples were stored at room temperature until analyzed at LLNL.
Anions analysis was performed on filtered samples using disposable plastic high capacity filters and collected in 125mL plastic Nalgene bottles that have been triple rinsed prior to sample collection. After collection, samples were stored on ice then kept refrigerated until analyzed at Lawrence Livermore National Laboratories (LLNL).
Noble gas concentration analysis was performed on unfiltered samples collected in four 40 ml amber colored VOA vials (VWR TraceClean™, amber borosilicate;
0.125in septa liner). The vials were triple rinsed prior to collection and sealed with no head space. Samples were stored on ice then refrigerated until analyzed (within 2-3 days) at LLNL.
35S analysis was performed on samples collected in 20 L plastic bottles, triple rinsed and stored at room temperature until analyzed at LLNL.
Excess helium and helium isotope analysis was performed on samples collected in two copper tubes approximately 11.8 in (30 cm) in length, mounted on an aluminum rack secured with stainless steel clamps at both ends [FIGURE 23]. A peristaltic pump was connected to a copper tube with Tygon tubing, attached with a hose clamp at one end.
41
The pump was run for several seconds and the copper tube tapped multiple times to purge the apparatus of air bubbles prior to sealing. To seal, the stainless-steel clamp on the end not connected to the Tygon tubing was first tightened with a socket wrench until fully clamped and a gas-tight seal achieved, followed by tightening the second steel clamp.
Duplicate samples were collected and stored at room temperature until analyzed at
LLNL.
Figure 23: Copper tube sample apparatus for excess helium and helium isotope ratio.
Lab and Analysis Methods
Radon analysis has been established as a useful tool to assess groundwater/surface water interactions, determine discharge locations, and estimate other hydrological properties (Ellins, Roman-Mas, & Lee, 1990; Guida, M., Guida, D., Guadagnuolo,
Cuomo & Siervo, 2013; Lamontagne & Cook, 2007; Mullinger, Binley, Pates & Crook,
42
2007). A natural radionuclide, Radon-222 (222Rn) is the daughter product of Radium-226
(226Ra), both members of a decay sequence starting with Uranium-238 (238U). Found ubiquitously, uranium is in all rock types with higher levels in igneous rocks (especially high in granite). Groundwater concentrations are directly related to aquifer lithology which controls the amount of dissolved aquifer solids, supplying the soluble parent nuclide (226Ra). The chemically and biologically inert daughter product (222Rn) becomes concentrated in the aqueous phase by diffusing into water occupying pores of the subsurface rock formation. The elevated concentration of the radon-enriched groundwater, which travels conservatively due to its inert nature, is typically one order of magnitude higher than surface water concentrations (Rogers, 1958). Influx locations to surface flow may then be identified by anomalously high radon activity. Upon discharge, atmospheric exposure will cause radon activity to rapidly decrease downstream of influx points due to the volatility of this noble gas and its short half-life (t1/2 = 3.82 days)
[FIGURE 24]. The radon emanation rate from the stream is dependent upon the water temperature, the stream width and depth, and stream morphology, with turbulent flow having a higher emanation rate than laminar flow. Radon concentrations (222Rn) were measured on a Perkin Elmer Quantulus 1220 liquid scintillation counter located at LLNL.
This method measures radioactive gamma activity using direct liquid scintillation counting, reported in units of picocuries per liter (pCi/L)(Prichard & Gesell, 1977;
Prichard, Venso & Dodson, 1992). Uncertainty is approximately 10%.
43
Figure 24: Radon emanations from degassing stream flow (modified from Alley, Healy, LaBaugh & Reilly, 2002).
Stable isotope data have been used to gain insight as to the potential hydraulic connectivity of fractured bedrock aquifers and their contributions to rivers as well as identifying sources and timing of groundwater (Ajami, Troch, Maddock, Meixner &
Eastoe, 2011; Heilweil, Solomon, Gingerich & Verstraeten, 2009; Hunt, Coplen, Haas,
Saad & Borchardt, 2005; Plummer, Busenberg, Böhlke, Nelms, Michel & Schlosser,
2001; Scanlon et al., 2002). Being part of the water molecule makes the stable isotope ratios of hydrogen (δ2H) and oxygen (δ18O) ideal for use as a conservative tracer.
Evaporation and precipitation produce elevated levels of 18O (compared to 16O) and 2H
(compared to 1H) due to the greater diffusivity and vapor pressure of 16O and the
44
relatively large mass difference between protium (1H) and deuterium (2H). The Rayleigh adiabatic condensation process states that air masses become preferentially depleted in
18O and 2H as they move across continents and lose their moisture as they become colder with altitude. However, evaporation enriches surface water in 18O relative to 2H leading to seasonal variations that allow 18O/16O and 2H/1H ratios to be used for source water identification and groundwater flow timing (Heilweil et al., 2009; Hunt at al., 2005)
[FIGURE 25]. Mean residence time can also be estimated using observed stable isotope composition in spring discharge if the seasonal variation in local precipitation is known.
This calculated age will include the residence time of water in the unsaturated zone which is not available with age calculations derived from 3H/3He ratios that marks only the time at which gas exchange has ceased due to confinement at the water table (Plummer et al.,
2001). Stable isotope concentrations for hydrogen (δ2H) and oxygen (δ18O) were measured using a laser isotope analyzer from Los Gatos Research located at CSUEB.
Obtained values were reported in per mil and relative to the Vienna Standard Mean
Ocean Water (VSMOW) with analytical uncertainties for δ2H and δ18O are 1‰ and
0.3‰, respectively.
45
Figure 25: Changes to stable isotope ratios of water resulting from climatic variation.
Further evaluation of recharge conditions can be made using the supersaturated dissolved gas concentrations present in natural groundwater (Klump et al., 2007). Where turbulent, rapid recharge occurs in fractured rock, higher levels of ‘excess air’ may be found. This is typically caused by the entrapment of air bubbles which dissolve under increased hydrostatic pressure as the water table quickly rises during recharge (Manning
& Caine, 2007; Ajami et al, 2011). A SRS RGA200 quadrupole mass spectrometer was used to measure Ne, Kr, and Xe concentrations while 3He/4He isotopic ratios were found using a VG-5400 noble gas mass spectrometer (NGMS) and a high-sensitivity capacitive manometer is used to measure Ar. For these processes, samples collected in copper tubes
46
are attached to pneumatic valves connected to stainless steel vessels enveloped in dry ice to cryogenically separate the dissolved gases. The valves are controlled by LabVIEW software which regulates the opening and closing of the valves while monitoring the pressure to ensure no leaks are present in the system manifold. After removing the dry ice, one clamp on the copper tube is released, allowing the water sample to enter the stainless-steel vessel. To separate the dissolved gas, heat guns are positioned under each vessel and allowed to run for 15 minutes before the vessels are once again submerged in dry ice which freezes the liquid and isolates the gas. Before the gas enters the manifold, it is first sent through titanium-zircon getters leaving only nonreactive noble gases. A coldfinger containing activated charcoal receives the gas sample and has liquid nitrogen poured on until the heavy noble gases (Ar, Kr, Xe) in the sample are sorbed on to the charcoal when the temperature drops to below 83° Kelvin. A second charcoal filled coldfinger is cooled to 8° Kelvin to separate helium and neon which then enters a sector field mass spectrometer using Faraday cup collectors to measure the isotope 4He which has an abundance several orders of magnitude larger than the 3He isotope. The 3He isotope is measured using an electron multiplier collector. Analytical uncertainties are
1% for 3He/4He; 2% for He, Ne, and Ar; 3% for Kr and Xe (Singleton & Moran, 2009).
Since Ne is the most soluble gas next to He, it’s concentration divided by the equilibrium solubility is used to express calculated excess air (ΔNe). Three models of excess air have been proposed by Cey and others (2009) and are used to calculate recharge temperature and excess air. In these models only Ne, Ar, Kr, and Xe concentrations are used, being there are no known significant terrigenic sources.
47
Terrigenic He is known to be derived from earth’s crust and mantle. To separate the
3 3 terrigenic sources ( Heter) from the total measured ( Hetot), the dissolved helium in
3 3 solubility equilibrium with the atmosphere ( Heeq), and the excess air ( Heexc) are taken into account in the equation below:
3 3 3 3 3 [1] Hetot = Heeq + Heexc + Heter + Hetri
Tritium (3H) concentrations were measured so as to calculate initial tritium present at the time of recharge and then used to determine the sample’s apparent age
(Beyerle et al., 2000; Ekwurzel et al., 1994). To do so, a 500ml sample undergoes the same heat, chill, and pump sequence described above for vacuum degassing with the sequence repeated a total of three times. The sealed vessels are then removed and stored for a minimum of three weeks, allowing the 3H to decay and the daughter product 3He to accumulate. The 3He is measured on the VG-500 NGMS and referenced against a NIST
3 standard with a known tritium concentration. The decay to He (t1/2 = 12.43 years) is now used to calculate age of water (t = years) or time that the water has been isolated from gas exchange with the atmosphere (Aeschbach‐Hertig, Schlosser, Stute, Simpson,
Ludin & Clark, 1998) with an uncertainty of approximately 1 yr (Esser et al., 2009;
Schlosser, Stute, Dörr, Sonntag & Münnich, 1988) using the below calculation:
[2] 3 T Hetri t 1/ 2 ln 1 ln 2 3H
35 2- The anionic form of sulfate ( SO4 ) is relatively conservative in groundwater and
35 the relatively short half-life of S (t1/2 = 87 days) makes it another useful tracer in shallow groundwater systems and for determining if the system’s recharge is likely from
48
recent precipitation. Cosmic ray spallation with 40Ar forms the 35S which is then oxidized and falls to land in precipitation when infiltrates into the groundwater. 35S activity was measured on a Perkin Elmer Quantulus 1220 liquid scintillation counter located at LLNL following sulfate extraction in a procedure described in detail by
35 Uriostegui & Bibby (2013) and Uriostegui et al. (2015). This procedure measures SO4 activity via low-level liquid scintillation counting, achieved by first preconcentrating approximately 100mg of SO4 when a 20L sample is passed through a column containing
Amberlite-400 ion-exchange resin using a peristaltic pump or gravity filtration. After the
SO4 has been eluted off the resin, it is collected on glass fiber filters when the SO4 has precipitated as BaSO4. Another method uses a ‘scintillation cocktail’ solution consisting of dissolved Na2SO4 crystals converted from the SO4.
- - - - 2- Analysis of major anion concentrations (F , Cl , Br , NO3 , SO4 ), most
- 2- importantly Cl and SO4 , also took place at LLNL using a Dionex ion chromatography instrument (model DX-600) with an analytical uncertainty (1σ) of approximately 10%.
Studying the variations of ion concentrations in stream discharge has proven useful for differentiating between various hydrological input components (Carreira et al., 2011;
Esser et al., 2009) and was used in this study to estimate contributions as is discussed in the “Mixing” portion of the Results and Discussion section.
49
SAMPLING LOCATIONS AND ID’S
Sample collection was first conducted on August 24, 2013. A survey was performed along the section of Tuolumne River that runs through Tuolumne Meadows, and measured 3.51 mi (5.65km) in total length. Six locations were selected for stream sampling and titled by the order in which they were collected, TR-07 to TR-12 [FIGURE
26]. The first river sample, titled TR-07, was collected where the survey terminated. A spill pool resides at this “knickpoint” marking the end of the meadow after which point the river begins to cascade. The second sample location, TR-08, is on the north side of the Tioga Road bridge where the river first enters the meadow. TR-09 is approximately
0.12 mi (200m) upstream of the bridge to Soda Spring. TR-10 is at the southernmost point of the river’s course within the meadow. TM-11 is approximately the middle point of the meadow and TR-12 is where Budd Creek, the largest of the four seasonal tributaries, intersects the river. River water was collected from all 6 locations for Radon analysis and one location (TR-08) for 35S.
50
Figure 26: Google Earth image of Tuolumne Meadows showing sample locations along the Tuolumne River (red) for August 2013.
November 3, 2013 is when sampling began a second time, with a total of 16 samples collected along the Tuolumne River during this trip [FIGURE 27]. Except TR-
09, all previous sample locations were identified by GPS, sampled, and retitled according to their location downstream from the easternmost point of the meadow; the sampling point at the Tioga Road bridge, TR-08, later renamed TM-01. Two additional locations were sampled, titled TM-1.5 and TM-2.5, otherwise all locations were labeled using whole numbers (TM-01 to TM-14). Average downstream distance between sampling points was 0.25mi (0.4km). Water was also collected from Soda Spring and the only snow sample to be collected for this study underwent the only 35S analysis during this trip. Sampling on this occasion was primarily for anions and radon.
51
Figure 27: Google Earth image of Tuolumne Meadows showing increased sample locations along the Tuolumne River (red) for November 2013.
Eleven of the driest months on record (Griffin & Anchukaitis, 2014) would pass before the next trip began on October 18, 2014. The 14 whole numbered locations along the river (TM-01 to TM-14) were again located by GPS and sampled along with Soda
Spring and three wells positioned within the meadow (Wells 23, 48d, 70) [FIGURE 28].
On this occasion samples and data were collected for all major analytes and analyses used in this thesis, including stable isotopes of hydrogen and oxygen, dissolved noble gas concentrations, tritium, 35S, radon, and stream discharge data. Although samples were not collected from all 14 locations for each individual analysis, the data are sufficient for an overall interpretation.
52
Figure 28: Google Earth image of Tuolumne Meadows showing sampled well locations (purple) along the Tuolumne River (red) and closest adjacent river sample locations (blue) for October 2014.
The final sampling trip began June 22, 2015. Of the 14 previously sampled river locations, all except TM-05 were located, and water once again collected for analysis of radon, stable isotope and anions. Two additional wells were able to produce enough water to be sampled (wells 46d, 69) along with the three sampled in October [FIGURE
29]. This was also the first instance that two of the four tributaries (Unicorn and Budd
Creeks) had adequate flow for sampling. Discharge measurements were carried out on five points along the main river (TM-1, TM-7, TM-09, TM-11, TM-14). Five 35S
53
samples were collected, two from the river (TM-01 and TM-11), two from wells (Wells
48d and 70) and one from Budd Creek.
Figure 29: Google Earth image of Tuolumne Meadows showing sampled well locations (purple) along the Tuolumne River (red) and closest adjacent river sample locations (blue) for June 2015.
54
RESULTS AND DISCUSSION
Radon
Radon concentrations from all four sampling trips are shown in FIGURE 30 and
TABLE 3. In general, radon activity in surface water samples is about ten times lower than groundwater due to the volatility of dissolved radon gas when exposed to the atmosphere. However, compared to 222Rn activities in losing streams, streams traversing non-granitic terrain, and streams with high discharge (Rogers, 1958; Mullinger et al.,
2007), 222Rn measured in the Tuolumne River during summer months is quite high and none of the samples from within the meadow area was below the detection limit.
Radon (pCi/L) 700
600
500
400
300
200
100
0
Aug-13 Nov-13 Oct-14 Jun-15
Figure 30: Graph of radon concentrations along the Tuolumne River from all 4 sampling trips showing spatial and temporal variability with consistent inputs at three sample locations (TM-04, 07, 11).
55
Table 3: Radon concentrations Radon Concentration (pCi/l)
Sample Location Aug. 2013 Error Nov. 2013 Error Oct. 2014 Error Jun. 2015Error TM-01 39.66 8.76 199.88 22.49 106.61 16.67 30.18 11.15 TM-02 158.36 20.29 73.95 14.36 18.62 8.52 TM-03 655.80 26.49 138.72 19.05 196.07 21.68 61.95 13.32 TM-04 368.60 29.51 437.41 31.70 36.28 12.99 TM-05 334.53 27.87 317.91 27.20 TM-06 267.85 25.69 401.66 30.36 TM-07 531.33 34.21 637.47 37.90 159.59 21.90 TM-08 64.15 17.68 321.6235 30.68893 370.50 29.27 64.20 16.45 TM-09 69.53 10.45 305.40 33.41 353.47 30.54 87.86 18.67 TM-10 342.88 35.12 348.76 30.26 63.25 14.39 TM-11 104.42 12.08 471.87 40.55 497.89 35.78 124.47 19.32 TM-12 465.60 40.24 450.66 34.51 95.40 17.82 TM-13 380.99 36.67 369.02 31.25 108.40 18.84 TM-14 105.70 11.99 395.19174 37.17646 309.04 28.77 95.68 17.41 Dana Fork 91.79 16.60 8.61 7.93 Soda Spring 10949.60 152.75 10698.43 153.70 Budd Creek Unicorn Creek 131.81 21.58 Well 1 8815.14 141.75 Well 23 2488.81 74.30 2166.34 83.05 Well 46d Well 48d 4770.27 108.38 4147.69 116.89 Well 69 Well 70 1677.77 195.85 2227.89 78.86
A study by Guida et al. (2013) investigated spatial and temporal variability of radon concentrations in surface and ground water to characterize points of influence where groundwater inflow is identified by anomalously high 222Rn activity in stream water samples. The highest concentrations were found immediately downstream of lateral springs whose water originated from karst aquifers registering the highest radon levels of the study. Similarly, the highest radon reading of our stream water samples is found in August 2013 (but not during subsequent sampling) at the location closest in
56
proximity to Soda Spring (TR-09, later renamed TM-03). Samples from Soda Spring, when tested in November 2013 and October 2014, contained radon levels that were never less than 10,000pCi/L (Nov 2013: 11,094.8pCi/L; Oct 2014: 10,698.43pCi/L). To assess the level of mixing and the spring’s likelihood of influencing the river, further analysis was performed using additional analytes and is discussed in the next section.
Of the six samples collected on August 24, 2013, sample TR-08 (later renamed
TM-01), from just south of the highway 120 bridge where the river enters the meadow, recorded the lowest 222Rn activity of the samples for that set of samples (39.7 pCi/L).
The last sample to be taken before the river exits the meadow (TR-07; later renamed TM-
14) is approximately 5.65 km (3.51 mi) downstream from TR-08 and recorded the second highest concentration (105.70pCi/L) revealing a subtle yet positive trend downstream across the meadow. Although sampling density on this trip was fairly limited, the observed positive trend was confirmed by subsequent field expeditions. Increased radon concentration provides validation that the river is continually receiving groundwater contributions.
The second round of samples, collected on November 3, 2013, increased the sample locations to 16 and may comprise the most extensive hydrologic chemical analyses ever performed in the area on this section of the Tuolumne River. With the improved spatial resolution, the data paint a more complete picture and some notable variations were observed. Foremost, the river’s generally gaining trend, detected by the
August sampling, is now revealed to be significantly diverse. Again, the lowest radon activities were located upstream (toward the east) with a net increase in activity across
57
the meadow. Three peaks were detected, TM-04: 368.60 pCi/L, TM-07: 531.33 pCi/L, and TM-11: 471.87 pCi/L. These activities are higher than radon activities typically observed in stream water (LaMontagne & Cook, 2007) and likely reflect both the U-rich lithology and relatively high proportion of groundwater influx in the meadow. It should be noted that TM-07 and TM-11 are both locations where two tributaries (Unicorn and
Budd Creeks) intersect the river [FIGURE 29]. Though the tributaries are visibly dry by this time of year, the radon concentrations suggest subsurface flows may persist throughout the season and/or stream morphology allows upwelling of groundwater in those areas. This is consistent with the time of year (autumn) when water contributions from snowmelt have ceased, leaving only baseflow. The tributaries descend from lakes nested among the nearby peaks of Cathedral, Echo, Unicorn, and Johnson. Lakes
Elizabeth and Budd provide the seasonal flow to Unicorn and Budd Creeks, which intersect with Tuolumne River at TM-07 and TM-11, respectively.
Water samples were next collected almost one year later, for two days, beginning
October 18, 2014. The locations were identified using GPS coordinates and surface water collected from all previously sampled sections with the exceptions of TM-01.5 and
TM-02.5, resulting in 14 total sample locations along the river. The resulting concentration profile [FIGURE 30], in general, closely mirrors that of the November
2013 analysis, with three spikes detected at the same locations (TM-04: 437.41 pCi/L,
TM-07: 637.47 pCi/L, and TM-11: 497.89 pCi/L). If the radon concentrations are used as direct evidence of groundwater influx locations, the data shows the greatest influx of
58
groundwater to be where the peak concentrations were observed, namely at TM-07, TM-
11, and TM-04.
The final round of sampling was conducted between June 22 and June 24, 2015.
Again, the 14 locations sampled in October were identified by GPS and all were sampled with the exception of TM-05, reducing the locations to 13 for this trip. The concentrations in this instance closely match those found in August 2013, differing by 20 pCi/L at most, with peak concentrations found again at TM-07 and TM-11. The profile overall is consistent with that of the previous two rounds.
In summary, both groundwater and stream water radon activities are quite high in this setting, and a generally increasing trend downstream indicates generally gaining conditions throughout the meadow. The August and June sample sets are in overall agreement with a couple of outliers, and the October and November sample sets are remarkably similar. Groundwater influx ‘hot spots’ are identified by particularly high activities, and the consistency in observed patterns on different dates indicate that the locations of groundwater influx are likely controlled by morphologic features rather than hydrologic factors. Observing vegetation patterning near abandoned river meanders further corroborates the important role sediment deposition variations play in hydrologic functionality (Loheide et al., 2009).
59
Stable Isotopes
Overall, stable isotope signatures in waters from Tuolumne Meadows are very light and reflect the fractionation brought about by the orographic effect, whereby high elevation regions on the west side of the Sierra Nevada crest receive precipitation that is strongly depleted in the heavier isotopes. Analysis of samples collected in October 2014 and June 2015 revealed less seasonal variation than observed for radon [FIGURES 31 &
32]. The seasonal variations are not easily observed when solely examining surface water data; average ẟ18O for both October and June are remarkably similar at -13.4‰, despite the different time of year. However, comparison of spring and well water from both sampling dates established that groundwater is lighter in June (Soda Spring: -16.8 to
-17.5; Well 70: -14.5 to -15.9) [TABLE 4]. Surface water values ranged from -13.8‰ to
-13.0‰ +/- 0.1 in October and -13.7‰ to -13.1‰ +/- 0.3 in June. Groundwater from the wells ranged -13.8‰ to -14.5‰ +/- 0.1 in October and -15.5‰ to -15.9‰ +/- 0.3 in June, a seasonal difference of less than 2‰.
60
δ18O (‰) vs. Distance (m) 6000 5000 4000 3000 2000 1000 0 -12 TM-04 TM-02 TM-11 TM-10 TM-03 TM-14 TM-13 TM-12 TM-09 TM-07 TM-01 -13
Oct-14 -14 Soda Spring TM Well 70 TM-06 TM-08 TM Well 70
Unicorn Creek -15 Jun-15 O O (‰)
18 Soda Spring δ Unicorn Creek -16 TM Well 70 Soda Spring
-17
-18 Distance (m)
Figure 31: Graph of 18O vs. distance.
61
δ2H (‰) vs. Distance (m) 6000 5000 4000 3000 2000 1000 0
Unicorn Creek TM-12 TM-09 TM-07 -97 TM-13 TM-10 TM-03 TM-01 TM-14 TM-11 Oct-14 -102 Soda Spring TM Well 70 TM-02 -107
TM-08 TM-04 Jun-15
H H (‰) 2
TM-06 δ Soda Spring -112 Unicorn Creek TM Well 70 Soda Spring TM Well 70 -117
-122 Distance (m)
Figure 32: Graph of 2H vs. distance. Ratios of stable isotopes of water for samples collected October 2014 and June 2015. Although the patterns observed in δ18O and δ2H are similar, 2H is more strongly affected by evaporation, which results in a shift toward heavier values in October compared to June.
62
Table 4: Ratios of stable isotopes of water in per mil relative to SMOW.
Deuterium versus ẟ18O plots [FIGURES 33 & 34] show that the well data falls between Soda Spring and the stream values. In October, all groundwater values
(excluding Soda Spring) fall slightly to the right of the Global Meteoric Water Line
(GMWL), clustered together with the river water samples, with little difference between the two. This likely signifies rapid infiltration and groundwater recharge supplied primarily by the Tuolumne River resulting in the very similar isotopic signatures.
63
Oct-14 -18 -17 -16 -15 -14 -13 -12 -100
-102 TM Well 48 d -104 TM Well 70 -106 River samples -108 Soda Spring TM Well 23 TM Well 23
-110 H H (‰)
2 TM Well 48 d δ -112 TM Well 70 -114 GMWL
-116 Soda Spring -118
-120 δ18O (‰)
Figure 33: Graph of 18O vs. 2H for October 2014. Stable isotopes of water for surface and groundwater samples, plotted with the Global Meteoric Water Line.
64
Jun-15 -18 -17 -16 -15 -14 -13 -12 -85 Budd Ck -90 Unicorn Creek River samples -95 TM Well 46 d Soda Spring -100 Unicorn Creek TM Well 69 Budd Ck
-105 H H (‰)
2 TM Well 46 d δ -110 TM Well 69 TM Well 70 TM Well 70 -115 GMWL Soda Spring -120
-125 δ18O (‰)
Figure 34: Graph of 18O vs. 2H for June 2015. Stable isotopes of water for surface and groundwater samples, plotted with the Global Meteoric Water Line.
Groundwater in June exhibits a distinct geochemical separation from the surface water. The well results migration to the left of the GMWL is a reflection of the isotopically lighter water generated by regional precipitation patterns. This localized signature in the wells is more pronounced in June, the separation caused by the seasonal infiltration of a very high elevation-derived, unevaporated snowmelt component into the system appearing in the groundwater in June, while in October the stream and groundwater appear to have the same source.
Both data sets show the river water samples grouped tightly, laying close, and to the right of the GMWL, signifying some degree of evaporation. The June samples are clustered closer to the GMWL than October, yet some evaporation is evident on both
65
dates [FIGURES 35 & 36]. In each case, the samples found to be the most isotopically depleted are from Soda Spring where the source is primarily high elevation snowmelt that does not experience evaporation before it permeates the ground at a significantly higher elevation. The water exhibiting the heaviest isotopic signature comes from Budd Creek, where the relatively enriched water is derived from a smaller, lower elevation catchment.
-14.5 -14 -13.5 -13 -12.5 -100 -101 -102 -103
H H (‰) -104 2 δ -105 -106 -107 δ18O (‰) GMWL Oct-14
Figure 35: Graph of stable isotopes of water for surface water samples in October 2014, plotted with the Global Meteoric Water Line.
66
-14.5 -14 -13.5 -13 -12.5 -97 -98 -99
-100
H H (‰) 2
δ -101 -102 -103 δ18O (‰) GMWL Jun-15
Figure 36: Graph of stable isotopes of water for surface water samples in June 2015, plotted with the Global Meteoric Water Line.
Anions
- - - - 2- Major anion analysis (F , Cl , Br , NO3 , SO4 ) was performed on samples from
- 2- November 2013, October 2014, and June 2015. Cl and SO4 concentrations were used most extensively for interpretation [TABLES 5 & 6]. Although TDS and all anion concentrations are quite low, the variation observed is useful for examining surface
- 2- water-groundwater interaction. Of the anions, Cl and SO4 are the most likely to behave conservatively, are somewhat higher in abundance, and have been useful in previous hydrologic studies of the Sierra Nevada (Clow, Mast & Campbell, 1996).
67
Table 5: Chloride concentrations Chloride Concentration (mg/L) Sample Location Nov. 2013 Error Oct. 2014 Error Jun. 2015 Error S of TM-01 bridge 14.43 0.40 TM-01 15.40 0.40 13.57 0.67 2.87 0.65 TM-01.5 15.30 0.40 TM-02 15.17 0.40 13.45 0.67 3.59 0.65 TM-02.5 14.93 0.40 TM-03 14.62 0.40 13.23 0.67 3.67 0.65 TM-04 14.48 0.40 12.33 0.67 2.68 0.65 TM-05 14.60 0.40 12.38 0.67 TM-06 12.15 0.67 3.34 0.65 TM-07 13.07 0.40 11.06 0.68 2.32 0.65 TM-08 12.87 0.40 11.10 0.68 2.63 0.65 TM-09 13.05 0.40 11.50 0.69 2.66 0.65 TM-10 12.66 0.40 11.04 0.69 3.26 0.65 TM-11 12.26 0.40 9.34 0.68 3.25 0.65 TM-12 11.28 0.40 9.82 0.68 3.57 0.65 TM-13 11.25 0.40 9.59 0.68 3.37 0.65 TM-14 9.88 0.40 8.61 0.68 3.27 0.65 Dana Fork 11.73 0.40 11.78 0.67 Soda Spring 61.23 0.46 59.94 0.77 Budd Creek 0.40 0.65 Unicorn Creek 0.43 0.65 Snow 0.64 0.41 Well #1 1.09 0.41 Well 23 5.58 0.68 Well 46d 0.66 0.65 Well 48d 2.61 0.69 1.68 0.65 Well 69 0.84 0.65 Well 70 4.43 0.69 1.97 0.65
68
Table 6: Sulfate concentrations Sulfate Concentrations (mg/L) Sample Location Nov. 2013 Error Oct. 2014 Error Jun. 2015 Error S of TM-01 bridge 3.15 1.24 TM-01 2.78 1.24 3.26 0.74 2.33 0.70 TM-01.5 2.79 1.24 TM-02 2.80 1.24 3.25 0.74 2.26 0.70 TM-02.5 2.85 1.24 TM-03 2.85 1.24 3.16 0.74 2.26 0.70 TM-04 2.92 1.24 3.11 0.74 2.28 0.70 TM-05 2.95 1.24 3.17 0.74 TM-06 3.12 0.74 2.24 0.70 TM-07 2.80 1.24 2.80 0.74 1.77 0.70 TM-08 2.92 1.24 2.88 0.74 2.20 0.70 TM-09 2.87 1.24 3.30 0.74 2.22 0.70 TM-10 2.85 1.24 3.23 0.74 2.19 0.70 TM-11 2.87 1.24 2.61 0.74 2.12 0.70 TM-12 2.74 1.24 2.78 0.74 2.18 0.70 TM-13 2.67 1.24 2.80 0.74 2.16 0.70 TM-14 2.45 1.24 2.55 0.74 2.13 0.70 Dana Fork 4.08 1.23 3.92 0.74 Soda Spring 21.34 1.20 21.43 0.72 Budd Creek 0.60 0.70 Unicorn Creek 0.62 0.70 Snow 0.84 1.25 Well #1 13.16 1.21 Well 23 2.49 0.74 Well 46d 1.53 0.70 Well 48d 1.01 0.74 1.10 0.70 Well 69 2.00 0.70 Well 70 2.60 0.74 3.12 0.70
The lowest observed anion concentrations were from snow sampled in November, and from the tributaries Budd and Unicorn Creeks in June; again, this was the only time the creeks had visible flow. Water from Soda Spring had the highest concentrations by several orders of magnitude, (Cl: 61.23 mg/L; SO4: 21.34mg/L), but does not appear to significantly affect the river. A slight increase in both chloride and sulfate is seen at the
69
sample locations downstream of Soda Spring (TM-05, TM-06), however they are modest compared to changes further down the river.
Chloride (mg/L) vs. Sample Location ID 18.00
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00 16 14 12 10 8 6 4 2 0
CL Nov-13 CL Oct-14 CL Jun-15
Figure 37: Chloride concentrations along the Tuolumne River
A plot of chloride vs. distance [FIGURE 37] reveals apparent trends for each trip and demonstrates the inverse relationship between chloride concentration and stream flow. The trend occurring in October and November is characterized by relatively high concentrations during periods of low stream flow. Very low humidity in Tuolumne
Meadows during summer and fall promotes increased evaporation which leads to the enriched concentrations. Decreasing chloride trends with distance downstream, identified in the October and November data, are remarkably similar. November values, slightly higher than October, result from a further decreased precipitation component and
70
greater evaporative concentration. Comparing sulfate vs. chloride activity [FIGURE 38],
June stream samples are grouped more tightly, at lower concentrations, with little variation between surface water sites. The seasonal difference observed by this comparison is further evidence for the increase in hydrologic homogenization during high flow in June.
4.5
Dana Fork - Nov 13 4 Dana Fork - Oct 14 13-Nov 3.5 Well 70 - Jun 15 Snow - Nov 13 Dana Fork - Nov 13 3 Well 70 - Oct 14 14-Oct
2.5 Dana Fork - Oct 14
Well 48d - Oct 14
(mg/L) 4
2 Well 70 - Oct 14 SO 15-Jun 1.5 Well 48d - Jun 15 Well 48d - Jun 15 1 Well 70 - Jun 15 Well 48d - Oct 14 Linear (13-Nov) 0.5 Snow - Nov 13 Linear (14-Oct) Linear (15-Jun) 0 0.0 5.0 10.0 15.0 Cl- (mg/L)
Figure 38: Anion concentrations, sulfate vs. chloride for river samples collected November 2013, October 2014, and June 2015. Groundwater and additional samples are labeled.
Well samples were found to have lower chloride and sulfate concentrations than the river [TABLES 5 & 6]. This is in contrast to the pattern typically observed in surface water-groundwater systems where exchange with alluvial materials or rock aquifers
71
increase dissolved solids in groundwater along flow paths (Clow et al., 1996). Well 70 was the only exception, having sulfate levels higher in June compared to river water; in
October, it switched to being among the lowest. In every round of sampling, the groundwater showed a clear chemical distinction separating it from the river samples which are all grouped together.
June chloride concentrations along the river, in general, took on a slightly increasing trend with distance downstream; concentrations were between 2.0 ppm and 4.0 ppm. Though the concentrations in June are dilute compared to October, the analyses showed similar points with slightly higher values at TM-03, TM-06, TM-12 and lower values at TM-04, TM-07, and TM-11.
Exploring sulfate concentrations along the river revealed an overall decreasing trend with values from TM-14 being lower than TM-01 for each sampling event
[FIGURE 39]. As with chloride, sulfate concentrations were lowest in June, with
October and November readings being higher and comparatively similar. October’s sulfate concentration profile has greater variation than that of chloride in terms of amplitude, but the overall locations of observed maxima and minima are consistent. TM-
07, specifically, is the only location to consistently witness a drop in both chloride and sulfate measurements during all three months and is the single outlier seen among the
June river samples. TM-11 observed a drop in both readings during June and October but with a slight increase in November. The low values further substantiate the interpretation of the radon data indicating groundwater influx at those locations. The much higher concentrations in Soda Spring are evidence for distinct, deeper, and longer flow paths
72
leading to the spring discharge area. However, the relatively low anion concentrations across the meadow attest to the vigor of this hydrologic system that limits water/rock interaction preventing significant ion exchange, particularly during high flow. The granitic nature of basement rocks, their resilience to chemical weathering, steep flowpaths, and a relatively thin top soil also lead to low dissolved solids.
Sulfate (mg/L) vs. Sample Location ID 3.5
3.3
3.1
2.9
2.7
2.5
2.3
2.1
1.9
1.7 16 14 12 10 8 6 4 2 0
SO4 Nov-13 SO4 Oct-14 SO4 Jun-15
Figure 39: Sulfate concentrations along the Tuolumne River
Mixing
Because of the distinct groupings between stream water and groundwater based on anions and other parameters, two component mixing analysis can be applied to further examine surface water-groundwater interaction. Tracer-based, multi-component mixing
73
models allow for hydrograph separation techniques to be utilized in identifying principal source components of surface runoff (Burns et al., 2001; Caine, 1989; Sueker, 1995). To estimate the subsurface contributions to streamflow within Tuolumne Meadows, a two- component mixing equation using chloride as the chemical tracer was applied. Again, chloride was chosen because it behaves conservatively (unlike Radon which volatilizes), because of the relatively large difference between surface water/groundwater concentrations, and because it is less biologically active than sulfate which adds potential sources of error (Burns et al., 2001; Clow et al., 1996).
The calculation uses the measured tracer concentration at any sample point along the river (ClSample), the surface water concentration at the beginning of the meadow TM-
01 (Cl0), and the concentration found in a groundwater sample (ClSub). The percentage of groundwater contribution at that sample location may then be estimated using a simple Cl balance equation:
[3]
Cl Cl Sample Sub fraction Cl Cl gw 0 Sub
The expression quantifies the relative proportions of runoff derived from an alternate source or identified end-member, in this case, surface water entering the valley, vs meadow groundwater. Accuracy of this method may be compromised if waters from different sources are not well mixed in the stream channel, if ClSub and Cl0 are the same or cannot be precisely measured, if ClSample is not conservative or if ClSub and Cl0 have temporal variations not captured by the data (Caine, 1989).
74
Using concentrations from October 2014, proportions of groundwater inflow were calculated and graphed [FIGURE 40]. The data profile is the inverse of the Cl concentration profile of that sampling event [FIGURE 37]. The calculations show that
53% of the total flow at TM-14 is from meadow groundwater contributions, with 47% comprising the original stream water. This value was determined using the averaged groundwater values (Wells 23, 48d, 70) [TABLE 5] as the end-member (ClSub). The average concentration of the wells was selected to represent the groundwater component since the three groundwater samples are spaced across the meadow, and are close in proximity to the stream with similar overall characteristics. If, instead of using an average Cl- concentration for the three meadow wells, one uses the result just for well
48d, the result is 55% stream water. These outcomes are in good agreement, considering the uncertainty in end member definition. Substituting chloride with sulfate concentrations produces a similarly estimated groundwater contribution of 58% (42% stream water). A possible third component, from deep fracture flow, is discussed further below.
75
Percent Groundwater Contribution -60%
-50%
-40%
-30%
-20%
-10%
0%
TM-03 TM-14 TM-13 TM-12 TM-11 TM-10 TM-09 TM-08 TM-07 TM-06 TM-05 TM-04 TM-02 TM-01
Meadow Groundwater Fractured bedrock flow
Figure 40: Groundwater contributions along the Tuolumne River, as measured using chloride concentrations from October 2014.
The equation represents the net difference in stream water contribution between
TM-01 and TM-14 based on chloride concentrations. Though chloride shows groundwater influx of over 50% during baseflow (Oct and Nov), discharge measurements registered an increase of only 8% (Oct) over the same reach [TABLE 7], thereby confirming the extensive exchange between surface water and groundwater taking place within the meadow. Multiple peaks in Radon corroborate the notion that groundwater discharges to the stream at multiple locations along the meadow, and that there are intervening reaches that are neutral or losing.
76
Table 7: Stream flow measurements Stream flow measurements (cfs) Sample Location Oct. 2014 Jun. 2015 TM-01 2.14 71.02 TM-02 2.55 TM-04 2.47 TM-07 1.76 69.14 TM-09 1.70 73.76 TM-11 1.86 74.40 TM-12 2.00 TM-14 2.32 79.3329 Net Percent Increase 8.34% 11.70%
To assess the age (or subsurface residence time) of this fast exchanging water, tritium and 35S results from the different water components were examined. Groundwater is often 35S “dead,” with the highest readings found in snow, due to isolation from and recent contact with the atmosphere, respectively. Indeed, snow sampled in November registered the highest 35S activity of this study (19.6 mBq/L). The presence of 35S in groundwater samples [TABLE 8] further demonstrates the rapid and pervasive communication between surface water and meadow groundwater which governs this system. October’s Tuolumne River samples ranged from 1.5 – 2.2 mBq/L, while the only groundwater sampled on that trip (well 48d) showed 1.7 mBq/L. In June, surface and groundwater 35S readings are more distinct, at 5.9mBq/L (TM-01) and 1.12mBq/L (Well
48d). The change is caused by snowmelt contributions to runoff that come to dominate hydrologic exchange in the system during peak flow; this results in elevated river concentrations relative to groundwater for some period of time, a pattern that is similar to that observed in stable isotopes in stream water and groundwater. Further evaluation of the 35S and tritium values (all found to be above the detection limit) confirm both surface
77
water and groundwater are dominated by water from that or the previous year’s precipitation. Calculated 35S ages using the snow activity as the initial value, range from
275 d to 553 d [TABLE 9].
Table 8: 35S concentrations Sulfur-35 Concentrations (mBq/L) Sample Location Aug. 2013 Error Nov. 2013 Error Oct. 2014 Error Jun. 2015 Error TM-01 1.50 0.22 5.92 0.72 TM-04 1.54 0.22 TM-07 1.82 0.53 2.19 0.24 TM-08 1.06 0.60 TM-09 1.64 0.24 TM-11 5.58 0.65 TM-12 1.75 0.27 TM-13 1.54 TM-14 1.91 0.29 Dana Fork 1.47 0.26 Budd Creek 4.93 0.69 Snow 19.56 0.58 Well #1 1.51 0.16 Well 48d 1.67 0.42 1.12 0.54 Well 70 1.24 0.68
Table 9: Age calculation comparison Age calculations (days) Tritium Sulfur-35 Sample LocationAug. 2013 Nov. 2013 Oct. 2014 Jun. 2015 Aug. 2013 Nov. 2013 Oct. 2014 Jun. 2015 TM-01 272.7 273.4 216.8 322.5 561.0 TM-03 586.7 TM-04 610.6 318.7 561.7 TM-07 861.5 297.8 275.0 452.3 552.3 TM-08 -215.0 365.4 437.1 TM-09 -53.5 311.0 553.5 TM-11 1318.2 166.1 TM-12 302.9 536.9 TM-13 318.7 TM-14 1389.4 1076.5 292.0 527.8 Dana Fork 167.9 324.8 542.5 Soda Spring 27135.0 27485.8 Snow Well #1 28846.3 Well 23 92.6 1232.7 Well 46d 1537.1 Well 48d 864.7 564.8 308.5 483.2 Well 70 282.8 1803.7
78
The calculated ages using tritium and 35S are in relative agreement and match age assessments in other mountain catchments (Cowie, Williams, Caine & Michel, 2010).
Soda Spring being “tritium-dead” (0.2 pCi/L) indicates a long flowpath between the aged, deep circulating water and the discharge point [TABLE 10]. It should be mentioned that while Soda Spring registered the greatest tritium age within the meadow (>75 years) it was not tested for 35S because of sampling complications due to low flow production.
Further, it should be noted that overall residence times resulting from tritium values represent a less precise age of the component of the water containing tritium, and groundwater mixing between a somewhat older component with a younger (recent snowmelt) component. As such, the tritium values across the meadow had considerably more variation than dates calculated using 35S.
Table 10: Tritium concentrations Tritium Concentrations (pCi/L) Sample Location Aug. 2013 Error Nov. 2013 Error Oct. 2014 Error Jun. 2015 Error TM-01 14.09 0.78 14.09 0.60 14.21 0.78 TM-03 13.43 0.70 TM-04 13.38 1.08 TM-07 12.88 0.77 TM-08 15.18 0.75 TM-09 14.81 0.95 TM-11 12.01 0.77 14.32 0.84 TM-14 11.88 0.60 12.46 0.99 Dana Fork 14.32 0.93 Soda Spring 0.23 0.58 0.22 0.55 Snow 14.69 0.66 Well #1 0.18 0.59 Well 23 14.48 0.75 12.17 0.59 Well 46d 11.61 0.62 Well 48d 12.87 1.27 13.47 0.93 Well 70 14.07 0.81 11.15 0.62
As expected, tritium activity was highest in snow (14.7 pCi/L - Nov), and surface waters (TM-08 15.2 pCi/L)(TM-09 14.8 pCi/L) with greater ages in the wells and toward the downstream end of the meadow Background tritium concentrations in California range from 4.0 pCi/L near the coast to 17.8 pCi/L at the Sierra Nevada with an increasing
79
trend inland (Harms, Visser, Moran & Esser, 2016). Variability in tritium activity in precipitation within this range is expected, considering the variability in storm tracks and in interaction with the upper atmosphere. The entire range in observed tritium activity
(excluding the two deep groundwater sources) is within the range that could reflect variability in the atmospheric signal. Calculated 3H ages, using the snow activity as the initial value, range from 0 to 4.3 years (excluding deep wells).
Contribution from Deep Fracture Flow
The deepest well sampled is also the only production well in the area. Located on the eastern side of Lembert Dome, near the Tuolumne Meadows Wilderness Center, this well (Well #1) serves the rangers’ needs during winter months. Drilled to 252 ft (76.8 m), and screened within the fractured granite bedrock (Reed, 1982), water from this well was collected and analyzed for noble gas and isotope concentrations using two methods,
NGMIMS and NGMS. Previously, noble gas concentrations have been useful for examining recharge conditions, and are typically used to evaluate temperature and excess air (Cey et al., 2009; Heilweil et al., 2009; Manning & Solomon, 2003; Singleton &
Moran, 2009). Excess helium (concentrations greater than the equilibrium component;
Equation 1) can be parsed to differentiate between various helium components.
Specifically, the terrigenic helium portion of ‘Excess 4He’ is of particular interest since it provides the most sensitive indicator of deep fracture flow (Visser et al, 2014; Moran, de
Jong, Visser, Singleton & Esser, 2015; Segal, Moran, Visser, Singleton & Esser, 2014).
Of the two analytical methods, NGMS is more precise and reliable than NGMIMS.
80
Based on previous work comparing results from both methods, a correction factor of 4.5 was applied to the NGMIMS results for stream water samples (Visser, Singleton,
Hillegonds, Velsko, Moran & Esser, 2013). Helium, being very light, quickly escapes during any atmospheric exposure which occurs with the VOA sampling method associated with NGMIMS. The sample method using cold-welded copper tubes for
NGMS is designed to prevent loss of any dissolved gas (Aeschbach-Hertig et al., 1998).
Calculated terrigenic helium (Equation 1) was applied in two component, linear mixing analysis to calculate the portion of groundwater originating from the deep, fracture-flow system. Using Well #1 as an end-member, NGMIMS data from stream samples registered the fractured flow component increasing from 0% to 1.6% along the stream.
This influx is relatively insignificant compared to the hydrologic interactions within the meadow and is unlikely to provide a buffer or offer any relief during extended drought.
As noted above, this component would not likely be detectable in an average year because the runoff and meadow groundwater components would overwhelm the fracture flow component.
81
CONCLUSION
Millions of Californians enjoy power and high-quality water sourced from alpine watersheds. Additionally, the watersheds sustain numerous ecosystems with dependent flora and fauna. The principle recharge process of infiltrating precipitation as well as the hydraulic link between river and groundwater has been studied and well documented.
Direct, measurable observations of physical parameters such as stream flow and groundwater elevation allow this type of investigation to be relatively easy, cheap and quick. Less understood is the extent of surface water-groundwater exchange and the temporal variability affecting these interactions, specifically in alpine regions during baseflow of late summer-early fall. Exploring subsurface communication during low flow periods requires supplementary research utilizing laboratory analyses of geochemical signatures and isotopic tracers. As such the objective of this study was to further the understanding of hydrologic processes in alpine meadows, focusing on
Tuolumne Meadows and its role pertaining to groundwater storage potential and ability to support ecosystems during extended drought.
Through Radon analysis, it was revealed that within the meadow, groundwater is being discharged into the Tuolumne River throughout the periods when sampling took place. While there is influx across the meadow, especially high readings pinpointed three
‘hotspot’ locations indicating increased groundwater contributions at those points.
Surface features at these point locations highlight the important effect of geomorphology on subsurface flow. Roughly half of the river’s total flow was calculated as being supplied by groundwater using a chloride mass balance equation. A disproportionately
82
small increase in flow across the meadow provides evidence of extensive hydrologic exchange occurring in the meadow sediments. Low anion levels substantiate groundwater contributions at identified tributary locations throughout the season even after visible surface flow ceases. Anion and stable isotope readings differentiate source signatures and distinguish groundwater evolution related to seasonal changes. Tritium and 35S results both indicate that relatively young water dominates meadow groundwater and surface flow. Aged water is recognized in some of the wells, and much older water is found at Soda Spring; however, the older, higher TDS component does not contribute significantly to the river. A small percentage of flow from the fractured bedrock of the meadow was revealed using 4He as a tracer. While meadow sediments hold primary storage potential, the bedrock flow may provide some relief for extended droughts however, it will be minimal.
83
REFERENCES
Aeschbach‐Hertig, W., Schlosser, P., Stute, M., Simpson, H. J., Ludin, A., & Clark, J. F. (1998). A 3H/3He study of ground water flow in a fractured bedrock aquifer. Ground Water, 36(4), 661-670.
Ajami, H., Troch, P. A., Maddock, T., Meixner, T., & Eastoe, C. (2011). Quantifying mountain block recharge by means of catchment‐scale storage‐discharge relationships. Water Resources Research, 47(4).
Alley, W. M., Healy, R. W., LaBaugh, J. W., & Reilly, T. E. (2002). Flow and storage in groundwater systems. science, 296(5575), 1985-1990.
Babalis, T., Stromberg, G., Schaible, D., & Torgerson, S. (2007). Tuolumne Meadows Historic District, Cultural Landscape Inventory, National Park Service.
Bateman, P. C. (1983). Geologic map of the Tuolumne Meadows quadrangle, Yosemite National Park, California (No. 1570).
Becker, R. A., Tikoff, B., Riley, P. R., & Iverson, N. R. (2014). Preexisting fractures and the formation of an iconic American landscape: Tuolumne Meadows, Yosemite National Park, USA. GSA Today, 24(11), 4-10.
Belitz, K., Dubrovsky, N. M., Burow, K., Jurgens, B., & Johnson, T. (2003). Framework for a ground-water quality monitoring and assessment program for California. US Geological Survey Water-Resources Investigations Report, 03-4166.
Beller, H, Eaton, G F, Ekwurzel, B E, Esser, B K, Hu, Q, Hudson, G B, … Moran, J. E. (2005). California GAMA Program: Sources and Transport of Nitrate in Groundwater in the Livermore Valley Basin, California. United States. doi:10.2172/888584
Beyerle, U. , Aeschbach-Hertig, W. , Imboden, D. , Baur, H. , Graf, T. , ... Kipfer, R. (2000). A mass spectrometric system for the analysis of noble gases and tritium from water samples. Environmental Science and Technology, 34(10).
Boulton, A. J. (2005). Chances and challenges in the conservation of groundwaters and their dependent ecosystems. Aquatic Conservation: marine and freshwater ecosystems, 15(4), 319-323.
Bryant, W. A., (1984), Hartley Springs fault zone, Mono County, California: California Division of Mines and Geology Fault Evaluation Report FER-157, microfiche copy in California Division of Mines and Geology Open-File Report 90-14, 7 p., scale 1:62,500.
84
Burns, D. A., McDonnell, J. J., Hooper, R. P., Peters, N. E., Freer, J. E., Kendall, C., & Beven, K. (2001). Quantifying contributions to storm runoff through end‐member mixing analysis and hydrologic measurements at the Panola Mountain Research Watershed (Georgia, USA). Hydrological Processes, 15(10), 1903-1924.
Čada, G. F., Deacon, M. D., Mitz, S. V., & Bevelhimer, M. S. (1997). Effects of water velocity on the survival of downstream‐migrating juvenile salmon and steelhead: A review with emphasis on the Columbia river basin. Reviews in Fisheries Science, 5(2), 131-183.
Caine, N. (1989). Hydrograph separation in a small alpine basin based on inorganic solute concentrations. Journal of Hydrology, 112(1-2), 89-101.
California Department of Water Resources. (1998). California Water Plan Update. Sacramento. Bulletin 160-98.
California Department of Water Resources. (2003). California’s Groundwater. Sacramento. Bulletin 118.
California Department of Water Resources. (2018). Tuolumne Meadows Incremental precipitation & River discharge. http://cdec.water.ca.gov/cgi- progs/stationInfo?station_id=TUM. Accessed 2/4/18.
Carreira, P. M., Marques, J. M., Marques, J. E., Chaminé, H. I., Fonseca, P. E., Santos, F. M., ... & Carvalho, J. M. (2011). Defining the dynamics of groundwater in Serra da Estrela Mountain area, central Portugal: an isotopic and hydrogeochemical approach. Hydrogeology journal, 19(1), 117-131.
Cey, B. D., Hudson, G. B., Moran, J. E., & Scanlon, B. R. (2009). Evaluation of noble gas recharge temperatures in a shallow unconfined aquifer. Ground Water, 47(5), 646-659.
Ciruzzi, D. M., & Lowry, C. S. (2017). Impact of complex aquifer geometry on groundwater storage in high‐elevation meadows of the Sierra Nevada Mountains, CA. Hydrological Processes, 31(10), 1863-1875.
Clark, M. M., Harms, K. K., Lienkaemper, J. J., Harwood, D. S., Lajoie, K. R., Matti, J. C., ... & Sims, J. D. (1984). Preliminary slip-rate table and map of late- Quaternary faults of California (No. 84-106).
Clow, D. W., Mast, M., & Campbell, D. H. (1996). Controls on surface water chemistry in the upper Merced River basin, Yosemite National Park, California. Hydrological Processes, 10(5), 727-746.
85
Cooper, D. J., Lundquist, J. D., King, J., Flint, A., Flint, L., Wolf, E., ... & Roche, J. (2006). Effects of the Tioga Road on hydrologic processes and lodgepole pine invasion into Tuolumne Meadows, Yosemite National Park. Yosemite National Park Report.
Cowie, R., Williams, M., Caine, N., & Michel, R. (2010). Estimated residence time of two snowmelt dominated catchments, Boulder Creek Watershed, Colorado. In Logan, Utah: Proceedings of the Western Snow Conference (p. 310).
Ekwurzel, B., Schlosser, P., Smethie, W. M., Plummer, L. N., Busenberg, E., Michel, R. L., ... & Stute, M. (1994). Dating of shallow groundwater: Comparison of the transient tracers 3H/3He, chlorofluorocarbons, and 85Kr. Water Resources Research, 30(6), 1693-1708.
Ellins, K. K., Roman-Mas, A., & Lee, R. (1990). Using 222Rn to examine groundwater/surface discharge interaction in the Rio Grande de Manati, Puerto Rico. Journal of Hydrology, 115(1-4), 319-341.
Esser, B. K., Beller, H. R., Carle, S. F., Hudson, G. B., Kane, S. R., Leif, R. N., ... & Moran, J. E. (2009). California GAMA Program: Impact of dairy operations on groundwater quality. Lawrence Livermore National Laboratory. UCRLTR- 223509, California State Water Resources Control Board.
Flint, A. L., Flint, L. E., & Dettinger, M. D. (2008). Modeling soil moisture processes and recharge under a melting snowpack. Vadose Zone Journal, 7(1), 350-357.
Griffin, D., & Anchukaitis, K. J. (2014). How unusual is the 2012–2014 California drought?. Geophysical Research Letters, 41(24), 9017-9023.
Guida, M., Guida, D., Guadagnuolo, D., Cuomo, A., & Siervo, V. (2013). Using Radon- 222 as a Naturally Occurring Tracer to investigate the streamflow-groundwater interactions in a typical Mediterranean fluvial-karst landscape: the interdisciplinary case study of the Bussento river (Campania region, Southern Italy). WSEAS Transaction on Systems, 12, 85-104.
Harms, P. A., Visser, A., Moran, J. E., & Esser, B. K. (2016). Distribution of tritium in precipitation and surface water in California. Journal of Hydrology, 534, 63-72.
Heilweil, V. M., Solomon, D. K., Gingerich, S. B., & Verstraeten, I. M. (2009). Oxygen, hydrogen, and helium isotopes for investigating groundwater systems of the Cape Verde Islands, West Africa. Hydrogeology journal, 17(5), 1157-1174.
Huber, N., (1989) The Geologic Story of Yosemite National Park. Washington, DC: U.S. Government Printing Office.
86
Hunt, R. J., Coplen, T. B., Haas, N. L., Saad, D. A., & Borchardt, M. A. (2005). Investigating surface water–well interaction using stable isotope ratios of water. Journal of Hydrology, 302(1), 154-172.
Jager, H. I., (2000). Predicting the Viability of Fish Populations in a Modified Riverine Environment (Doctoral dissertation, University of Tennessee).
Klump, S., Tomonaga, Y., Kienzler, P., Kinzelbach, W., Baumann, T., Imboden, D. M., & Kipfer, R. (2007). Field experiments yield new insights into gas exchange and excess air formation in natural porous media. Geochimica et Cosmochimica Acta, 71(6), 1385-1397.
Kope, R. G., & Botsford, L. W. (1990). Determination of factors affecting recruitment of chinook salmon Oncorhynchus tshawytscha in central California. Fishery Bulletin, 88(2), 257-269.
Lamontagne, S., & Cook, P. G. (2007). Estimation of hyporheic water residence time in situ using 222Rn disequilibrium. Limnology and Oceanography: Methods, 5(11), 407-416.
Lerner, D. N., Issar, A. S., & Simmers, I. (1990). Groundwater recharge: a guide to understanding and estimating natural recharge (Vol. 8, pp. 1-345). Hannover: Heise.
Loheide, S. P., Deitchman, R. S., Cooper, D. J., Wolf, E. C., Hammersmark, C. T., & Lundquist, J. D. (2009). A framework for understanding the hydroecology of impacted wet meadows in the Sierra Nevada and Cascade Ranges, California, USA. Hydrogeology Journal, 17(1), 229-246.
Loheide, S. P., & Gorelick, S. M. (2007). Riparian hydroecology: a coupled model of the observed interactions between groundwater flow and meadow vegetation patterning. Water Resources Research, 43(7).
Loheide, S. P., & Lundquist, J. D. (2009). Snowmelt‐induced diel fluxes through the hyporheic zone. Water Resources Research, 45(7).
Lowry, C. S., Deems, J. S., Loheide, I. I., Steven, P., & Lundquist, J. D. (2010). Linking snowmelt‐derived fluxes and groundwater flow in a high elevation meadow system, Sierra Nevada Mountains, California. Hydrological processes, 24(20), 2821-2833.
Lowry, C. S., & Loheide, S. P. (2010). Groundwater‐dependent vegetation: Quantifying the groundwater subsidy. Water Resources Research, 46(6).
87
Lundquist, J. D., Dettinger, M. D., & Cayan, D. R. (2005). Snow‐fed streamflow timing at different basin scales: Case study of the Tuolumne River above Hetch Hetchy, Yosemite, California. Water Resources Research, 41(7).
Lytle, D. A., & Poff, N. L. (2004). Adaptation to natural flow regimes. Trends in ecology & evolution, 19(2), 94-100.
Manning, A. H., & Caine, J. S. (2007). Groundwater noble gas, age, and temperature signatures in an Alpine watershed: Valuable tools in conceptual model development. Water Resources Research, 43(4).
Manning, A. H., & Solomon, D. K. (2003). Using noble gases to investigate mountain- front recharge. Journal of Hydrology, 275(3), 194-207.
Matthes, F. E. (1930). Geologic history of the Yosemite Valley (No. 160). US Geological Survey.
Moran, J. E., de Jong, M., Visser, A., Singleton, M. J., & Esser, B. K. (2015). GAMA: Ambient groundwater monitoring & assessment program special study.
Mote, P. W., Hamlet, A. F., Clark, M. P., & Lettenmaier, D. P. (2005). Declining mountain snowpack in western North America. Bulletin of the American meteorological Society, 86(1), 39-49.
Mullinger, N. J., Binley, A. M., Pates, J. M., & Crook, N. P. (2007). Radon in Chalk streams: Spatial and temporal variation of groundwater sources in the Pang and Lambourn catchments, UK. Journal of Hydrology, 339(3), 172-182.
Naiman, R. J., Bunn, S. E., Nilsson, C., Petts, G. E., Pinay, G., & Thompson, L. C. (2002). Legitimizing fluvial ecosystems as users of water: an overview. Environmental management, 30(4), 455-467.
National Park Service. (2014). U.S. Department of the Interior. Tuolumne Wild and Scenic River: Final Comprehensive Management Plan and Environmental Impact Statement.
National Park Service. (2017). Average Temperatures & Precipitation. https://www.nps.gov/yose/planyourvisit/weather.htm. Accessed 2/4/17.
National Research Council. (1996). Rock fractures and fluid flow: Contemporary understanding and applications. National Academies Press.
88
Nilsson, C., & Svedmark, M. (2002). Basic principles and ecological consequences of changing water regimes: riparian plant communities. Environmental Management, 30(4), 468-480.
Plummer, L. N., Busenberg, E., Böhlke, J. K., Nelms, D. L., Michel, R. L., & Schlosser, P. (2001). Groundwater residence times in shenandoah national park, blue ridge mountains, virginia, usa: a multi-tracer approach. Chemical Geology, 179(1), 93- 111.
Prichard, H. M., & Gesell, T. F. (1977). Rapid measurements of 222Rn concentrations in water with a commercial liquid scintillation counter. Health Physics, 33(6), 577- 581.
Prichard, H. M., Venso, E. A., & Dodson, C. L. (1992). Liquid-scintillation analysis of {sup 222} Rn in water by alpha-beta discrimination. Radioactivity and Radiochemistry, 3(1).
Ratliff, R. D. (1985). Meadows in the Sierra Nevada of California: state of knowledge.
Reed, D. (1982). State of California. The Resources Agency Department of Water Resources. Water Well Drillers Report. (No. 226237).
Rogers, A. S. (1958). Physical behavior and geologic control of radon in mountain streams. US Government Printing Office.
San Francisco Public Utilities Commission. (2007). Upper Tuolumne River: Description of River Ecosystem and Recommended Monitoring Actions.
San Francisco Public Utilities Commission. (2008). Program Environmental Impact Report. Water System Improvement Program, (Vol. 3)
Scanlon, B. R., Healy, R. W., & Cook, P. G. (2002). Choosing appropriate techniques for quantifying groundwater recharge. Hydrogeology journal, 10(1), 18-39.
Schlosser, P., Stute, M., Dörr, H., Sonntag, C., & Münnich, K. O. (1988). Tritium/3 He dating of shallow groundwater. Earth and Planetary Science Letters, 89(3), 353- 362.
Segal, D. C., Moran, J. E., Visser, A., Singleton, M. J., & Esser, B. K. (2014). Seasonal variation of high elevation groundwater recharge as indicator of climate response. Journal of hydrology, 519, 3129-3141.
89
Singleton, M., & Moran, J. (2009). Using dissolved noble gas and isotopic tracers to evaluate the vulnerability of groundwater resources in a small, high elevation catchment to predicted climate change. 9. Water Resources Research, vol. 46, n/a, December 14, 2010, W00F06, 46(LLNL-JRNL-418084).
Singleton, M. J., & Moran, J. E. (2010). Dissolved noble gas and isotopic tracers reveal vulnerability of groundwater in a small, high‐elevation catchment to predicted climate changes. Water Resources Research, 46(10).
State Water Resources Control Board. (2016). Substitute Environmental Document in Support of Potential Changes to the Water Quality Control Plan for the San Francisco Bay-Sacramento/San Joaquin Delta Estuary: San Joaquin River Flows and Southern Delta Water Quality. Recirculated Draft (SCH#2012122071). September. (ICF 00427.11.) Sacramento, CA. Prepared with assistance from ICF International, Sacramento, CA.
Stewart, I. T., Cayan, D. R., & Dettinger, M. D. (2005). Changes toward earlier streamflow timing across western North America. Journal of climate, 18(8), 1136-1155.
Sueker, J. K. (1995). Chemical hydrograph separation during snowmelt for three headwater basins in Rocky Mountain National Park, Colorado. IAHS Publications-Series of Proceedings and Reports-Intern Assoc Hydrological Sciences, 228, 271-282.
United States Department of Agriculture, Natural Resources Conservation Service. (2007). Soil survey of Yosemite National Park, California. Accessible online at: http://soils.usda.gov/surve/printed_survey/.
United States Geological Survey. (2018). Streamflow diagram. https://water.usgs.gov/edu/streamflow2.html. Accessed 2/4/18
35 Uriostegui, S., & Bibby, R. (2013). Recovery of S-SO4 in MAR Groundwater and Surface Water Samples for Radiometric Analysis. Unpublished.
Urióstegui, S. H., Bibby, R. K., Esser, B. K., & Clark, J. F. (2015). Analytical Method for Measuring Cosmogenic 35S in Natural Waters. Analytical chemistry, 87(12), 6064-6070.
Verner, J. & Boss, A. (1980). California wildlife and their habitats: western Sierra Nevada. U.S. Dept of Agriculture, Forest Service, Pacific Southwest Forest and Range Experiment Station, Berkeley, Calif
90
Visser, A., Singleton, M. J., Hillegonds, D. J., Velsko, C. A., Moran, J. E., & Esser, B. K. (2013). A membrane inlet mass spectrometry system for noble gases at natural abundances in gas and water samples. Rapid Communications in Mass Spectrometry, 27(21), 2472-2482.
Viers, J. H., Purdy, S. E., Peek, R. A., Fryjoff-Hung, A., Santos, N. R., Katz, J. V. E., ... & Yarnell, S. M. (2013). Montane meadows in the Sierra Nevada: Changing hydroclimatic conditions and concepts for vulnerability assessment. Center for Watershed Sciences Technical Report. Davis, California: Center for Watershed Sciences.
Visser, A., Moran, J. E., Singleton, M. J., & Esser, B. K. (2014). California GAMA Special Study: Geostatistical analysis of groundwater age and other noble gas derived parameters in California groundwater. Lawrence Livermore National Laboratory LLNL-TR-654049.
Voeckler, H. M. (2012). Modeling deep groundwater flow through fractured bedrock in a mountainous headwater catchment using a coupled surface water-groundwater model, Okanagan Basin, British Columbia (Doctoral dissertation, University of British Columbia).
Williams, J. G., & Matthews, G. M. (1995). A review of flow and survival relationships for spring and summer chinook salmon Oncorhynchus tshawytscha from the Snake River Basin. Fishery Bulletin, 93, 732-740.
Wilson, J. L., & Guan, H. (2004). Mountain‐Block Hydrology and Mountain‐Front Recharge. Groundwater recharge in a desert environment: The Southwestern United States, 113-137.