The Geochemical Dynamics of Paulina Lake, Newberry Volcano, Oregon

Wesleyan University The Honors College

Paula Meredith Tartell Class of 2018

A thesis submitted to the faculty of Wesleyan University in partial fulfillment of the requirements for the Degree of Bachelor of Arts with Departmental Honors in Earth & Environmental Sciences

Middletown, Connecticut April, 2018

Table of Contents

Acknowledgements ...... 4 Abstract ...... 5 1. Introduction ...... 6 1.1. Study Objectives...... 6 1.2. Geologic Setting ...... 8 1.2.1. Newberry Volcano and Caldera...... 8 1.2.2. Newberry Hydrothermal System ...... 11 1.2.3. Paulina Lake Descriptive Limnology and Hydrology...... 12 1.2.4. Geochemistry of Paulina Lake: Past Observations ...... 15 2. Methods ...... 18 2.1. Field Methods ...... 18 2.1.1. Water Sampling ...... 18 2.1.2. Sediment Sampling ...... 19 2.1.3. Measuring Lake Surface CO2 Evasion and Gas Sampling ...... 19 2.2. Laboratory Analyses ...... 21 2.2.1. Ion Concentrations in Water ...... 21 2.2.2. Multi-elemental Analyses of Sediment ...... 22 2.2.3. Stable Isotopes in Water ...... 22 2.2.4. Stable Isotopes of CO2 Gas ...... 23 2.3. Calculations ...... 24 2.3.1. Web-PHREEQ ...... 24 2.3.2. CO2 Flux Calculations and Sequential Gaussian Simulation (SGS) ...... 24 3. Results ...... 26 3.1. Field Measurements ...... 26 3.1.1. Temperature ...... 26 3.1.2. Dissolved Oxygen (DO) ...... 27 3.1.3. pH ...... 28 3.1.4. Conductivity ...... 29 3.1.5. Paulina Creek Flow Rate ...... 30 3.2. Water Chemistry ...... 33 3.2.1. Alkalinity ...... 33 3.2.2. Major Anions ...... 34 3.2.3. Major Anions ...... 35 3.2.4. Stable Isotopes of Water ...... 37 3.3. Carbon Studies ...... 39 3.3.1. CO2 Flux Field Data ...... 39 3.3.2. Stable Isotopes of Chamber CO2 Gas and Ambient Air ...... 42 3.3.3. δ13C of Dissolved Inorganic Carbon (DIC) ...... 44 3.3.4. δ13C of Aqueous CO2 ...... 452

3.4. Sediment Chemistry ...... 46 3.4.1. Bulk Dry Density ...... 46 3.4.2. Major Elements ...... 46 3.4.3. Trace Elements ...... 52 3.4.4. Indicators for the presence of volcanic ash ...... 55 3.4.5. Vivianite ...... 56 4. Discussion ...... 59 4.1. Water Chemistry ...... 59 4.1.1. Physical Limnology ...... 59 4.1.2. Chemical Trends ...... 60 4.1.3. H2O Stable Isotopes and Water Balance ...... 63 4.2. Sediment Chemistry ...... 67 4.2.1. Raw sediment trends ...... 67 4.2.2. Identity of the Ash Layer and Core Age Model ...... 69 4.3. Ash-Free Sediment Mass Accumulation Rates ...... 84 4.4. Sediment Mineralogy ...... 86 4.5. Comparison to Precambrian Banded Iron Formations (BIFs) ...... 90 4.6. The Carbon Cycle in Paulina Lake ...... 95 4.6.1. CO2 Evasion Flux ...... 96 4.6.2. δ 13C of DIC and Aqueous CO2 ...... 102 4.6.3. δ13C-CO2 Gas ...... 104 4.6.4. Two-Box Carbon Model ...... 107 4.7. Hydrothermal Fluid Geothermometry and Element Concentrations ...... 114 5. Conclusions ...... 120 References ...... 126 Additional Data Tables ...... 131

Acknowledgements

While I am the only one who receives official credit for this thesis, there are so many people who must be recognized for making the final product possible.

First and foremost, I would like to acknowledge the unparalleled mentorship and support provided by my advisor, Johan Varekamp. Joop, I feel so fortunate to have stumbled into the lab of a person who approaches teaching with the care and devotion that you do. From the slippery floors of the geochemistry lab to the top of Newberry Volcano, I cherish each memory and teaching moment, whether silly, demanding, or inspiring! You have made my undergraduate experience feel like a gift.

E&ES is the best department at Wesleyan because of the intellectual community fostered by its faculty, students, and staff; thank you all for providing me with the gusto and extensive toolbox necessary to complete this work. I would especially like to thank Joel Labella, Tim Ku, Jeff Gilarde, Ellen Thomas, Ginny Harris, Valerie Nazzaro (Quantitative Analysis Center), and Dana Royer, all of whom have selflessly devoted time and attention to this particular endeavor. Thanks to my amazing cousin, Hannah Plon ’14, who encouraged me to take Peter Patton’s Geomorphology class during the fall of my sophomore year. I shudder to imagine in what dark hall of Hall-Atwater I would be had I not discovered the joys of fluvial geomorphology.

Of course, a wholehearted thank you to Team Newberry! Hilary Brumberg ‘17, thank you for your unwavering friendship and faith in me, and for being my intellectual role model. Celeste Smith’19 and Molly Wagner MA ‘19, a huge thank you for sticking it out with me in the field this year while we coughed up our lungs. I especially appreciate the time and emotional support you both devoted over this school year to helping me with my project, all while you had your own burdens to carry. East Lake Resort staff (especially loyal Connor!), thank you for the warmth, local knowledge, labor, food, drink, and memories. You all ask for nothing, and yet we could not have completed the required data collection or stayed positive without your help.

I could probably write another thesis about the ways in which my family and friends have gotten me through to this point. I am endlessly grateful for my support systems. To my family, I cannot begin to articulate my appreciation in one sentence, but I think you know. The kindness my friends have shown me have made me kinder to myself. Thanks to my brilliant and supportive housemates – Eva, Julia, and Mira – all of whom sustained me at home as they struggled through their own thesis writing processes. Kamla and Arden also gave me that final burst of energy in the last 24 hours…those are the moments of friendship you never forget. Thanks to my other best friends who kept me going and have been thoughtful beyond belief; you know who you are! My final shout out goes to my wonderful headphones; this thesis truly would never have materialized without your range, fidelity, and noise cancelling properties…

This work was supported by the Wesleyan Research in the Sciences Fellowship, the James T. Gutmann Field Studies Scholarship, and NASA Connecticut Space Grant. 4

Abstract

Paulina Lake, one of two small crater lakes in the Newberry Volcano caldera,

Oregon, is fed by subaqueous hydrothermal springs that account for its carbonate-rich water composition. Its sediments consist predominantly of Fe and Si (up to 15 and

29%, respectively), with strong enrichments in P and As. All sediment analyses indicated the presence of some silicic volcanic ash, and a thick ash layer from the 720

A.D. Newberry eruption was identified in the main sediment core. An age model was calibrated around the ash layer, which suggested that the core represents 2800 years of lake history. Some of Paulina Lake’s hydrothermally-derived chemical constituents

(e.g. Fe, As, Mn, P) precipitate immediately from the oxygenated, neutral waters

(pH=~8) and settle into the sediments. Others (e.g. Ca, Na, K, Si) initially remain in the water as dissolved solids, although some hydrothermal silica may precipitate near the subaqueous hot springs. The dissolved species either leave the system through the

Paulina Creek outflux or are sequestered by photosynthetic biota, and then accumulate into the sediment (~4% Corg). The lake releases CO2 from its surface, and the summation of the Paulina Creek outflow, organic sequestration, and CO2 release demand a weekly hydrothermal input of 40 tonnes of carbon. Taking all of the chemical sinks into account through the analysis of the sediment core and water chemistry data, a steady state mass balance equation was invoked to constrain the composition and fluxes of the subaqueous hot springs. The core analyses provided strong evidence for enhanced hydrothermal inputs between 0 and 500 B.C, and silica geothermometry indicated hydrothermal fluid temperatures of ~140 ºC. The Paulina

Lake sediment shows strong similarities with Precambrian Iron Formations. 5

1. Introduction

1.1. Study Objectives

Volcanic lakes are regarded as surface expressions of volcano-hosted hydrothermal systems, because their waters react to chemical changes in the subsurface hydrothermal and magmatic system (Varekamp et al., 2000). Study of their waters can provide insight into the activity of the volcanic system at the time of sampling, while study of their sediment chemistry can indicate changes in hydrothermal activity over longer temporal scales. Thus, volcanic lakes have within their confines many valuable tools for monitoring the host volcano and can even serve as predicters of future volcanic unrest (Badrudin, 1994; Rouwet and Tassi, 2011; Varekamp, 2015).

Beyond tracking potential hazardous changes of underlying volcanic activity, furthering understanding of volcanic lakes can also help to identify and mitigate hazards within the very limnic systems being studied. Volcanic and geothermal effluents, by which volcanic lakes may be infiltrated, can contain toxic trace elements such as B, Li, As, or Hg. These toxics can manifest in local ecosystems and agricultural land, or contaminate drinking water downstream (Varekamp, 2015).

Some volcanic lakes, furthermore, contain poorly soluble gases, such as methane or

CO2. The buildup of CO2 gas in meromictic, irregularly mixed volcanic lakes can lead to dangerous explosions, as observed in Lake Nyos, Cameroon (Rouwet and

Tassi, 2011).

Volcanic lakes can also serve as analogs for ancient earth and extraterrestrial environments. Copahue Volcano in Argentina, for example, is an active acid hydrothermal system that precipitates Mg- and Fe-sulfates, minerals that are 6

uncommon on Earth but may have been present in early aqueous Mars environments

(Rodríguez et al., 2015). Study of this site illuminated the photochemical processes that formed these minerals. Rodríguez et al. (2015) found that photo-reduction played a role in mineral formation in the terrestrial environments, and they suggested that these mechanisms might be applied to understanding photochemical processes on

Mars. This study is just one example of the various ways in which insights gained from these exotic environments might be applied elsewhere, through time and space.

Paulina Lake (PL), Oregon, the volcanic lake that is the focus of this study, could also be an analog for ancient terrestrial environments. Lake bottom sediments in Paulina Lake might be a modern analog of ancient Iron Formations (BIFs), which were formed during the early history of the earth (~3.8-1.8 Ga). Most Archean BIFs were part of greenstone belts that have been deformed, metamorphosed, and dismembered (Klein, 2005); the idea that PL could be a modern analog to these formations provides a unique opportunity for comparative study. Accordingly, this study seeks to determine the validity of this postulation.

Furthermore, this study seeks to better develop this unique volcanic lake toolbox by illuminating the mechanisms that regulate the hydrogeochemical dynamics of PL, one of two crater lakes in the Newberry Volcano caldera. The main objective, specifically, is to learn more about the concentration and magnitude of subaqueous hydrothermal inputs to the lake, both at the time of sampling and over history. Once the hot springs’ chemical constituents are ultimately constrained, established hydrothermal geothermometry techniques can be employed to determine the temperature of the fluids (Verma, 2002). 7

Since PL’s subaqueous hydrothermal fluids could not be discretely sampled and analyzed, such a task requires the incorporation of a first-order lake mass balance model. Based on observations confirming water steady state phenomena and a lack of riverine inputs into the system, it is assumed that all outfluxes from PL must be replenished by the subaqueous hot springs. Sediment, water, and gas data can be integrated in order to calculate PL’s total chemical outflux, which is comprised of all sinks into the sediment and out of the system via Paulina Creek (PCR) fluid discharge and CO2 gas evasion; thus, at mass balance with the total outflux, hydrothermal influxes and theoretical fluid compositions can be determined over time.

This project is part of an ongoing, seven year-long study of Newberry

Volcano’s crater lakes. Varekamp and students have collected data from East Lake and PL in the summers of 2009-2012 and 2014-2016 in an effort to understand the geochemistry and limnogeology of the Newberry caldera lakes. This is the first study that focuses specifically on the geochemistry of PL. For undergraduate thesis projects, Lefkowitz (2011) conducted a comprehensive, comparative survey of the two lakes, and Capece (2016) and Brumberg (2017) focused their studies on the carbon dynamics of East Lake.

1.2. Geologic Setting

1.2.1. Newberry Volcano and Caldera

Newberry Volcano (43˚41.21’N, 121˚15.18’W) is one of the largest Quaternary volcanoes in the United States. Its expanse covers an area of more than 1,500 km2 and its volume is 450 km3 (Macleod and Sherrod, 1988). Located 65 kilometers east of 8

the Cascade Range and 30 km south of Bend (Figure 1, Lefkowitz et al., 2016), it lays at the convergence point between Sisters, Brothers, and Walker Rim fault zones from the north, northeast, and southwest, respectively.

Figure 1: Location of Newberry Volcano and its twin crater lakes, East Lake and Paulina Lake (Lefkowitz et al., 2016).

The volcano has relatively gently sloped flanks which meet at an 8-kilometer- wide caldera (Figure 2). Filled with Pleistocene and Holocene pyroclastics, flows, domes, and lacustrine sediments, it was formed at around 75 ka BP (Higgins, 1973).

The volcano’s upper flanks are composed of dacite, rhyodacite and rhyolite domes and flows; Holocene basaltic andesite flows occur on the flanks, and Holocene rhyolitic material occurs within the caldera (MacLeod et al., 1995). Within the caldera reside Paulina and East Lakes, two small volcanic lakes separated by a narrow ridge. This ~2km-wide ridge consists of the Central Pumice Cone and the Big

Obsidian flow, which are aged ~6.1 ka (MacLeod and Sherrod, 1988). Further details about Newberry Caldera geology are illustrated in Figure 2. 9

Figure 2: Geologic map of Newberry Caldera from MacLeod and Sammel (1982).

Newberry Volcano has erupted thousands of times in its 500-700k years of activity. Six volcanic stages have been classified for Newberry Volcano, each based on chemical composition and stratigraphic relations to the Mazama eruption ash layer

(MacLeod and Sammel, 1982). The most recent Newberry eruption occurred 1,300 years ago, at the end of a ~9000-year eruptive period (Morgan et al., 1997; Sammel et al., 1988). The geologic remnants of this particular cycle are air-fall tephra, ash- flows, and obsidian.

1.2.2. Newberry Hydrothermal System

Previous work by USGS provided information about the composition of the geothermal fluids, and a ~1000m drill core illuminated hydrothermally-altered lithologies (Keith and Bargar, 1988). At ~300m depth, volcanic flows and clastic debris are interrupted by a thick bed of hydrated basaltic sand, siltstone, and mudstone, believed to be of lake or riverine origin and suggesting the presence of a large caldera lake (Keith and Bargar, 1988). Below this unit, rhyodacite pumices and tuffs (i.e. possible ash flows) indicate evidence of likely caldera collapse events

(Higgins, 1973; Keith and Bargar, 1988). Above the lacustrine unit, rhyolitic lapilli, tuff, breccia, and alluvium are found, punctuated by the presence of many glassy fragments; these findings constitute evidence of subaqueous eruptions (Keith and

Bargar, 1988).

Fluids within the core of Newberry Volcano are probably transported along fractures, faults, and brecciated intrusions (MacLeod and Sammel, 1982), and those from the bottom of the drill hole consist primarily of steam and CO2 (Ingebritsen et 11

al., 1986). The presence of magmatic stocks 1-3 km below the caldera floor are well- evidenced (Stauber et al., 1988; Gettings and Griscom, 1988). This active hydrothermal system, from which hot springs and fumaroles derive, suggest the possibility of continued hot magma at depth (Ingebritsen et al., 2014).

1.2.3. Paulina Lake Descriptive Limnology and Hydrology

Resting at 1930 meters above mean sea level (i.e. 15 meters below its East Lake counterpart), PL is both the deeper of the two crater lakes and that of greater surface area. Its maximum depth, average depth, and volume are 76m, 50m, and 310 × 106 m3, respectively (Johnson, 1985). More than half of Paulina Lake is deeper than 60 meters (Figure 3).

Figure 3: Bathymetric map of Paulina Lake. Depth contours are represented in meters (Lefkowitz et al., 2016).

There are many steep valleys in Newberry Volcano’s flanks, but they are predominantly dry throughout the duration of the year. There are probably no riverine inputs into Paulina Lake, as evidenced by the lack of clay in its sediments (Upin,

2016), and it is posited that canyons within the caldera were derived during the last ice age (Russell, 1905). The only canyon with active water flow is Paulina Creek

Canyon, which is the historic site of a poorly dated catastrophic flood (Chitwood and

Jensen, 2000).

Unlike East Lake, a terminal lake, Paulina Lake has an overflow through

Paulina Creek (PCR), which discharges into the Little Deschutes River valley over the western caldera wall (Lefkowitz et al., 2016). A dam, built in PCR during the early twentieth century, controls the water level of Paulina Lake (Russell, 1905). The caldera presumably contains multiple permeable perched aquifers underlain by impermeable volcanic material (MacLeod and Sammel, 1982).

The Newberry caldera receives approximately 89 cm of rain and snow a year, predominantly between November and April (Sammel and Craig, 1983). Recharge sources for PL are probably both meteoric and subaqueous hot springs inputs

(Lefkowitz et al., 2016). Paulina Lake as well as East Lake both have bubbling hot springs along their peripheries, indicating input from subsurface hydrothermal fluids

(Russell, 1905; Johnson, 1985). Lake levels for Paulina Lake vary with annual precipitation, although it is assumed to exist in hydrologic steady state (Morgan et al.,

1997; Russell, 1905), since water levels in the lake have only fluctuated by ~5m since the mid-1800s (Russell, 1905). Paulina Lake is dimictic and freezes over between

November and May (Morgan et al., 1997). 13

Analyses of PL’s shoreline exposures provide important historical context for this study. In areas in which there is currently a direct hydrothermal input, beach sediments are silicified into 2-4 cm thick plates comprised of wave-rounded pebbles, scoria, lava, obsidian, and sand-sized pumice (Reynolds et al., 1998) (Figure 4).

Similar silicified plates were found submerged in the lake, and radiocarbon dates of recovered organic material yielded ages of 6.75 +/- 0.35 ka (Reynolds et al.,

1998). The silicification process that formed the now-submerged plates probably occurred near the lake’s surface, as evidenced by the wave-rounded sand and gravel found within. If the silicified plates indeed originated near the surface, it can be assumed that they originated around the shoreline (Reyolds et al., 1998).

These paleoshorelines vary from the modern ones in that they occurred around the full periphery of the lake. This disparity suggests that there may have been more hydrothermal activity both inside and on the periphery of the lake than there is today.

Furthermore, the paleoshoreline is restricted to a depth interval of 12-18 meters, which indicates a rapid increase in lake level. This change in lake volume, ranging anywhere between 18 and 30%, could have been caused by a variety of processes, including intracaldera eruptions, earthquakes, landslides, or changes in climate

(Lefkowitz et al., 2017).

Since the plates are positioned beneath the Interlake Obsidian Flow, Mazama tephra, and younger PL ash flow – all of which are of known ages – the age of the paleoshoreline can be gleaned via stratigraphic analysis. The radiocarbon date of 6.75

+/- 0.35 ka BP agrees with the stratigraphic constraints. The plates, therefore, indicate

that the separation of the lakes occurred presumably shortly before this time

(Reynolds et al., 1998; Lefkowitz et al., 2017).

Figure 4: Some of Paulina Lake’s uplifted shoreline consisted of silicified plates. This phenomenon

was observed exclusively in areas of known hydrothermal activity (Photo: Reynolds et al. 1998).

1.2.4. Geochemistry of Paulina Lake: Past Observations

While Sammel & Craig (1983) proposed that seepage occurs down the hydraulic gradient from East Lake to PL, the twin lakes have disparate water compositions.

Paulina Lake is three times more alkaline than East Lake and has observed pH values averaging ~8. In 2011, total dissolved solids concentrations in PL were around three 15

times higher with respect to East Lake, at about 15 mmol l-1. Bicarbonate is the main anion in PL, and PL exceeds East Lake in bicarbonate and most anion concentrations,

2- with the exception of SO4 (2-3 ppm). Trace elements are in the parts per billion to parts per trillion range in both lakes, but PL, notably, has about three times more arsenic in its water column (~14 ppb). These levels almost exceed drinking water limits (WHO, 1996). While the toxic element arsenic is observed in PL waters, East

Lake is uniquely host to elemental mercury. Fish in East Lake have high mercury concentrations, while those in PL indicate neither arsenic nor mercury in their tissues.

Elevated levels of these toxic elements are typical of many volcanic thermal springs

(Lopez et al., 2012).

Sediment compositions between the two lakes are also vastly different, although, in both lakes, they consist largely of diatom remains, organic matter, and volcanic components. In PL, for example, Corg contents are lower (~4%). Diatom frustules are abundant in sediments, and past XRF analyses for PL show 80-90%

SiO2. Critically, PL sediments have strikingly higher Fe and P enrichments compared to East Lake.

The discrepancies between the occurrences of toxic elements mercury and arsenic in the lakes are reflected in the sediments, too. Past analyses indicate concentrations of up to 250 ppm arsenic in PL sediments, and it is hypothesized that arsenic might be associated with the diagenetic ferrous iron phosphate mineral

2- vivianite (Fe3(PO4) ・8H2O), substituting in place of phosphorus in the mineral structure.

These discrepancies are theorized to stem from dissimilar hydrothermal supplies to the two lakes (Lefkowitz et al., 2016). East Lake receives CO2, H2S, and traces of Hg , as well as a relatively insignificant input of hot water with dissolved constituents. Contrastingly, PL presumably has aqueous hydrothermal inputs, rich in

Fe, Mn, P, As, and major cations. It is speculated that geothermal fluids rise below the lakes from the east side and, when they reach a vertical structure related to the emplacement of the volcanic ridge that separates the two lakes, the fluids develop a separate gas phase comprising of the CO2, H2S, and Hg traces. Then, the residual fluids, enriched in the aforementioned chemical constituents, enter PL. While geothermally-derived carbon enters East Lake as CO2 gas, it mainly enters PL as bicarbonate. As will be explained in the body of this paper, carbon cycles in various forms in the lake system, and it ultimately exits the system as CO2 via degassing phenomena or as bicarbonate through PCR.

Some of the hydrothermally-derived chemical constituents (e.g. Fe, As, Mn,

P) precipitate immediately in PL’s well-oxygenated, relatively neutral waters

(pH=~8) and settle into the sediments. Others (e.g. Ca, Na, K, Si) remain in chemical steady state as dissolved solids. These species either leave the system through PCR outflux or are sequestered by the ‘geophytic’ photosynthetic biota, thus sinking back

- down to the Fe2O3- and SiO2 -rich sediment (Lefkowitz et al., 2016).

2. Methods

2.1. Field Methods

Sample collection was conducted at Newberry Volcano in June and July of 2016 and in August 2017. Fieldwork coincided with wildfire burns as nearby as 5 miles, and a considerable amount of residual smoke was transported by wind to the study site. No precipitation occurred during the two-week period. Air temperatures ranged between

24-26 °C during the day and 6-7 °C at night.

2.1.1. Water Sampling

Two water depth profiles were taken for temperature, dissolved oxygen (DO), standard conductivity, pH, and oxidation potential measurements (Table 1). These data were obtained with a YSI Pro 2030 probe, which quantified each of the aforementioned parameters down to 30-m depth, and with a 3000 T-L-C YSI Meter, which measured solely temperature and standard conductivity down to 45-m depth.

The instruments were calibrated in standard solutions at Wesleyan University two weeks before their use. The vertical depths at which each measurement was taken are somewhat uncertain, because wind-induced boat drift sometimes caused the probe to drop through the water column at an angle. Therefore, some depths might be slightly overestimated.

Two 55-m water sample profiles were collected at 10-m increments using a

Teflon van Dorne water sampler (Table 1) down to 66-m water depth. The collected water was filtered in-situ through 0.45 µm filters and immediately subsampled into:

1) 1 L ultraclean bottles with 2 mL HCl for trace element analyses 2) 250 mL bottles for bulk ion analyses 3) 30 mL glass crimp cap bottles for alkalinity analyses 4) 30 18

mL glass crimp cap bottles for methane analyses 5) 12 mL glass exetainers with 5 mL samples for carbon stable isotope analyses. Supplemental surface water samples

(Table 1) were gathered in addition to those collected at water profile sites, one of which was collected in October 2017.

2.1.2. Sediment Sampling

A 2.6-m core was collected using a piston coring rig. The coring location (Table 1) was in the deep region of the lake to avoid sediment input through slumping from the sides. Two days after extraction from the lake, the core was extruded at 2-cm increments and transferred into plastic containers. Four grab samples (Table 1) were extracted from the lake bottom sediment using a stainless steel Van Veen grab sampler. The samples were air-dried in their containers at Wesleyan University.

General color striations were noted before drying, although visual appearance remained relatively homogenous all the way down the core’s length. Wet and dry weights were measured in order to determine bulk dry density. When dried, portions of sediment samples were ground and homogenized for further laboratory analysis using a mortar and pestle.

2.1.3. Measuring Lake Surface CO2 Evasion and Gas Sampling

CO2 flux surveys of PL were conducted at 31 sites in 2017 and at 8 sites in 2016. The coordinates of each location were measured with a handheld GPS system, and a floating accumulation chamber was deployed in order to measure lake surface CO2 evasion from the lake over a given period of time. Anhydrone (Mg(ClO4)2) desiccant 19

removed the water contents from the evaded CO2 prior to analyses, and the remaining gas was pumped into a LICOR (LI-6252) CO2 Analyzer. Using infrared absorption spectroscopy, the LICOR gave CO2 concentrations at 3-second intervals. Air pressure and temperature were also determined and used for later CO2 flux calculations. The increase in CO2 over time (ppm/sec) is the main parameter that determines the CO2 flux rate, after incorporation of the geometric factors of the float chamber.

Gas samples for isotope analyses were drawn via syringe from the accumulation chamber septum and were injected into a pre-evacuated exetainer for

13 δ C-CO2 analyses. The CO2 concentration in the accumulation chamber was recorded when each sample was drawn. Ambient air samples were also taken around the lake and in the surrounding Newberry Volcano area.

Sample ID Type Date Latitude Longitude (2017) (decimal deg., N) (decimal deg., W) CMPPLV1 YSI profile 8/23 43.71491 121.27245 CMPPLV2 YSI profile 8/30 43.71281 121.25354 CMPPLW1 Water profile 8/27 43.72396 121.25594 CMPPLW2 Water profile 8/28 43.71242 121.26214 CMPPLS1 Surface 8/30 43.72051 121.25068 CMPPLS2 Surface October -- -- CMPPLHS Hot springs 8/30 43.72982 121.24772 CMPCPL1 Sediment core 8/31 43.72078 121.25441 GCMPPL1 Grab 8/30 43.71628 121.27084 GCMPPL2 Grab 8/30 43.71286 121.26457 GCMPPL3 Grab 8/30 43.61951 121.25357 GCMPPL4 Grab 8/31 43.72083 121.25441

CMPPL3 CO2 gas 8/27 43.71467 121.22253

CMPPL8 CO2 gas 8/27 43.71636 121.26915

CMPPL11 CO2 gas 8/27 43.71459 121.26401

CMPPL15 CO2 gas 8/28 43.71288 121.24832

CMPPL18 CO2 gas 8/28 43.72311 121.24954

CMPPL20 CO2 gas 8/30 43.72982 121.24717

CMPPL31 CO2 gas 8/30 43.72988 121.24784 CMPAIR1 Air 8/28 43.70409 121.31767 CMPAIR2 Air 8/28 43.70581 121.30006 CMPAIR3 Air 8/30 43.69955 121.37725 Table 1: Paulina Lake 2017 sample IDs, types, collection dates, and coordinates (decimal degrees).

2.2. Laboratory Analyses

2.2.1. Ion Concentrations in Water

All water samples were analyzed for their major ions. An Ion Chromatograph Dionex

- 2- 600 measured the contents of anions chloride (Cl ) and sulfate (SO4 ), using a calibration curve of known standards.

Concentrations of the major cations (Ca, Mg, Na, K, and Si) were obtained using an Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) at 21

Smith College in Massachusetts. Results were compared against those acquired by a range of standards (1-60 ppm), which were made from 1000 mg/L stock solutions of

Ca, Mg, Na, K, and Si diluted with 2% nitric acid.

Alkalinity and pH were determined at Wesleyan University using a Mettler

DL12 auto-titrator, which was calibrated using NBS standard liquids (pH= 4, 7, and

10). The titrant consisted of 0.1 N HCl, and the final pH was set at ~4.3. Synthetic and ocean water standards were used to ensure precision and accuracy of measurements. Because laboratory pH measurements were calculated at room temperature (22 °C), as opposed to field temperature (~4 °C), YSI field temperature and pH measurements were used for the DIC calculations.

2.2.2. Multi-elemental Analyses of Sediment

Fifty finely pulverized sediment samples were sent offsite to SGS Mineral Services for multi-elemental analyses. Samples were first fused into glass with Na2O2 and then digested with a concentrated aqua regia mixture of HCl and HNO3. This digestion method ensured total chemical recovery in the solution, with no residual solids. The samples were then diluted and finished with either ICP-MS or ICP-AES analysis.

Thirty samples were analyzed by ICP-AES for 29 elements and twenty samples by

ICP-MS for 56 elements.

2.2.3. Stable Isotopes in Water

The δ18O and δD isotopes in water were measured at the UC-Davis Stable Isotopes

Facility using a Laser Water Isotope Analyzer V2. Each sample was injected at least 22

six times, and the average of the last four injections was used for isotope ratio calculations. Isotopes measurements were standardized against a range of reference waters; precision for δ18O was <3.0 per mil, and precision for δD was <2.0. Final values were reported relative to VSMOW.

The δ13C of dissolved inorganic carbon (DIC) was also measured by mass spectrometry (IRMS) at UC-Davis Stable Isotopes Facility. Both lithium carbonate dissolved in degassed deionized water and deep seawater were used as standards, and the average standard deviation was 0.03‰. Final values were reported relative to

VPDB international standard.

2.2.4. Stable Isotopes of CO2 Gas

Stable isotopes of CO2 gas were measured at UC Davis Stable Isotope Facility in headspace vials using a ThermoScientific GasBench system interfaced to a

ThermoScientific Delta V Plus isotope ratio mass spectrometer. The gas was sampled using a six-port rotary valve and separated from N2O and other residual gases by a

Poroplot Q GC column. Pure CO2 reference gas was used to calculate delta values for the sample peak and then fine-tuned for changes in linearity and instrumental drift.

Laboratory reference materials were analyzed with every 10 samples. Laboratory reference materials were calibrated against NIST 8545, and final values were reported relative to VPDB international standard.

2.3. Calculations

2.3.1. Web-PHREEQ

Chemical speciations of water profiles CMPPLW1 and CMPPLW2 were acquired using Web-PHREEQ, an online aqueous geochemistry modeling program. Accurate pH measurements were made for the first time in-situ with the YSI; aqueous speciations could, therefore, be attained in one step by using field temperatures and pH. In a preliminary run of Web-PHREEQ, measured alkalinity, O2 saturation, total cation and anion concentrations, field temperature, and field pH (fixed) were used as input in order to acquire activities and concentrations of aqueous inorganic species.

Web-PHREEQ also calculates theoretical saturation indices for a range of minerals.

The results of the first Web-PHREEQ run were used to calculate the δ13C of dissolved CO2. PHREEQ calculated the concentration of total inorganic carbon in the water as well as all present carbonate species. Using δ13C-DIC data from UC-Davis, carbonate species concentrations derived from Web-PHREEQ, and known temperature dependent interspecies isotope fractionations (Mook, 2001), these results

13 were incorporated into an isotope mass balance equation to solve for δ C-CO2 (aq).

2.3.2. CO2 Flux Calculations and Sequential Gaussian Simulation (SGS)

The flux rate at each of the 31 sampling sites was acquired by plotting the concentration of CO2 over time and drawing a best fit line. The slope, denoting the change in CO2 concentration per second (ppm/s), was scaled by a factor, K, which incorporates the ideal gas law and float chamber geometry:

V 86400 × 푃 K = × A T × R × 106 where

 P = barometric pressure (HPa)  R = gas constant 0.083145 (bar L K-1 mol-1)  T = air temperature (K)  V = chamber net volume (m3)  A = chamber inlet net area (m3).

(Equation 1)

-1 -1 A CO2 flux rate (in moles of CO2 m day ) was acquired by multiplying the linear increase of CO2 over time by the K factor.

A Sequential Gaussian Simulation (SGS) was used to spatially interpolate the flux data and estimate the daily total CO2 emission from the surface of the lake, as well as to determine associated uncertainty. The simulation was conducted using the statistical program R. The data points were declustered, then interpolated via normal score transformation. Once the data pool was expanded, the program generated histograms and variograms that accurately reflected the original data. 1000 equiprobable simulations of data spatial distributions were produced and then averaged together. The differences among all of the simulations were used to compute the uncertainty of the flux estimation (Mazot et al. 2008, Deutsch and

Journel, 1998, Goovaerts, 2001). The mean of all data points acquired by the averaged simulation was ultimately multiplied by the surface area of the lake

2 (6,196,000 m ) and molar mass of CO2, which yielded total CO2 flux in metric tonnes.

3. Results

3.1. Field Measurements

3.1.1. Temperature

The temperature depth profiles of PL show thermal stratification in late August before fall turnover occurs (Figure 5). Paulina Lake’s surface waters in August 2017 were

~18.9 °C, with the pronounced thermocline positioned between 6.5 and 20.5 meters.

The hypolimnion extended from the bottom of the thermocline to the lake bottom, where the temperature remained relatively constant at ~4.2 °C.

The 2011 data were not as high in resolution, but the thermocline seemed to have existed at around the same depth. Bottom waters were warmer at the measurement site in June of 2011, at an average of 7.2 °C. These measurements were taken on-board the sampling boat, so samples may have experienced some warming before they were measured.

Temperature (°C) 0 2 4 6 8 10 12 14 16 18 20 0 Epilimnion 10

20 Thermocline

30 Hypolimnion Depth Depth (m)

40 CMPPLV1 CMPPLV2 PL 2011 50

Figure 5: In-situ depth temperature profile of Paulina Lake waters (°C), showing thermal stratification in August 2017 and June 2011.

3.1.2. Dissolved Oxygen (DO)

Paulina Lake had moderate dissolved oxygen levels at the surface, with concentrations of ~9 mg/L (Figure 6). Dissolved oxygen concentrations in the thermocline were ~20 mg/L, indicating a possible photosynthetic contribution. As water temperature dropped below the thermocline-hypolimnion interface, dissolved oxygen levels also dropped. Measurements were limited to a depth of 30 meters, but oxygen levels probably remained low for such cold waters, at ~6 mg/L.

Within the shallowest 30 meters, dissolved oxygen contents were generally lower in 2011 than in 2017. 2011 surface waters had 1.2 mg/L of dissolved oxygen.

2011 data also indicated a sharp dissolved increase in dissolved oxygen at 10 meters,

but only up to 9.2 mg/L. Deep waters showed constant depletion of dissolved oxygen, at around 2.5 mg/L.

Dissolved Oxygen (mg/L) 1 3 5 7 9 11 13 15 17 19 21 23 0

Depth (m) Depth 40 CMPPLV1 50 CMPPLV2 PL 2011 60

Figure 6: Dissolved oxygen depth profiles of Paulina Lake waters (mg/L).

3.1.3. pH

Field analyses in 2017 indicate a trend of decreasing pH with depth in PL (Figure 7).

Surface waters had a pH of 8.62, and waters below the thermocline had a pH of ~7.5.

This pH change of ~1 unit causes a significant depth-related shift in the carbonate speciation.

Neither 2015 nor 2011 showed as drastic of a negative pH gradient with depth. Surface waters were slightly more acidic with a pH of ~8.4 and only decreased by ~0.35 from top to bottom. The pH of bottom waters during 2015 and 2011 had values of ~0.5 greater than those during 2017. 28

pH 7 7.5 8 8.5 9 0

Depth Depth (m) 40 CMPPLV1 CMPPLV2 50 PL 2015 PL 2011 60 Figure 7: 2017 in-situ pH depth profiles of Paulina Lake waters, as measured with the YSI Pro 2030 probe, as well as 2015 field and 2011 laboratory measurements. Laboratory pH measurements were made at 25 °C.

3.1.4. Conductivity

Conductivity is an indicator of the abundance of total dissolved solids (TDS) in natural waters, because the current in an electrolyte solution is primarily dependent on the concentration of ions (Hayashi, 2004). Because conductivity is temperature- dependent, measured conductivities throughout PL’s thermal gradient were corrected to 25°C.

Paulina Lake surface waters had a conductivity of ~0.62 μS/cm. At around 5 meters, there was a sharp decline in conductivity to 0.59 μS/cm, indicating less dissolved material. Conductivity remained relatively constant from 15 to 30 meters

(Figure 8).

Temperature Corrected Conductivity (μS/cm TC to 25 0.58 0.59 0.6 °C) 0.61 0.62 0.63 0.64 0

15 CMPPLV1

20 CMPPLV2 Depth Depth (m) 25

Figure 8: Conductivity depth profiles of Paulina Lake waters (μS/cm), corrected to a temperature of 25 °C.

3.1.5. Paulina Creek Flow Rate

USGS gauging station data was used to assess both long- and short-term temporal changes in PCR’s flow rate (m3/sec). The data provided monthly resolution between the years 1982 and 1995. Figure 8 shows both the slight decrease in discharge over the years and provides a visual for the seasonal patterns under which flow rates changed. Both yearly and monthly average discharges were averaged in order to quantify these changes (Figures 9-10).

The plot for average yearly discharge by year shows a slight decrease (m = -

0.0277) in average flow rate between 1982 and 1994 (Figure 11). The discharge of

PCR steadily declined between 1982 and 1987 from 0.69 to 0.46 m3/sec; after a relatively sharp increase in discharge to 0.61 m3/sec in 1988, there was a more gradual decrease back to 0.39 m3/sec by 1994. 30

The plot for average discharge by month illustrated a weakened average flow rate during the winter months and peak average flow rate in the summer month of

June. Between January and June, there was an increase in average discharge from

0.45 to 0.63 m3/sec and then a drop to an average of 0.28 m3/sec in the colder months of November and December.

1 Paulina Creek Discharge from 1982-1995 0.9 0.8 y = -0.0026x + 0.67 0.7 0.6 0.5 0.4 0.3

Discharge (m3/sec) 0.2 0.1 0 0 12 24 36 48 60 72 84 96 108 120 132 1982 Year Figure 9: 1982-1995 USGS Paulina Creek discharge data (m3/sec).

Paulina Creek Avg. Monthly Discharge 1982-1995 0.7

0.6

0.5

0.4

0.3

0.2 Average MonthlyDischarge (m3/sec) Average Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Month Figure 10: 1982-1995 USGS Paulina Creek discharge data (m3/sec).

1 Paulina Creek Avg. Discharge 1982-1994 0.9 0.8 0.7 0.6 0.5 0.4 y = -0.028x + 0.67 0.3

Discharge (m3/sec) 0.2 0.1 0 1 2 3 4 5 6 7 8 9 10 1983 Year 1994 Figure 11: Paulina Creek average yearly discharge (m3/sec) by month for 1982-1994 USGS data. 32

3.2. Water Chemistry

3.2.1. Alkalinity

The two alkalinity profiles of PL indicated disparate trends but similar ranges (Figure

12). The alkalinity of CMPPLW1 remained relatively constant with depth. The alkalinity was 6.8 mmol/L at the surface and increased to 6.86 at 50 meters.

CMPPLW2 showed a steeper positive gradient with depth. The alkalinity was 6.6 mmol/L at the surface, spiked up to 6.96 mmol/L at 20 meters, and averaged at 6.87 mmol/L between 30 and 60 meters. Despite discrepancies above the hypolimnion, both profiles were within an average of 1.2% from each other below 30 meters. 2011 data indicated lower alkalinities, ranging between 6.36 and 6.53 mmol/L. Like

CMPPLW2, alkalinity values were lower at the surface.

Alkalinity (mmol/L) 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 0

20 CMPPLW1

30 CMPPLW2 Depth Depth (m) 40 PL 2011

Figure 12: Alkalinity (mmol/L) depth profiles of Paulina Lake waters.

3.2.2. Major Anions

Anions observed in past studies of PL were analyzed in CMPPLW1 and CMPPWL2.

2- In both profiles, there was no apparent trend for (SO4) with depth (Figure 13).

Concentrations were variable with depth and ranged between 0 and 3.24 ppm. The same held true for Cl- concentration profiles, in which concentrations ranged between

- 2- 0 and 3.85 ppm and had no apparent patterns (Figure 14). Cl and (SO4) data in

2011, 2012, and 2014 laid in the upper range of those examined in 2017, and they were, notably, more constant with depth.

(SO )2- (ppm) 0 1 4 2 3 4 0

10 2017 PL1

20 2017 PL2 PL 2014 30 PL 2012

Depth Depth (m) PL 2011 40

2- Figure 13: (SO4) concentration depth profiles of Paulina Lake waters (ppm).

Cl- (ppm) 0 1 2 3 4 0

10 2017 PL1 20 2017 PL2 PL 2014 30 PL 2012 Depth Depth (m) PL 2011 40

Figure 14: Cl- concentration depth profiles of Paulina Lake waters (ppm).

3.2.3. Major Anions

The concentrations of major cations K+, Na+, Mg2+, Ca2+, and Si4+ were determined in both water profiles. Results were plotted on two separate graphs solely for visual clarity, based on concentration range (Figures 15 and 16). Most cations were relatively constant with depth with the exception of calcium. The mean concentrations for K+,

Na+, Mg2+, and Si4+ were 6.2, 52.4, 43.4, and 21.1 ppm respectively. 2017 was the first year in which relatively highly variable calcium cation concentrations were observed with depth (Figure 16). Calcium ions in 2017 were depleted as low as 8.56 ppm in surface waters and had a mean concentration of 23.3 ppm. In all prior years, calcium gradients were comparatively constant with depth with higher average values, between

23.8 and 29.0 ppm (Figure 17). 35

Cation concentration (ppm) 0 10 20 30 0

20 Ca (W1) Ca (W2) K (W1) 30 K (W2) Depth Depth (m) Si (W1) 40 Si (W2)

60 Figure 15: 2017 cation concentration depth profiles of Ca, K and Si ions (ppm) in Paulina Lake.

Concentration (ppm) 0 10 20 30 40 50 60 0

Depth Depth (m) 40 Mg (W1) Mg (W2) 50 Na (W1) Na (W2) 60

Figure 16: 2017 cation concentration depth profiles of Mg and Na ions (ppm) in Paulina Lake. 36

Concentration Ca (ppm) 5 10 15 20 25 30 0

10 2017 W1 20 2017 W2 2015 30 2014 2012

Depth Depth (m) 40 2011 50

Figure 17: Calcium ion concentration depth profiles in Paulina Lake waters from 2011-2017.

3.2.4. Stable Isotopes of Water

For stable isotopes of both hydrogen and oxygen in water, isotopic values were slightly higher in surface waters (Figures 18 and 19). δ2H was about ~88‰ at the surface and decreased to ~-92‰ at 10 meters. Values for δ2H remained relatively constant below 10 meters, fluctuating between -89.3‰ and -93.6‰. Surface δ18O was

~10.4‰ and decreased to ~-11.2‰ between 10 and 20 meters. δ18O remained relatively constant below 20 meters, fluctuating between -11.04 and -11.35‰. Hot spring waters were lighter compared to the rest of the lake. Isotopic values in the hot

2 18 springs were -105‰ and -13.62‰ for δ H and δ O, respectively. 37

2 2017 Paulina Lake δ HH2O Water Profiles δ2H (‰, VSMOW) -107 -102 -97 -92 -87 0

30 Depth (m) (m) Depth CMPPLW1 CMPPLW2 40 PLSurface1 50 PL hot springs

60 2 Figure 18: Paulina Lake 2017 depth profiles of δ H-H2O (‰) relative to VSMOW.

2017 Paulina Lake δ18OH2O Water Profiles δ18O (‰, VSMOW) -14 -13 -12 -11 -10 0

30 Depth (m) (m) Depth CMPPLW1 40

CMPPLW2 50

18 Figure 19: Paulina Lake 2017 depth profiles of δ O-H2O (‰) relative to VSMOW. 38

2017 oxygen isotope data was analyzed comparatively to 2011-2016 data for possible temporal trends (Figure 19). Isotopic values for 2017 deep waters fit well within the numerical range of samples taken between 2014 and 2017, while

CMPPLW1 and CMPPLW2 surface waters were heavier by about 0.4 and 1‰, respectively. In 2011, oxygen in water was heavier, with δ18O values reaching to -

9.43‰ in the epilimnion and -10.81‰ below the thermocline. Although isotopic values were generally higher in 2011, trends with depth that year were akin to those in 2017. Surface waters for both 2011 and 2017 were heavier and then got lighter at

20 meters. 2011 and 2017 samples were both collected in late August, while 2014-

2016 samples were collected in June.

3.3. Carbon Studies

3.3.1. CO2 Flux Field Data

Paulina Lake flux measurements from 2017 indicated an average flux rate of

-2 -1 -2 -1 0.13 moles CO2 m day and ranged between 0.2 and 0.6 moles CO2 m day

(Figure 20, Table 2). Two data points taken directly over the hot springs showed

-2 -1 higher fluxes of 0.4 and 0.6 moles CO2 m day , while all other points showed fluxes

-2 -1 of less than 0.27 moles CO2 m day . In 2016, 11 data points taken over Paulina

-2 -1 Lake had a range of fluxes from 0.04 to 0.35 moles CO2 m day , with an average of

-2 -1 0.17 moles CO2 m day .

Sample ID Latitude Longitude CO2 Flux °N °W moles m-1 day-1 CMPPL1 43.7151 121.27453 0.023137711 CMPPL2 43.7149 121.27401 0.072219818 CMPPL3 43.71467 121.27253 0.069554279 CMPPL4 43.71403 121.27381 0.04253426 CMPPL5 43.71511 121.27422 0.056237111 CMPPL6 43.71605 121.27422 0.065103237 CMPPL7 43.71611 121.27118 0.115998679 CMPPL8 43.71636 121.26915 0.059507904 CMPPL9 43.71572 121.26973 0.06148917 CMPPL10 43.73055 121.26673 0.028796917 CMPPL11 43.71459 121.26401 0.105235887 CMPPL12 43.71263 121.26214 0.110734545 CMPPL13 43.711823 121.25862 0.048485906 CMPPL14 43.710397 121.253385 0.129893616 CMPPL15 43.712878 121.248321 0.044568575 CMPPL16 43.715546 121.249523 0.083366316 CMPPL17 43.718012 121.249888 0.08438644 CMPPL18 43.723098 121.249544 0.13278825 CMPPL19 43.722974 121.253664 0.05842843 CMPPL20 43.724215 121.258042 0.065475023 CMPPL21 43.721734 121.265337 0.039922564 CMPPL22 43.71956 121.26774 0.193037353 CMPPL23 43.72112 121.2633 0.145020083 CMPPL24 43.72333 121.25835 0.102918592 CMPPL25 43.72604 121.25435 0.268821107 CMPPL26 43.72821 121.25223 0.217715355 CMPPL27 43.73115 121.25148 0.164296714 CMPPL28 43.73017 121.24802 0.2464232 CMPPL29 43.729987 121.247881 0.59740548 CMPPL30 43.7297 121.24723 0.40715481 -2 -1 Table 2: Average CO2 flux measurements (moles m day ) and respective sampling locations.

-2 -1 Figure 20: Map of Paulina Lake 2017 CO2 fluxes (moles m day ).

KEY

= 0.4-0.6 moles m-2 day-1

= 0.2-0.4 moles m-2 day-1

= 0.1-0.2 moles m-2 day-1

= 0-0.1 moles m-2 day-1

3.3.2. Stable Isotopes of Chamber CO2 Gas and Ambient Air

13 The δ C (CO2) values for chamber gas samples ranged between -10.0 and -12.0‰.

δ18O values ranged between -4.5 and -10.5‰. These samples were mixtures of ambient air and lake gas, since the chamber was filled with the ambient air prior to flux measurement. The chamber CO2 concentrations were used for calculations and plots instead of exetainer-derived concentrations, which were less precise. The δ13C of ambient air samples from the surrounding Newberry Volcano area were very

13 similar to those of the chamber samples, and δ C (CO2) ranged between -10.0 and -

11.5‰ (Table 3, Figures 21-22).

18 δ O -CO2 of 2017 Gas Samples Concentration of CO2 (ppmv) 0 0 200 400 600 -2

-4 (VPDB) (VPDB)

2 -6

CO -

O -8 Chamber 18

δ Gas -10 Ambient Air -12

18 Figure 21: Field CO2 concentration (ppm) versus δ O-CO2 (‰, VPDB) for chamber and ambient air samples.

13 δ C -CO2 of 2017 Gas Samples Concentration of CO2 (ppmv) 0 0 200 400 600 -2 -4

(VPDB) -6 2

-8

CO -

C -10 Chamber

13 Gas δ -12 Ambient Air -14

13 Figure 22: Field CO2 concentration (ppm) versus δ C-CO2 (‰, VPDB) for chamber and ambient air samples.

Sample Lab 13 ID Latitude Longitude 휹 CVPDB CO2 Chamber CO2 (decimal (decimal degs., N) degs., W) ‰ ppmv ppm CMPPL3 43.71467 121.22253 -11.0 527 456 CMPPL8 43.71636 121.26915 -11.7 535 466 CMPPL11 43.71459 121.26401 -11.9 544 471 CMPPL15 43.71288 121.24832 -11.2 536 467 CMPPL18 43.72311 121.24954 -12.1 567 450 CMPPL30 43.72982 121.24717 -9.9 608 609 CMPPL31 43.72988 121.24784 -10.9 484 699 CMPAIR1 43.70409 121.31767 -10.0 423 N/A CMPAIR2 43.70581 121.30006 -10.6 436 N/A CMPAIR3 43.69955 121.37725 -11.5 458 N/A Table 3: Field and laboratory CO2 gas data.

3.3.3. δ13C of Dissolved Inorganic Carbon (DIC)

The δ13C-DIC trends with depth obtained in 2017 deviated from those historically observed in PL (Figure 23). From 2011-2016, δ13C(DIC) values were generally constant with depth, at an average of about -0.4‰. Since 2011, δ13C(DIC) has become slightly lighter. In 2017, deep waters were markedly lighter than in past years, at an average of -1.4‰. Furthermore, there is a new observed gradient above the thermocline; the δ13C(DIC) of surface waters reached ~+0.5‰. δ13C-DIC values have been consistently lower in PL relative to East Lake, which remains true in 2017.

DIC-δ13C Depth Profiles 2011-2017 13 δ CVPDB (‰) -2.5 -0.5 1.5 3.5 0

10 2017 W1 (late Aug.) 2017 W2 (late Aug.) 20 PL 2017 hot springs EL 2017 (late Aug.) 30 2016 (June) 2015 (June) Depth(m) 40 2014 (June) 2011 (late Aug.) 50

13 Figure 23: DIC-δ CVPDB (‰) water profiles of Paulina Lake between 2011 and 2017. Seasons during which samples were taken are noted, because seasonality might affect the isotopic gradient of DIC.

13 3.3.4. δ C of Aqueous CO2

The δ13C-CO2 (aq) depth gradient has a negative slope (Figure 24). Dissolved CO2 in surface waters was heavier than that in deeper waters. Isotopic values were about -

7.6‰ at the surface and sharply decreased in the thermocline to ~-11‰. Lighter dissolved CO2 in the hypolimnion was relatively constant, fluctuating between -11 and -10‰. This trend with depth was a result of declining temperature and pH with depth, as well as an increasing δ13C(DIC) in the surface.

13 δ C-CO2VPDB (aq), (‰) -12 -11 -10 -9 -8 -7 -6 0

20 CMPPLW1

30 CMPPLW2

Depth Depth (m) 40

13 Figure 24: Paulina Lake 2017 water profiles of δ CVPDB in dissolved CO2 (‰).

3.4. Sediment Chemistry

3.4.1. Bulk Dry Density

The bulk dry density (BDD) of sediment in core CMPPLC1 varies between 0.12 and

0.40 g/cm3 (Figure 25). Generally, the BDD of the core increases with depth. Between

113 and 150 cm, BDD increases with a peak of 0.40 g/cm3 at 121 cm, which coincides with a volcanic ash layer in the core.

0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10

Bulk Dry Density (g/cm3) Density Dry Bulk 0.05 0.00 0 50 100 150 200 250 Depth (cm) Figure 25: Bulk dry density (g/cm3) of CMPPLC1 sediment core with depth (cm).

3.4.2. Major Elements

Preliminary analyses indicated sediments particularly rich in iron (up to 22%

Fe2O3), silica (up to 29%), manganese (up to 5700 ppm), and phosphorus (up to 2%

P2O5). 46

Iron

Concentrations for Fe2O3 showed strong variation throughout the length of the core.

There was a dip in concentration to 4.65% between 105 and 137 cm, and a pulse reaching 22.45% at around 219 cm. These disparities are denoted in red and blue respectively (Figure 26).

(%) 15

O 2

Fe 10

0 0 50 100 150 200 250 Depth (cm)

Figure 26: CMPPLC1 depth profile of Fe2O3 (%) in Paulina Lake sediments.

Silica

Paulina Lake sediments are comprised of silica from biogenic, hydrothermal, and ash sources (See: Discussion). The total silica depth profile mirrored trends of the iron concentration depth profile (Figure 27). Silica levels peaked at 29.5% at 125 cm, which is where the iron curve hit its minimum. Similarly, the minimum of the silica curve (19.2%) coincided with the maximum of the iron curve at 223 cm. Since iron 47

and silica are the two main components of the sediment, this inverse relationship is largely the result of the constant sum (100%) issue.

Concentration (%) Concentration Si 5 Fe2O3 0 0 100 200 Depth (cm)

Figure 27: CMPPLC1 depth profiles of Si and Fe2O3 (%) in Paulina Lake sediments.

Phosphorus

Phosphorus is strongly enriched in these sediments, and concentrations ranged between 0.05 and 0.86%. The minimum value was reached in the region between 117 to 141 cm. There was a small peak between 37 and 57 cm, and a larger one between

203 and 243 cm (Figure 28).

1 0.9 0.8 0.7 0.6

0.5 P (%) P 0.4 0.3 0.2 0.1 PL 2107 0 0 100 200 Depth (cm) Figure 28: CMPPLC1 depth profile of phosphorus (%) in Paulina Lake sediments.

Manganese

Manganese concentrations varied between 1835 and 9689 ppm throughout the sediment core (Figure 29). There was a dip in concentration in the 117 to 141 cm region of the core, where manganese concentration levels dropped to 1835 ppm. The manganese profile lacked the characteristic spike between 215 and 237 cm associated with iron, although there was a minor increase. Instead, the curve hit its maximum closer to the top of the core at 109 cm.

12000

10000

8000

6000 Mn (ppm) Mn

4000

2000

0 0 50 100 150 200 250 Depth (cm) Figure 29: CMPPLC1 depth profile of manganese (ppm) in Paulina Lake sediments.

Other major elements

Other major elements had less extreme concentration variations and are plotted together (Figures 30-31). Potassium peaks between 121 and 137 cm, with a maximum of 2.7%. Throughout the rest of the core, potassium concentrations remained below 0.1%. Concentration values for Ca, Mg and Ti were 0.81, 0.50, and

0.03 % respectively.

3 K 2.5 Ca

2 Mg Ti 1.5

1 Concentration (%) Concentration

0.5

0 0 50 100 150 200 250 Depth (cm)

Figure 30: CMPPLC1 depth profiles of major elements potassium, calcium, magnesium, and titanium (%) in Paulina Lake sediments.

1.2 K Ca 1 Mg

0.8 Ti

0.6

0.4 Concentration (%) Concentration

0.2

0 0 50 100 150 200 250 Depth (cm) Figure 31: Zoomed-in CMPPLC1 depth profiles of major elements potassium, calcium, magnesium, and titanium (%) in Paulina Lake sediments. 51

3.4.3. Trace Elements

Trace elements were plotted according to whether their concentrations depressed or peaked in the 121 to 141 cm region. Figure 32 depicts the depth profiles for Ni, V, Co, Sr, and Zn (ppm), all of which reached their minimums in this region.

The concentrations of Ni, V, and Co all peaked in the bottom half of the core, while

Sr and Zn remained low and relatively constant below 50 ppm throughout.

Trace Elements 1 250 Ni V 200 Co Sr Zn 150

100

50 Concentration (ppm) Concentration

0 0 50 100 150 200 250 Depth (cm)

Figure 32: CMPPLC1 depth profiles of trace elements nickel, vanadium, cobalt, strontium, and zinc (ppm) in Paulina Lake sediments.

Figures 33-34 depict trace elements that reached a peak in the 121 to 141 cm region. Ba reached the highest peak in this region, at 660 ppm. Zr also had a considerable increase to 266 ppm. While these elements had the most drastic increases, Th, Cs, and U also increased in this interval. 52

700 Trace Elements 2 Ba 600 Zr Rb 500 Th 400 Cs U 300

200

Concentration (ppm) Concentration 100

0 20 70 120 170 220 270 Depth (cm)

Figure 33: CMPPLC1depth profiles of trace elements barium, zirconium, rubidium, thorium, cesium,

100 Trace Elements 2 Ba 90 Zr 80 Rb Th 70 Cs U 60 50 40 30

Concentration (ppm) Concentration 20 10 0 20 70 120 170 220 270 Depth (cm) and uranium (ppm) in Paulina Lake sediments. 53

Figure 34: Zoomed-in CMPPLC1 depth profiles of trace elements barium, zirconium, rubidium, thorium, cesium, and uranium (ppm) in Paulina Lake sediments. Arsenic

Arsenic levels varied greatly with depth between 11 and 243 ppm, which are considered relatively high levels of arsenic. Arsenic concentrations reached their minimum in the ash region between 105 and 137 cm. They peaked in an area of sharp increase between 207 and 255 cm, which was also where iron contents peaked

(Figure 35).

250

200

150

100

50 Concentration Arsenic Arsenic (ppm) Concentration 0 0 50 100 150 200 250 300 Depth (cm) Figure 35: CMPPLC1 depth profiles of arsenic (ppm) in Paulina Lake sediments.

3.4.4. Indicators for the presence of volcanic ash

Several trace elements were highly concentrated in the ash layer and were very low throughout the rest of the core. These elements all exhibited similar trends.

Barium, yttrium, zirconium, and aluminum, as some examples, all showed an abrupt exponential rise at ~141 cm. Concentration levels remained elevated until 117 cm, where there was a gradual trailing off in concentration. The aluminum curve is depicted in Figure 36. The low, almost constant Al concentrations in the sediment outside of the ash layer are striking, but they are in agreement with observations that no clay-fraction material is present here.

Core Depth vs. Aluminum Concentration 6

3 CMPCPL1 2

0 Concentration Aluminum (%) Aluminum Concentration 0 100 200 Depth (cm)

Figure 36: CMPPLC1 depth profile of aluminum concentrations (%) in Paulina Lake sediments

3.4.5. Vivianite

100

1 10 15 20 25 30 35 40 45 50

Figure 37: XRD pattern for vivianite with reference data peaks.

2- The phosphate mineral vivianite (Fe3(PO4) ‐8H2O) is of particular interest to this study, because it might provide a host phase for the element arsenic. XRD analyses of what was presumed to be vivianite showed near-perfect matches for just about every peak (Figure 37). Vivianite nodules, furthermore, were analyzed via Scanning

Electron Microscopy; according to the machine’s compositional analysis, the green- blue grains in question had P, O, and Fe, vivianite’s key chemical components

(Figures 38-39). Three grains were analyzed on the SEM, and none of them showed that vivianite is a host phase for arsenic. More data is needed, however, to conclusively make this determination.

57 Figure 38: SEM point analysis schematic for vivianite nodule.

Figure 39: SEM photographs of vivianite nodules. 58

4. Discussion

4.1. Water Chemistry

The water chemistry data facilitated a better understanding of PL’s mixing trends, water balance, and biogeochemical cycles. This knowledge informed the construction of more robust chemical models and, ultimately, an estimate for the concentration of the entering hydrothermal fluids.

4.1.1. Physical Limnology

Paulina Lake exhibited some characteristic components of a dimictic lacustrine system. The lake had a pronounced thermal gradient in late August, with a thermocline between 6.5 and 20.5 meters (Figure 5). Surface waters were ~18 ºC, and the hypolimnion was ~4.3 ºC. As the summer progressed after spring turnover, surface waters warmed relative to bottom waters. This observation was consistent with data accrued in past years. Typically, PL expresses steeper summer temperature gradients and no gradients after fall and spring turnover events, indicating holomixing

(Lefkowitz et al. 2016).

Dissolved oxygen levels are elevated in the thermocline (Figure 6), due to the presence of photosynthesizing primary producers such as phytoplankton (Johnson,

1985). This oxygen is constantly released through diffusional exchange with the atmosphere. Bottom waters, on the other hand, are hypoxic due to the bacterial respiration of organic matter. These de-oxygenated, less oxidizing waters in the hypolimnion do not get replenished.

4.1.2. Chemical Trends

A relatively strong negative pH gradient was observed in 2017 compared to in earlier years (Figure 7). pH is defined as the negative logarithm of hydrogen ion activity, so the total decrease of ~1 from top to bottom waters reflects a significant shift in carbonate speciation. A negative pH gradient in the lake could be pronounced by the reintroduction of CO2 (and subsequent production of carbonic acid) to the lake bottom via bacterial respiration and the oxidation of organic matter. An increase in subsurface hydrothermal inputs might also reduce the pH, depending on the pH of the hydrothermal fluids. At the surface, a combination of photosynthetic processes and

13 CO2 surface evasion might raise the pH value, and the trend in δ C(DIC) also suggests the escape of isotopically light CO2.

All cations have generally remained constant with depth over the last seven years. The subsurface hydrothermal fluid inputs are presumably highly concentrated in dissolved solids; in the summer months when there is little whole-lake mixing, an enrichment in bottom waters might be expected but is not observed.

Paulina Lake waters also lack some expected nutrient gradients. Dissolved silica is needed for diatom productivity (Telford et al., 2004). This silica uptake should theoretically cause a depletion of silica in the surface waters, but no such phenomenon was observed. Calcium was the only element with a gradient with depth

(Figure 17). There was a depletion of calcium in the surface waters, which was a new observation. It was initially hypothesized that there was increased calcite precipitation, but recent mineralogical analyses indicated the presence of no calcium carbonate in PL sediments. Perhaps, calcium may have dissolved in deeper waters. 60

The lack of chemical gradients suggests relatively frequent lake mixing, while the thermal gradient suggests the opposite. It is possible that the lake mixes on shorter timescales than the heating of the lake by the sun, but there is no evidence to support this postulation. Furthermore, ~20 ºC surface waters mixing with ~4 ºC bottom waters would likely raise the temperature of the hypolimnion, although the large volume of the hypolimnion relative to the epilimnion might counteract the observation of such mixing effects. The hot springs might, additionally, present another source of heat to bottom waters. This contradiction presents one of the principal remaining questions about PL and suggests the need for developing a thermal balance model of the system. Nevertheless, all models hereafter assume that the lake is dimictic in behavior.

Data this year reinforces current hypotheses that the lake is in chemical steady state. Depth profiles for major cations were predominantly constant with depth between 2011 and 2017, and total yearly average concentrations could be analyzed comparatively to discern possible changes in lake composition over time (Figure 40).

Cation concentrations in PL’s waters seem to be in steady state between 2011 and

2017, with a slight overall mean increase of 1.5 ppm over seven years. Not only have dissolved major elements remained unchanged over the last few sample years, but these trends also adhere to measurements taken in 1964 and 1983 (Phillips & Van den

Burgh, 1968; Sammel & Craig, 1983; Lefkowitz et al., 2016). A lack of steady state behavior might be a function of changing hydrothermal input fluxes, upholding the notion that volcanic lakes are surface expressions of geothermal systems. Shifts from steady state could also be a reflection of changing physical properties of the system 61

(Varekamp, 2015); if the flow rate of PCR changed significantly, for example, the system might evolve over time into a new steady state.

Time vs. Mean Concentrations of Major Cations in Paulina Lake 60

50 Na 40 Mg Ca 30 Si 20 K 10

MeanConcentration (ppm) 0 2011 2012 2013 2014 2015 2016 2017 2018 Year

Figure 40: Mean concentrations of major cations (ppm) in Paulina Lake waters between 2011 and 2017. Paulina Lake’s waters are effectively in steady state over time, with a slight mean increase of 1.5 ppm. Concentrations of dissolved solids in PL are strikingly high, considering the dilution effects of meteoric water and the lake’s sheer size. The volcanic stratigraphy and fracture systems within the caldera lend themselves to complex lateral flow of waters, which might influence the disparate types of inputs into East Lake and PL

(MacLeod and Sammel, 1982). Paulina Lake, as usual, shows a lack of sulfate in its waters. Presumably, the sulfur enters East Lake with CO2 as H2S gas and oxidizes into sulfate once in the oxygenated waters (Lefkowitz et al., 2016).

Much of the highly concentrated, hydrothermally-derived Na+, K+, Ca2+, and

Si4+ remain in the waters and flow out through PCR. The residuals fall to the

2+ 2+ 3- sediment with other hydrothermally-derived species such as Fe , Mn , PO4 , and arsenite or arsenate. When using mass balance to calculate hydrothermal inputs of any of the former species, both sediment and river outflux sinks must be taken into account. The latter species largely do not remain in the water column. The hydrothermal fluids in which they enter the lake are highly reducing; once ions such as Fe2+, Mn2+, and arsenic species come into the contact with PL’s well-oxygenated waters, they oxidize and may precipitate into mineral form. For these species, sediment sinks are sufficient for calculating their contents in the hydrothermal fluids.

4.1.3. H2O Stable Isotopes and Water Balance

The hydrological cycles associated with PL must be understood in order to conceptualize its chemical cycles. The PL water budget is comprised of precipitation

(both direct and from the lake’s limited watershed) and hydrothermal fluids as inputs, and evaporation and PCR outflow as outputs (Lefkowitz, 2011; Lefkowitz et al.,

2016; Brumberg, 2016; Capece, 2015). The elevated hot springs on the periphery of the lake do not seem to contribute significantly to the water budget (Ingebritsen et al.,

2014; Forcella 1982). Isotopic water balance models constructed by both Sammel &

Craig (1983) and Lefkowitz et al. (2016) agreed that PL is in water steady state, but only after closure by a larger hydrothermal input (Lefkowitz et al., 2016) than

Sammel and Craig (1983) had envisioned. The original model suggested that

hydrothermal sources contribute 3x106 m3 annually, which is still only 1% of the entire lake volume (Lefkowitz, 2012) and at 12 Mm3/a around 4%.

The 2011-2017 stable isotope concentrations of PL and its hot springs were plotted alongside the local meteoric water line (LMWL) for Oregon (Figure 41). The

LMWL was constructed from various Oregon river waters, using stable isotope data from Kendall & Coplen (2001). Paulina Lake water samples plot on a less steep evaporation line than the LMWL (since lighter molecules evaporate preferentially), and waters from the subaerial hot springs plot close to the local meteoric endmember.

These findings support previous hypotheses that the hydrothermal waters are shallowly recycled rainwater, not magmatic or of deeper origin (Macleod and

Sammel, 1982). Because of the isotopic similarity between PL hot springs and meteoric waters, water isotopic mass balance equations cannot readily be used to determine the strength of geothermal input (Varekamp and Kreulen, 2000).

δ18O (‰, VSMOW) -50 -15 -13 -11 -9

-60 Oregon Rivers LMWL 2011 y = 8.19x + 10.8 -70 R² = 0.99 2011 HS

2012

-80 2014

2015

‰, VSMOW) ‰, -90

HS 2015 D D (

δ 2016 -100 Paulina Lake y = 4.96x - 36.7 2017 R² = 0.88 2017 HS -110

-120 Local Meteoric Water Line Figure 41: Stable isotope diagram with Paulina Lake samples from 2011-2017 (Lefkowitz 2011; Lefkowitz et al., 2016; Capece, 2015; Brumberg, 2016). Oregon river values are from the literature (Kendall and Coplen, 2001). Triangles represent water depth profile data from Paulina Lake, and crosses represent data from the subaerial hot springs. The linear regression through Oregon River data points represents the Local Meteoric Water Line, while Paulina Lake data plot on an evaporation line.

The PL water line intersects with the LMWL endmember at δD ≅ -108‰ and

δ18O ≅ -14‰. Most of the 2017 data plot in a cluster with 2012-2015 data, but three points lay higher up on the evaporation line (i.e. were more isotopically evolved) near

2011 water data. 2011 was a particularly dry year, so PL would have experienced more evaporation and, subsequently, have heavier waters.

The surface values were the most isotopically evolved for both 2011 and

2017, indicating an evaporation phenomenon from the surface of the lake. Water samples for both 2011 and 2017 were taken in August, when the climate was drier and the lake was more stratified. Therefore, more evaporation would be expected to occur. Waters were always much more uniform in isotopic composition in June, which, relative to August, is both when less evaporation occurs and sooner after a mixing event. Mixing phenomena in 2011 appeared to have been more complex.

18 δ OH2O Depth Profiles 2011-2017 δ18O (‰, VSMOW) -11.6 -11.1 -10.6 -10.1 -9.6 0

10 2017 W1 (late August) 20 2017 W2 (late August) 30 2016 (June)

40 2015 (June) Depth (m) Depth (m) 50 2014 (June)

60 2011 (late August) 70

18 Figure 42: Paulina Lake 2011-2017 depth profiles of δ O-H2O (‰) relative to VSMOW. 2011 and 2017 samples were collected in late August, and 2014-2016 samples were collected in June.

4.2. Sediment Chemistry

The unusual composition of the PLsediment strongly suggests a strong contribution from the subaqueous hot springs. Paulina Lake has no river inputs, and soils are poorly developed on the ash-rich mountaintop, making aeolian soil import unimportant as well. Earlier workers (Lefkowitz et al., 2015) suggested, therefore, that the main contributions to the sediment stem from biologic activity (e.g. photosynthesis, calcite precipitation) and from the hydrothermal fluids that enter the lake. The hydrothermal fluids that enter the lake are highly reducing; once ions such as Fe2+ and Mn2+come into contact with PL’s well-oxygenated waters, they oxidize and precipitate into mineral form. Extensive sediment analyses can, therefore, provide vital information about underlying geothermal processes, both in time and space.

Furthermore, determining compositions of sediment core chronologies can facilitate the calculation of chemical concentrations in the hydrothermal fluids, both today and over history.

4.2.1. Raw sediment trends

Sediment core CMPCPL1 is particularly rich in iron (up to 22% Fe2O3), silica

(up to 29%), manganese (up to 5700 ppm), phosphorus (up to 2% P2O5), and arsenic

(up to 250 ppm). The average concentration of Corg in Paulina Lake sediments is relatively low, at around 4% (Lefkowitz et al., 2016). Strong Fe, P and As enrichments are found in the core between 207 and 251 cm. These elements are typical tracers for hydrothermal inputs and suggest the presence of a hydrothermal 67

pulse between 207 and 251 cm. Examples of such elements are Fe, P, As, V, Ni, and

Co. Figure 43 shows this phenomenon reflected in arsenic and iron depth profiles.

The figure also shows a drop in these elements in the region presumed to be an ash layer from the Newberry 720 A.D. eruption.

HYDROTHERMAL A HIGH 20 s h

15 l HYDROTHERMAL LOW a y 10 e r

5 Concentration (% or ppm) ppm) Concentration (% or Fe (%)

0 0 50 100 150 200 250 Depth (cm)

Figure 43: Depth profiles for iron and arsenic (ash-free) in CMPPLC1, which indicate a possible hydrothermal pulse between 207 and 251 cm. Arsenic concentrations were divided by 10 for scaling purposes.

Of course, these observed peaks might have little to do with changes in the hydrothermal release rate. Perhaps diatom productivity was relatively diminished during the time period in which hydrothermal highs were observed; if this were true, the signal of other elements would be enhanced. Similarly, more diatom productivity towards the present would create a dilution effect for other elements. Figure 55 shows 68

that relative silica concentrations decreased at the time of the presumed hydrothermal pulse; however, it is unknown whether hydrothermal silica, biogenic silica, or both are driving this decline. In future research, the amount of biogenic silica in the sediment should be measured. Then, biogenic and hydrothermally-derived silica curves can be isolated from one another, giving light to the possible mechanisms informing observed trends.

Elements such as Yttrium, Zirconium, Aluminum, and Barium showed strong enrichments in the core region between 117 and 141 cm. These elements are most likely derived from the rhyolitic volcanic ash.

4.2.2. Identity of the Ash Layer and Core Age Model

Beyond the possibility of these trends being influenced by enrichment effects from changes in diatom productivity, trends observed might also be influenced by the presence of ash in the core. For example, some of the elements found in the hydrothermal component might also exist in the ash. Background levels of ash were found throughout the core’s chronology, thus diluting the samples and limiting the ability to quantify contributions of hydrothermal fluids to the sediment composition over time. These signals must, therefore, be parsed out. To do so, it is important to know the composition and origin of the ash found in sediment core CMPPLC1 so that reliable analyses of the sediment can be conducted. Because there is no clay in PL sediments, once the data is normalized to remove ash contents, chemical trends found in the sediments can be wholly traced to hydrothermal activity.

Further ash analyses can also be useful from an analytical chemistry standpoint. If the 720 A.D. Newberry Volcano eruption event is identified in the sediment core, such a finding can provide a starting point for developing an initial age model. Bulk chemical analyses affirmed the presence of ash from the Newberry

Volcano 720 A.D. eruption between 117 and 141 cm, with relatively strong confidence. Once the ash was identified as 1298 years old, a linear sedimentation rate of .076 cm/year was interpolated based on the distance to the top of the core.

Ash in the CMPPLC1 core was first identified via depth profiles of elements such as aluminum, barium, yttrium, and zirconium, which are expected to exist in high levels in the rhyolitic ash (Kuehn, 2002) and at lower concentrations in the remainder of PL sediment. Preliminary depth profiles of these key elements indicated a well-defined depositional layer of volcanic ash between 117 and 141 cm, with a consistent peak at 121 cm. In this region, concentrations of the selected elements were 5 to 7 times greater than baseline concentration levels observed throughout the rest of the core’s chronology (Figure 36).

At around 141 cm, the steep slopes depicted in these graphs all illustrated sudden post-eruption deposition of primary pyroclastic material. Due to higher sampling resolution of elements yttrium and barium, their depth plots most adequately show the gradual tailoring off of ash above 117 cm depth, which might indicate less abrupt post-eruption deposition through the watershed. While the concentration of these elements between 114 and 141 cm was markedly higher than in the rest of the core, there was sub-variation in this ash layer. This inconsistency exists because the samples within this region were not comprised of pure ash, 70

possibly due to sediment slumping within the core. Furthermore, the 2-cm thick samples were not pulverized in their entirety, but, rather, as smaller samples. This form of sampling bias could account for arbitrary disparities in ash contents between ash-rich samples.

Analyses of rare earth elements (REEs) both connected PL ashes to Newberry ashes and illustrated that all REEs found in the core are attributed to ash contributions

(Figure 44). Samples from the CMPCPL1 ash layer were plotted in a chondrite- normalized REE plot alongside samples from other regions of the core, East Lake samples, and pure Newberry pumice (from Kuehn, 2001). Notably, samples from the

PL and East Lake ash layers were almost the same in concentration for all REEs.

Furthermore, they plotted just below and parallel to Newberry pumice’s shallow slope line, with the same relative depletion in Europium. While relative REE patterns are consistent between the lake ashes (PL121, PL137, and EL ash layer) and Newberry pumice, the lake ashes were slightly depleted in REEs with respect to the pumice.

This discrepancy possibly occurred because the samples do not consist of pure ash and, therefore, have slightly diluted levels of these tracers.

The graph also shows that the PL core is generally poorer in ash than the East

Lake cores, which may be due to a dilution effect from hydrothermal precipitates in

PL or higher primary productivity with biogenic silica accretion. The latter is in disagreement with the higher Corg levels in East Lake sediment. Nevertheless, the consistent slopes observed throughout Figure 44 suggest the conclusion that all REEs

found in the core come from ash, and, moreover, draw similarities between the lake ash and the 720 A.D. Newberry eruption products.

Rare Earth Elements EL and PL Sediments 100 NB pumice PL121 (ash-rich) PL ash layer PL137 (ash) EL1

10 EL3

EL2

EL4

PL105 logConcentration (ppm) PL95

1 PL163 La Ce Pr Nd PmSm Eu Gd Tb Dy Ho Er TmYb Lu PL155 Rare Earth Elements Figure 44: Comparative chart of rare earth elements concentrations in Paulina Lake core (CMPCPL1), East Lake core (CMPCEL1), and Newberry pumice sample (Kuehn, 2002). Samples were selected from the ash layers in each core, as well as from a distribution of depths above and below the ash layers. Although this graph only illustrates a handful of samples for visual clarity, these patterns are observed throughout each core.

While the presence of volcanic ash in the core was almost immediately verifiable, the lack of pure ash samples made it more difficult to determine that the observed ash was specifically from the Newberry Volcano 720 A.D. eruption.

In order to assess whether the ash found in the core was derived from the 720 A.D.

Newberry eruption, it was compared directly to Newberry ash data from Kuehn

(2002) (Table 4). Sample PL 121 (120-122 cm) was used for comparison because it is ostensibly the most ash-rich sample out of those analyzed. While PL 121 consisted of the most ash-rich sediments, however, it was a homogenized sample and did not 72

consist of pure ash. Sediment chemistry data from PL 121, therefore, does not quite represent the composition of pure core ash.

SiO2 Al2O3 TiO2 FeO MnO K2O Na2O % % % % % % % Kuehn Ash 72.95 14.3 0.23 2.02 0.06 4.07 5.3 PL 121 62.04 11.15 0.23 4.18 0.24 3.25 8 Error Fract. 0.15 0.22 0 -1.07 -2.95 0.2 -0.51

P2O5 Ni Cr Sc V Ba Rb % ppm ppm ppm ppm ppm ppm Kuehn Ash 0.03 6 0 9.4 5 857.61 114.1 PL 121 0.11 0.0054 0 6 18 660 89.7 Error Fract. -2.67 1 0 0.36 -2.6 0.23 0.21

Sr Zr Y Nb Ga Cu Zn ppm ppm ppm ppm ppm ppm ppm Kuehn Ash 53.83 335.11 44.58 21.77 18.11 9.44 51.11 PL 121 62 266 32.9 15 17 13 46 Error Fract. -0.15 0.21 0.26 0.31 0.06 -0.38 0.1

Pb La Ce Th Pr Nd Sm Eu ppm ppm ppm ppm ppm ppm ppm ppm Kuehn Ash 13.62 32.6 63.65 11.89 6.89 26.63 6.44 0.9 PL 121 13 25.5 49.7 8.6 5.84 23.2 4.8 0.72 Error Fract. 0.05 0.22 0.22 0.28 0.15 0.13 0.25 0.2

Gd Tb Dy Ho Er Tm Yb Lu ppm ppm ppm ppm ppm ppm ppm ppm Kuehn Ash 6.31 1.17 7.42 1.59 4.64 0.74 4.86 0.78 PL 121 4.71 0.82 5.63 1.13 3.56 0.59 3.9 0.59 Error Fract. 0.25 0.3 0.24 0.29 0.23 0.2 0.2 0.24

Th Hf Ta U Cs ppm ppm ppm ppm ppm Kuehn Ash 11.5 9.01 1.58 3.93 4.52 PL 121 8.6 7 1 3.2 3.9 Error Fract. 0.25 0.22 0.37 0.19 0.14 Table 4: Elemental concentrations in average Kuehn ash (2002), PL 121 (120-122 cm), and the fractional error between them. Kuehn ash data refers to the 720 A.D. Newberry Volcano eruption.

Comparisons between PL 121 and the Kuehn data revealed that iron oxide, manganese oxide, P2O5, nickel, and vanadium all had significant error fractions (less than or equal to -1). Other chemicals, such as sodium oxide, scandium, niobium, copper, and tantalum had fractional errors of greater than 0.30. The average error fraction was .43.

While the fractional error between the two ashes was persistent, these errors had constant offsets. The linear regression between the two data sets has an r2 value of 0.998 and a slope of 0.772 (Figure 45). These findings suggest that the core ashes mixed into the sediment could be derived from the Newberry 720 A.D. eruption.

700

600

500 y = 0.7721x + 1.5264 R² = 0.9984 400

300

200

100121 Concentrations PL

0 0 200 400 600 800 1000 Average 720 A.D. Ash Concentrations Figure 45: Linear regression of average 720 A.D. ash concentrations by element (Kuehn, 2002) versus elemental concentrations in PL 121, the most ash-rich sample in CMPPLC1.

In order to compare PL ash directly to the Kuehn ash, it was first necessary to determine the composition of pure ash in CMPCPL1 samples. Then, each element in the PL ash could be compared one-to-one with concentrations observed in the Kuehn ash. A binary mixing model, used to determine the percentage of ash in each sample, facilitated these analyses (Equation 2):

X푡표푡푎푙 = (a × X푎) + (b × X푏) , Xtotal = composition of the whole sample a = weight mixing fraction of ash Xa = composition of ash (avg. of Newberry ashes analyzed by Kuehn, 2002) b = weight mixing fraction of hydrothermal and biogenic silica component Xb = composition of hydrothermal and silica components

The silica and hydrothermal components can be treated together here for elements that are not silica or germanium. Silica exists in both the biogenic silica and hydrothermal components, which complicates the use of a binary mixing model for this element. The same is assumed for germanium, because it exhibits similar chemical behavior to silica (Jolly, 1966) and substitutes for Si in a number of silicate minerals (Goldschmidt, 1958).

To isolate the weight-mixing fraction of ash in each sample, elements assumed to have high concentrations in ash and little to no presence in the hydrothermal component are used. Therefore:

X푡표푡푎푙 = (a × 푥푎푠ℎ)

푋푡표푡푎푙 a = 푋푎푠ℎ (Equation 3):

Ash-mixing fractions were calculated from elements that fit these criteria.

Aluminum and zirconium are expected to exist minimally in the hydrothermal component, since they are chemically immobile in the neutral waters. Elements such as lanthanum, rubidium, barium, and yttrium are also rich in the ash component and poor in the other sediments; if they exist in the hydrothermal component, it is likely in negligible amounts. Since PL sediment is presumably clay-poor due to a lack of riverine inputs, the only opportunity for the travel of these elements is through sorption by organic matter or iron oxides; this is uncharacteristic behavior for these elements.

Ideally, all of these immobile elements should yield similar mixing fractions to each other at each depth if they occur only in the ash. If this holds true, ash-mixing fractions gleaned from this mixing model are reliable. Figures 46 and 47 show predominantly overlapping plots for all selected elements except barium, which plots higher than the other elements. Barium is, therefore, likely present in the hydrothermal fluids as well as the ash, which is not unsurprising, since barite veins are common in hydrothermal domains (Edwards and Atkison, 1986). PL121, the most ash-rich layer, consists of 77.8% ash, meaning that 22.2% of the sample is lake sediment. Notably, the offset quantified by the linear regression between PL 121 and

Kuehn ash was also 78%.

0.8

0.7 Al Rb 0.6 Ba 0.5 La 0.4 Zr 0.3 Y

0.2 Volc. Ash Ash MixingFraction Volc. 0.1

0.0 15 65 115 165 215 265 Depth (cm)

Figure 46: Mixing fractions of volcanic ash for aluminum, rubidium, barium, lanthanum, zirconium, and yttrium in core CMPPLC1. 77

Throughout the rest of the core, ash contents remained at a baseline level of less than

5%. Any ash observed in the background may also include that deposited via aeolian inputs from surrounding pumice and cinder fields (Lefkowitz et al., 2017).

Background levels of ash before the eruption might be rhyolitic ashes from past

Mount Mazama eruptions, transported to the caldera via aeolian deposition.

.20 .18 Al .16 Rb .14 Ba .12 La .10 Zr Y .08 .06

.04 Volc. Ash Ash MixingFraction Volc. .02 .00 15 65 115 165 215 265 Depth (cm) Figure 47: Zoomed version of Figure 46, better depicting the relative enrichment of barium compared to other elements.

In order to compare PL ash composition directly to that analyzed by Kuehn

(2002), samples were normalized to the mixing fraction of aluminum or zirconium, which were chosen because they are abundant, immobile, and not expected to occur in the hydrothermal component. Upon isolating the ash composition, the ratios between components of PL ash and the Newberry ash were calculated. 78

The REEs and trace elements made a strong case that the PL ash came from the Newberry Volcano 720 A.D., eruption. The concentration of each REE in the

Paulina Lake ash was found in the same concentration as in the Kuehn (2002) ash within a 10% margin of error (Figures 48 and 49). For trace elements, most were found within a 10% margin of error, while lead and zinc had margins of errors of less than 20%. The tantalum ratio verified a 21% error. The concentrations of tantalum are small (0.8-1.6 ppm), however, so small discrepancies would cause large percent errors.

Paulina Lake Ash Layer (normalized to Zr)

Compared to NB 720 A.D. Ash Composition cm cm

- 1

Eu Tb Lu

ash layer/concentration 720 A.D. ash) A.D. 720 layer/concentrationash

Nd Ho Yb

Tm Ratio (concentration normalized 121 normalized (concentrationRatio Rare Earth Elements

Figure 48: Rare earth element comparison between the composition of Paulina Lake CMPCPL1 ash layer (normalized to zirconium) and Newberry 720 A.D. ash (Kuehn, 2002).

Paulina Lake Ash Layer (normalized to Zr) Compared to NB 720 A.D. Ash Composition

Rb Trace Elements Trace Cs

Zn 0 1 Ratio (normalized 121-cm ash layer/720 A.D. ash) Figure 49: Non-enriched trace elements in Paulina Lake CMPCPL1 ash layer (normalized to zirconium) ratio with respect to Newberry 720 A.D. ash (Kuehn, 2002).

Many of the enriched trace elements (Figure 51) may have entered the system through the hydrothermal component. Nickel, by far the most enriched with a ratio of

11.34, is loosely correlated with the presence of iron in the sediment (r2=0.42). Iron enters the system via hydrothermal input (Lefkowitz, 2011), so this correlation might suggest that they enter the system together hydrothermally (Figure 50).

300

250

200 y = 11.386x + 8.6876 R² = 0.42385 150

Ni (ppm) Ni 100

0 0 5 10 15 20 Fe (%)

Figure 50: Iron concentrations (%) versus Ni concentrations (ppm) in Paulina Lake sediment samples.

Paulina Lake Ash Layer (normalized to Zr) Compared to NB 720 A.D. Ash Composition 11

10 cm ash cm - 9 8 7 6 5 4 3 layer/720 A.D. ash) A.D. layer/720 2 1 Ratio (normalized Ratio121(normalized 0 Ni V Cu Sr Sc Enriched Trace Elements Figure 51: Enriched trace elements (>20% error) in CMPCPL1 ash layer (normalized to zirconium) ratio with respect to Newberry 720 A.D. ash (Kuehn, 2002). 81

According to these analyses, it is likely that the ash layer in the Paulina Lake

CMPCPL1 core is attributed to the 720 A. D. Newberry Volcano eruption. The comparisons with major elements at first seem to complicate such a conclusion.

While K, Si, and Ti show a one-to-one relationship between the two ash samples, Ca,

Fe, P, Mg, and Mn are all enriched for PL. Because no clay has been observed in PL sediments and there are no riverine inputs, however, it is assumed that these elements are hydrothermally sourced. Sediment core analyses, as explicated later in this section, affirm this hypothesis. The other apparent question that arises from these comparisons is how calcium and magnesium ended up enriched in the sediments, which can be explained by the precipitation of Ca-Mg carbonates.

Paulina Lake Ash Layer (normalized to Al) vs. Newberry 720 A.D. Ash Al K Si Ti Ca Fe

Major CationsMajor P Mg Mn 0 1 2 3 4 5 6 Ratio (normalized 121-cm ash layer/720 A.D. ash) Figure 52: Major cations in Paulina Lake ash layer (normalized to aluminum) with respect to Newberry 720 A.D. Ash (Kuehn, 2002).

Once the ash layer was identified as 720 A.D., an age model was built accordingly. The model needed adjustment at the periphery of the ash layer where sedimentation was greater, which was calculated based off the relative deposition rates of Yttrium and ash mixing proportions. Then, an average linear sedimentation rate of 0.8 mm/year was interpolated from there to the top of the core. The same linear sedimentation rate was applied from the ash layer to the bottom of the core, giving the oldest sample a rough age of ~2880 years old. The few 210Pb ages of

Lefkowitz et al. (2015) gave an approximate sedimentation age of 1.5-2.0 mm/year for the top ten cm, which needs further work to be more precise.

This single age model is limited in that it assumes constant accretion throughout the core’s extent. If there was indeed greater hydrothermal activity before the 720 A.D. eruption, hypothetically, higher accretion rates would be required.

Dating pollen and pine needles found in the core would be one way of better calibrating the age model in the future.

All data beyond this point is presented on an ash-free basis.

4.3. Ash-Free Sediment Mass Accumulation Rates

Once a linear sedimentation rate was derived from the age model, mass accumulation rates were acquired using bulk dry density (BDD) data (Figure 53). Mass accumulation rates (MARs) could not be obtained for ash layer samples, since a linear sedimentation rate cannot be determined for an instantaneous sediment deposition event. Data showed that mass accumulation rate increased with depth. The bulk dry density might have increased with depth due to changes in composition, a sediment compaction effect, or both. Compaction leads to higher BDD – since the age scale is linear, any changes in MAR must be the result of changing BDD. The volume fraction of water should, in theory, be smaller with compaction. To probe this possibility, compressed pellets were made of Fe-rich sediment to trace changes of Fe and BDD throughout the core; however, the pellets didn’t have significant enough variation in BDD for any conclusions to be drawn. There was no correlation between iron contents and average sediment density. BDD calculations were based upon the average sediment density of these pellets, which was 1.52 g cm-3.

0.02

)

yr 1 - 0.015

0.01

0.005

Mass Accumulation Rate (g cm (g Rate Accumulation Mass Ash layer 0 0 50 100 150 200 250 Depth (cm)

Figure 53: Mass accumulation rates (g cm-2 yr-1) of sediment samples in core CMPPLC1 with depth (cm). Mass accumulation rates could not be obtained for ash layer samples, since a linear sedimentation rate cannot be determined for a discrete sediment deposition event.

Figure 54 shows ash-free mass accumulation rate trends for elements enriched in the sediment. Silica and iron comprise a majority of what’s accumulating in the sediment, as was expected based off of bulk sediment analyses. Therefore, these elements are probably highly concentrated in the hydrothermal fluid. Most element trends show higher concentrations in the area of presumed elevated hydrothermal activity, except for vanadium, which decreases in concentration. Magnesium and calcium are remarkably constant with depth. Mineralogical analyses (see next section) show that carbonates in the sediment are magnesium-rich. If conditions are not changing greatly in the lake, these carbonate components could be precipitating and sinking to the bottom of the lake at a relatively constant rate over time. 85

350

300

P*40 250 Si Fe*2 Ni 200 As V 150 Ba Mg

100 Ca

50 Mass Accumulation Accumulation Rate (tonnes/year) Mass

0 15 65 115 165 215 265 Depth (cm) Figure 54: Element mass accumulation rates of enriched elements for the whole of Paulina Lake (tonnes/year).

4.4. Sediment Mineralogy

Lefkowitz et al. (2016) provided a schematic for how hydrothermally-derived chemicals might cycle through PL and precipitate into minerals. They suggested that 86

the iron might arrive as Fe2+ from the hydrothermal reservoir and is, then, largely sequestered as iron oxides and/or hydroxides in the sediment upon mixing with oxic lake water. Furthermore, the hydrothermal silica might be sequestered mostly as diatom frustules, although a separate hydrothermal silica precipitate is also likely, especially around spots of hot spring emanation.

The formation of the mineral vivianite is probably related to these processes.

Their textures strongly point to a diagenetic origin of vivianite, and the mineral would not be a primary precipitate from the mixture of hydrothermal fluids and lake waters.

If the Fe-oxides indeed precipitate from the water, P and As would most likely be adsorbed onto these Fe-rich, poorly crystalline masses. The subsequent reaction of the

Fe-oxides with organic carbon in the sediment would lead to Fe-oxide reduction and release of the adsorbed components. The vivianite could then precipitate in the pore fluids as a result of these Fe2+ and high phosphate concentrations (Rothe et al., 2016).

The host phase for the As is still unknown. Figure 58 provides an illustration and the governing equations for vivianite formation.

Figure 58: Hypothesized schematic for Paulina Lake precipitation reactions and diagenetic formation of vivianite (Lefkowitz et al., 2016).

New bulk sediment XRD analyses of PL sediment were carried out at the

IODP facility in Bremen, FRG on one sample from a short PL core. Opaline silica comprised a majority of PL sediments (92-93%) and the 1-2% plagioclase that was found was likely derived from the ash component. Potassium-feldspar was detected at

9-10%, which may be derived from devitrified obsidian glass, given the abundance of obsidian flows surrounding the lake. Surprisingly, no iron oxides were detected, which may be a reflection of their poor crystallinity. No calcite was detected in the sediments, although ostracod valves are common. About ~1% dolomite was detected, 88

suggesting that the ostracods may have valves made of a magnesium carbonate.

Given the Mg/Ca in the lake waters (Mg/Ca-(wt) = 1.7), it is possible that dolomite- like phases have crystallized from PL; Upin (2016) found evidence for the mineral

Huntite (Mg3Ca(CO3)4) on PL boulder evaporates.

These XRD analyses showed no evidence of vivianite, which may indeed have been characteristic for that specific sample. Blue-green vivianite, on the other hand, were analyzed by XRD at Wesleyan University and showed near-perfect diffraction patterns (Figure 37). Three grains were also analyzed via Scanning

Electron Microscopy with EDAX, which showed pure Fe-phosphates with no detectable arsenic (Figures 38-39).

The SEM sediment images also provided new, unrelated insights: both benthic and planktonic diatoms were observed in the sediment. Because this was a relatively deep-water core (Lefkowitz 2012), benthic diatoms could not have lived there, as it would have been too dark. Re-sedimentation phenomena must have affected the sediment in this 2011 core, as was also suggested by previous 210Pb and 137Cs data

(Lefkowitz, 2012).

4.5. Comparison to Precambrian Banded Iron Formations (BIFs)

Volcanic lake settings can be analogs to ancient earth environments. Paulina Lake sediments might be a modern analog of Banded Iron Formations (BIFs), rock sequences from sedimentary environments that existed during the early history of the earth (~3.8-1.8 Ga). Most Archean BIFs were part of greenstone belts that have been deformed, metamorphosed, and dismembered (Klein, 2005); the idea that PL could be a modern analog to these formations provides a unique opportunity for comparative study. Once ash contents were removed from the PL core data, the extent to which PL sediments resemble Banded Iron Formations (BIFs) could be assessed. The prevailing theory behind the formation of BIFs is that, at a time when the bottom layer of the ocean was fairly reducing, parts of the ocean were contaminated with iron-rich hydrothermal fluids. Oxygen was produced by incipient life in the photic surface ocean, and during mixing events iron oxides may have precipitated out. When hydrothermal fluids mixed with seawater and cooled, amorphous silica was precipitated. Such processes gave rise to alternate layering of iron and silica-rich chert layers (Morris, 1993; Raiswell and Canfield, 1998; Klein, 2005).

The closest connection between PL sediments and BIFs are their bulk chemical compositions (Fe-Si rich), conceding, of course, that some of the PL silica sediment sequence was formed by diatoms that did not exist during the Precambrian era. The PL sediments lack a fine millimeter-scale banding of Fe and Si rich layers typical for BIFs (Trendall, 2002) (Figure 56), which may be related to overall small- scale mixing of the upper sediment column during bi-annual turnovers. The silica and iron accumulation rates in PL show a modest degree of correlation (Spearman’s Rank 90

Correlation test for non-linear regressions provided a correlation value of 0.41). There probably is an autocorrelation at play as well, because the sum of Fe and Si is essentially constant (almost 100% in terms of their oxides) (Figure 55).

Contrasting viewpoints on the genesis of BIFs and their layering invoke that they are the result of deposition from density currents (e.g., Krapez et al. 2013) and suggest that precursor sediments to the BIFs derive directly from hydrothermal muds, not minerals precipitated out of seawater.

Mass Accumulation Rates of Fe and Si in Sediment

300 Fe

250 Si

200

150

100

Mass Accumulation Accumulation Rate (tonnes/year) Mass 50 200 700 1200 1700 2200 2700 Years Before Present

Figure 55: Mass accumulation rates of Fe and Si over Paulina Lake with time (tonnes/year). Non- linear correlation parameter for relationship between Fe and Si MARs provided a coefficient value of 0.41 (indicating a moderate relationship).

The PL sediment appears visually homogenous with depth, and chemical alternations between Fe and Si are possibly observed on at least a decimeter scale.

The lithologies of these coarser chemical layers should be studied to determine whether they constitute separate mesobands, which are defined as coarse alterations of contrasting rock types (Trendall and Blockey, 1970). Then, analogies might be drawn to younger, Early Proterozoic BIFs, which were oolitic iron deposits characteristic of shallower ocean basin settings. These still had their origin in hydrothermal inputs that were, compared to Archaean inputs, either lower in flux or temperature (Klein, 2005) (Figure 57).

Figure 56: Alternating massive and microbanded mesobands from the Archaen Yilgarn Block, Western Australia (Klein, 2005).

Figure 57: Palaeoceanographic models for iron-formation deposition from the Archean to the Middle Proterozoic (Klein, 2005).

Extensive REE studies have elucidated the source of iron and silica as hydrothermal inputs from the deep ocean; REE patterns in BIFs typically express an anomalous Europium enrichment (Klein, 2005). The REE patterns in BIFs reflect the composition of the Precambrian seawater and its hydrothermal injections (Dymek and

Klein, 1988). In contrast, the REEs in PL sediment predominantly stem from the rhyolitic ash component in the sediment (~99%), and REEs are extremely dilute in PL waters. Element ratios found in PL sediments, generally, do not simply reflect the 93

composition of the lake water. Ca/Mg ratios in Paulina Lake waters, for example, differ from those in the sediments.

In summary, these initial data on PL sediment show a strong similarity with

Precambrian Iron Formations, and the PL sediment and BIFs share a hydrothermal origin that is responsible for the Fe-Si enrichments as well as other minor and trace elements.

4.6. The Carbon Cycle in Paulina Lake

Quantifying the contents of hydrothermal inputs into volcanic lakes and monitoring changes with time can help to identify changes in volcanic activity

(Hernández, 2001; Rouwet et al., 2011). Comprehension of the lake’s carbon cycle can be an important tool for tracking such changes and is, furthermore, required in order to ultimately calculate the concentration of carbon in the hydrothermal fluids.

Paulina Lake has various carbon sinks, each of which operates at its own rate and with its own isotopic fractionation processes. Therefore, modeling the cycle of organic and inorganic carbon in the lake is an involved process.

The major inorganic hydrothermal carbon input is in the form of dissolved bicarbonate in the hot springs. The majority of organic matter found in the lake system was produced in-situ, since little debris from the surrounding pine tree forest are found in the lake (Lefkowitz et al., 2016). The lake bottom sediment has ~4% organic matter (Lefkowitz, 2012) with an average δ13C value of -28‰ (Capece,

2015). Past C:N analyses indicate that a majority of PL’s organic matter originates from freshwater algae, and δ13C and δ15N analyses of the bulk sediment indicate the presence of cyanobacterial (Nostoc) and diatom organic remains (Lefkowitz, 2012;

Capece, 2015). The organic matter sinks to the hypolimnion, where bacterially- mediated respirational processes contribute light CO2 to the lake DIC. Thus, CO2 production from biotic respiration as well as carbonate inputs from hydrothermal fluids are sources of DIC in the hypolimnic PL waters.

The photosynthetic flux ultimately leads to the sequestration of carbon in the sediment. A larger carbon outflux from the lake waters occurs through PCR and 95

through CO2 evasion at lake surface. A two-box carbon model was developed that describes quantitatively these fluxes in and out and between the two boxes of PL.

Once the fluxes of all carbon outputs are accounted for, chemical mass balance can be invoked to constrain the subaqueous hydrothermal carbon influx.

An isotopic mass balance equation was developed to map δ13C-DIC composition over time and space, although the empirical DIC isotope data show little evidence for a depth gradient in 13C(DIC) (Figure 23). This section discusses the various analyses conducted in order to constrain these terms, as well as to model seasonal changes of δ13C(DIC) and total carbon in the water over multiple years.

Since this task is complicated by physical disparities and interactions between the epilimnion and hypolimnion, a two-box carbon cycle model was necessary to adequately illustrate temporal changes in PLs overall carbon cycle.

4.6.1. CO2 Evasion Flux

Paulina Lake flux measurements from 2017 indicated an average flux rate of

-2 -1 -2 -1 0.13 moles CO2 m day and ranged between 0.2 and 0.6 moles CO2 m day (see

Section 3.3.1). Two data points taken directly over the hot springs showed higher

-2 -1 fluxes of 0.4 and 0.6 moles CO2 m day , while all other points showed fluxes of less

-2 -1 than 0.27 moles CO2 m day . In 2016, 11 data points taken at PL had a range of

-2 -1 -2 fluxes from 0.04 to 0.35 moles CO2 m day , with an average of 0.17 moles CO2 m day-1. Between the two years, the average flux value did not change significantly.

While 2017 data had greater maximums, no flux measurements were made directly over the hot springs in 2016. 96

With 31 measurements taken over the surface of PL, 2017 data provides better spatial resolution than had previously been attained. Significantly, this year’s field measurements suggest with more certainty that PL has a lower average CO2 gas evasion than East Lake. 2015, 2016, and 2017 data showed that East Lake had

-2 -1 average fluxes of 0.2, 0.3, and 0.2 moles CO2 m day . Paulina Lake has a higher pH than East Lake, so lower PCO2 in the surface waters are expected, although PL also has up to four times more DIC than East Lake. Nevertheless, a lower CO2 degassing flux is expected in PL (Mazot and Bernard, 2015).

The average of 1000 equi-probable stochastic sequential Gaussian simulations showed likely spatial distributions of flux rates based on the existing field data points.

The averaged simulation (Figure 60) affirmed that CO2 flux levels are likely elevated over the presumed hot springs, and that the flux in the rest of the lake tends to bemuch lower. Areas surrounding the hot springs had fluxes that are at least twice as high as those in the rest of the lake.

Figure 60: Map of SGS-generated fluxes in moles m-2 day-1 based on field flux measurements over Paulina Lake from 2017. Average of 1000 simulations.

A sample size of nine declustered simulations was randomly chosen as a representation of how well the simulations agreed with both one another and the final averaged product (Figure 61). The declustered simulations as well as the averaged simulation appeared similar, with high CO2 fluxes illustrated near the hot springs and

some less active degassing indicated on the western part of the PL.

Figure 61: Nine randomly selected declustered SGS simulations, all depicting similar CO2 flux patterns off the surface of Paulina Lake.

The SGS-generated histogram of simulated CO2 flux points showed a strong leftward skew, reaffirming what is already shown in the predominately blue map

(Figure 62). It must be noted, however, that the simulation predicts minimum CO2 flux values in scantly sampled areas. No samples were taken in the NW quadrant of the lake in 2017, and the generated CO2 fluxes in that area lie within the low range of

0.00 to 0.05 moles m-2 day-1. The area of the skew on the histogram, therefore, might be an overestimate.

Because the simulation projects low values for areas with few data points, calculations based off of the SGS-produced data might be minimum estimates. The average contributions of each cell obtained after 1000 SGS simulations suggested an

-2 -1 average flux rate of 0.103 moles CO2 m day . This SGS-generated average was

-2 -1 close to the empirical average value of 0.13 moles CO2 m day . Calculations based off the average flux value and lake surface area indicated that PL evades a daily minimum of 28 tonnes of CO2.

-1 -1 Figure 62: Histogram of SGS-generated CO2 flux points (moles CO2 m day ).

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In order to determine the degree to which the normal scored data was spatially dependent and continuous, a spherical variogram model was applied. The rising slope of the graph showed that, unsurprisingly, there was lower variance between points that were more proximal. The regression approached a horizontal asymptote at a distance of 1.2 kilometers, meaning that there was no correlation between points within 1.2 kilometers of each other.

Figure 63: SGS-generated theoretical and experimental variogram functions. Distance is measured in kilometers.

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The regression does not pass through the origin, indicating that reproduction of spatial characteristics is imperfect. If two points are overlapping, the covariance should ideally equal zero. Regardless, the magnitude of the discontinuous jump, often called the nugget, is relatively insignificant in this case. Spatial trends in CO2 fluxes can provide evidence of tectonic faults underneath volcanic lakes, and no such trends were seen (Mazot and Bernard, 2015), although data density may be too limited for such observations.

13 4.6.2. δ C of DIC and Aqueous CO2

The 2017 δ13C-DIC trends with depth deviate from those observed before in

PL. There is typically a lack of 13C(DIC) gradient with depth in PL, resulting either from more thorough lake mixing, less biological productivity, or less CO2 degassing than its East Lake counterpart. On the other hand, the much larger DIC inventory buffers the 13C(DIC) to losses of light carbon such as photosynthesis. The 2017 DIC observed in the surface waters was isotopically relatively heavy, establishing a

δ13C(DIC) gradient (Figure 23). At a value of +3.1‰, the δ13C(DIC) values in surface waters were ~3‰ higher than before. Between the two years in which CO2 flux analyses of Paulina Lake occurred, average flux rates remained almost the same, indicating that more CO2 degassing in 2017 is not likely a significant impetus for this change.

An algal bloom in PL coincided with the 2017 field sampling campaign, an event that might provide a better explanation for this isotopic change. Dissolved 102

oxygen in the thermocline was high in 2017, which corroborates the presence of elevated photosynthetic activity. Due to the preferential uptake of light CO2 by these photosynthetic algae, heavier surface waters would be expected (Herczeg, 1987).

Pronounced δ13C-DIC surface gradients are often observed in conjunction with algal bloom events. Algal blooms in the Dead Sea, for example, caused fractionations as large as 8‰ (Oren et al. 1993).

13 13 The predicted δ C of CO2 (aq)was calculated using the bulk δ C-DIC values and the calculated concentrations of each carbonate species from Web-PHREEQ.

These data, along with known temperature-dependent isotopic fractionation offsets

(Clark and Fritz, 1997; Mook, 2001), were incorporated into the isotope mass balance equation (Wanninkhof, 1985):

13 13 13 13 δ CDIC = m1*δ CCO2 (aq) + m2*δ CHCO3- + m3*δ CCO3= (Equation 4), where m1, m2, and m3 are the mole fractions of their corresponding carbonate species.

13 The subsequent water profile for δ C of CO2 (aq) indicates values between ~ -

10 and -7.5‰ with heavier values in the surface waters. This pattern is the result of

13 the  C(DIC)gradient and temperature gradient in PL waters. The CO2 gas evading

13 from the surface in equilibrium with the local waters would have  C(CO2) = ~ -7.5 to -8.0‰.

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13 Figure 64: Depth profile of the predicted δ C of dissolved CO2 gas (‰, VPDB).

13 4.6.3. δ C-CO2 Gas

13 Besides the predicted  C-CO2(aq), estimates for the isotopic composition of

13 the evading CO2 were made. A ‘Keeling’ plot of 1/CO2 versus δ C-CO2 should provide a linear array between two endmembers of ambient air and lake CO2 gas

(Figure 65). The air data show abundant variation, leaving a triangular focal area for the potential lake gas isotopic compositions. By extrapolating a best fit line towards the y-intercept, the theoretical isotopic composition of the pure CO2 gas can be predicted. The bounding lines generated by each endmember project that the δ13C value of the gas lies in the range between ~ -10.3‰ and -9.8‰. A wider range of lake 104

gas concentrations might improve the accuracy of this value, and much heavier gas compositions confirm that the predicted values (~-8‰, see Section 4.6.2.) are also permissible within this data set (dashed orange line, Figure 65).

13 The  C-(CO2) isotope data are different between PL and East Lake (Figure

66), as expected given the different 13C(DIC) values in each lake. It was also noted that ambient air samples are substantially lighter than well-mixed ambient air (~-

8.5‰), suggesting the presence of a lighter additional component. Forest fire smoke was present in the region during sampling partially accounting for anomalies in 2017

(Das et al., 2010). Air samples from earlier years without such extensive forest fires were also unusually light, with isotopic values averaging about -11‰ and excursions near -17‰ (Brumberg, 2016; Capece, 2015). The very light signature may be impacted by the presence of light respiration CO2 from the surrounding forests, suggesting a poorly mixed local airshed.

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1/CO2 (ppm) -8.00 0 0.5 1 1.5 2 2.5 -8.50

-9.00 훿13C Lake CO2

-9.50

(VPDB) (VPDB) 2

-10.00

CO -

C -10.50 13

δ -11.00

-11.50 Chamber Samples -12.00 Ambient Air -12.50

13 Figure 65: 1/CO2 versus δ C-CO2 for linear array of Paulina Lake gas and ambient air 13 endmembers. The y-intercept provides a theoretical value for the δ C-CO2 of the lake gas. The y- intercept of the orange line indicates the isotopic composition of the predicted value, based on an isotope mass balance equation (Wanninkhof, 1985).

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1/CO (ppm) -8.00 2 0 0.5 1 1.5 2 2.5 -8.50

-9.00

-9.50

-10.00 (VPDB) (VPDB)

2 -10.50

CO -11.00 -

C -11.50 Chamber Samples 13

δ Ambient Air -12.00 East Lake -12.50

13 Figure 66: 1/CO2 versus δ C-CO2 plot of East Lake and Paulina Lake accumulation chamber samples, as well as ambient air samples.

4.6.4. Two-Box Carbon Model

Box models are simplified renderings of geochemical cycles intended to show how an environmental system reacts to changes in inputs and outputs. They assume that any given box is a homogenous mixture and are considered to be in steady state if their inputs equal their outputs (Varekamp,1988; Baker, 1994). Creating a box model of PLs carbon cycle helps to better understand the carbon flows on both short and long temporal scales. Of particular interest are how the amount and isotopic composition of carbon in the lake are impacted by seasonal changes, and whether these parameters, despite such changes, approach steady state over yearly timescales.

Because PL becomes stratified in the summertime, a two-box model approach is needed. One box does not account for the different processes impacting the carbon 107

budgets in the surface and bottom waters, nor does it account for the various interactions between the two interfaces. The two boxes here are defined as the epilimnion (surface to ~12m) and the hypolimnion (~12m to the bottom).

Based on the fairly well-substantiated assumption that the carbon in the lake is in chemical mass balance, the total carbon input flux into the lake should equal the

13 total output flux (Varekamp, 1988) The lake evades CO2 (δ C = ~-9‰) at a rate of

13 35 tonnes of carbon per week, and an additional 24 tonnes of carbon (δ Cinitial =

~0‰) leave through the PCR outflow per week. The PCR output was determined using the mean monthly discharge of 0.6 m3 sec-1 and a concentration of 0.08 grams of carbon per liter in lake surface waters. An additional 2.1 tonnes of organic carbon

13 (δ Corg = ~-28‰) are extracted from the epilimnion each week through photosynthetic processes. This value was based on the sediment mass accumulation rate of organic carbon. The total value was obtained after multiplication by the lake’s surface area. The amount of organic carbon in the sediment was doubled based on the assumption that about 50% of organic matter becomes oxidized as it sinks to the lake bottom (Wetzel, 2001).

When chemical mass balance is invoked using these input and output fluxes, the hydrothermal flux of carbon into the lake is ~40 tonnes of carbon per week, which is assumed to be constant year-round. This value was obtained assuming that photosynthesis and CO2 evasion only occur when the lake is not ice covered.

However, we assume that during the summer more water is transferred from the hypolimnion to the epilimnion than in winter, when wind-driven mixing is minimal

108

due to ice cover. The δ13C of hot spring fluids are assumed to be ~3.5‰ based off the measurements of CO2 in the sub-aerial hot springs.

The epilimnion and hypolimnion in the early summer have carbon contents of

~ 5825 and 16310 tonnes C respectively. These calculations were based on the volumes of each box and the average carbon concentration in PL water in each compartment (derived from Web-PHREEQ). The surface water carbon contents were assumed to remain equal. In the summer, 35 tonnes of carbon degas from the surface per week based on SGS-derived mean average flux, while, between November and

May, there is no CO2 evasion due to ice cover. In the winter, the ice cover prevents surface wind-induced mixing, and less carbon is transferred from the hypolimnion to the epilimnion. The outflux of Paulina Creek also decreases during the winter; thus, mass balance is roughly maintained even though degassing and photosynthetic outputs change with the season. The flux from the hypolimnion to the epilimnion was determined simply by mass balance, assuming some form of mixing in the thermocline zone. The subsequent fluxes from hypolimnion into the epilimnion were

61 and 24 tonnes carbon per week for the summer and winter respectively. The hot springs input is not impacted by seasonal changes, so an average input of 40 tonnes per week was applied throughout the whole year. Once all the fluxes were accounted for, the isotopic mass balance model was run concurrently in the epilimnion and hypolimnion to track total carbon and δ13C-DIC values on weekly timesteps for five years. The lake was homogenized every May and November.

These were the conditions by which modelled lake DIC values broadly agreed with those observed in the field. As with most models, this model rests on certain 109

assumptions and has its limitations. For example, it assumes that total carbon is evenly distributed within the two boxes. In a lake that is non-uniform both vertically and horizontally in depth-topography, light reception, tectonic structure, thermal budget, among other discrepancies, this assumption inevitably simplifies a highly dynamic system. Furthermore, the model does not denote a metalimnion, the key part of the lake where mixing processes occur. In order to incorporate a third box for the metalimnion, a greater understanding of the lake’s thermal balance and mixing regimes are needed, as has been discussed. The lack of chemical gradients denotes full-scale mixing, while the thermal gradient contradicts this prospect. Furthermore, if a metalimnion is incorporated, the disappearance of that metalimnion during the winter must be assimilated into the model. Instead, the lake has been simplified into two boxes throughout the year, between which there is an unknown carbon exchange mechanism through water mixing. In East Lake, the carbon exchange between deep and shallow water results from the rise of CO2 bubbles, but CO2 bubbles are uncommon in PL (Lefkowitz et al., 2016; Brumberg, 2016; Capece, 2015; Lefkowitz,

2012). The model, however, provides a suitable starting point before a more detailed mixing model can be generated. Finally, the averaging of winter and summer mass balance closing terms for the hot springs was one of the other noteworthy liberties taken in this model creation.

Despite these limitations, the model provided some key insights. δ13C-DIC values stayed within the observed range over the years and the lake moves more or less towards isotopic and chemical steady state over the course of the five years

(Figures 67 and 68). In the summer months, the δ13C-DIC values in the epilimnion 110

steadily climb as the light carbon both evades the lake and is sequestered in photosynthates. The model shows that isotopic values rise as high as 0.76‰, which is only slightly higher than the observed δ13C-DIC values in surveys pre-2017. The hypolimnion gets lighter – albeit at a slower rate – from respirational processes and the hydrothermal input of bicarbonate.

Photosynthesis and CO2 degassing, the two processes that involve isotopic fractionation, cease in the winter, and isotopic values in the epilimnion remain relatively constant. PCR export continues, but there is no fractionation involved in this process. Furthermore, carbon received from the hypolimnion will not drastically change the isotopic composition of the epilimnion, because the δ13C-DIC values are both so close to 0.

111

1.5

observed 1 values

0.5

(‰) 0 DIC DIC -0.5- winter winter winter winter C winter summer 13 summer summer summer δ summer summer -1

-1.5 Epilimnion Hypolimnion

-2 0 52 104 156 208 260 312 Time (weeks) Figure 67: Two-box model plot shows seasonal δ13C-DIC changes in the hypolimnion and epilimnion over the course of 5 years. The shaded area denotes the range in which Paulina Lake δ13C-DIC values were measured.

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22600

22400

22200

22000

21800

21600

Total Lake Carbon (metric Lake Carbon (metric tonnes) Total 21400 0 54 108 162 216 270 Time (weeks) Figure 68: Seasonal changes in total carbon (metric tonnes) in Paulina Lake over the course of 5 years, showing a trend towards steady state.

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4.7. Hydrothermal Fluid Geothermometry and Element Concentrations

A first-order mass balance approach was used in order to model the subaqueous hydrothermal input, as conceptualized by Lefkowitz et al. (2016). It was assumed that the export of conservative chemicals through PCR equaled the input from the hot springs. The PCR export rates and sediment sequestration rates for each element were calculated to determine element loss terms, and it was assumed that the lake bottom hot springs had to resupply that same amount to the lake system in order to maintain the steady state conditions that have been observed over the last decade.

For each element, both theoretical hydrothermal fluxes and concentrations were calculated over time. Fluxes of each element were calculated by determining element mass accumulation rates in the sediment (ash-free) and multiplying them by the surface area of the lake. For elements that were exported via PCR, sediment sinks and PCR export rates were added together to determine the total sink. The lack of clay in the sediments suggests that all non-ash contents were hydrothermally-derived, and subsequent fluxes were, therefore, calculated based on the assumption that these sinks had to be resupplied by the hot springs. Because calculations were based on mass accumulation rates, and subsequently bulk dry densities, possible compaction effects and/or dilution effects could affect results (see Section 2 of Discussion). The carbon inputs were discussed in the section above.

These calculations also assume that hydrothermal fluids are entering at the same rate in any given time period, and that, with regard to spatial distribution, they enter the lake and distribute their contents uniformly. Grab samples should be taken for a transect of PL to test the validity of these spatial assumptions. 114

Most of the hydrothermal flux trends (Figure 69) mirrored mass accumulation trends in the sediment column. Many of the element fluxes (e.g. Fe, P, arsenic, As,

Ba) show evidence of increased hydrothermal inputs prior to the time of ash layer deposition. These observations show that, perhaps, there was stimulated hydrothermal flow prior to the eruption. The precursor time in which hydrothermal activity might have been elevated was about 1000 years, which is a very long period for a direct relationship with the eruption at Newberry in 720 A.D. Nevertheless, it shows that there was a possible resetting of the hydrothermal system. Silica shows elevated fluxes pre-eruption, but also experiences a relative decline near the tiem of peak hydrothermal emissions. Manganese shows no clear trends in the sediment core; this element with multiple valences, it may have moved after deposition via diffusion in the sediment column.

From these fluxes, concentrations were calculated based on a hot springs water flux of 1.2 × 1013 grams year-1. This value was based on water steady state and isotopic mass balance as outlined by Lefkowitz et al., 2016. Estimates for the concentration of carbon in the hot springs are derived from the carbon cycle model, indicating that about 2000 tonnes of carbon (mainly bicarbonate) enter PL each year via subaqueous hydrothermal fluids.

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700

600

500 Fe*2 Si P*40 400 As Mn/9 Ba 300 Ca Mg/1.5 200 V Ni

Hydrothermal flux Hydrothermal (tonnes/year) 100

0 15 65 115 165 215 265 Depth (cm)

Figure 69: Subaqueous hydrothermal fluxes (tonnes/year) into Paulina Lake over time. Samples at 263 cm are ~2800 years old based on the single age model. Fluxes of certain elements were normalized for scaling purposes.

Hot spring concentration estimates for the year 1793 A.D. (~17 cm depth in core) are listed in Table 5, and trends with depth are depicted in Figure 70.

Concentration estimates are also listed for the year 596 B.C. (Table 6), which was presumably the time of peak hydrothermal activity. Because the carbon cycle model was based on modern lake conditions, concentration estimates for carbon and HCO3- are omitted. Similarly, potassium and sodium estimates were based solely on modern water data, so these estimates were omitted, too.

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C HCO3- Fe Si P As Mn Ba ppm ppm ppm ppm ppm ppm ppm ppm 347 1764 6 44 0.23 2.2 330 2

Ca Mg Na K V Ni Zn ppm ppm ppm ppm ppm ppm ppm 29 58 74 8 3 6 0.7 Table 5: Element concentrations in subaqueous hot springs in the year 1793 A.D.

Fe Si P As Mn Ba ppm ppm ppm ppm ppm ppm 12 49 0.5 22 509 3

Ca Mg V Ni Zn ppm ppm ppm ppm ppm 29 59 4 19 1 Table 6: Element concentrations in subaqueous hot springs in the year 596 B.C.

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Relative Hot Springs Concentration Patterns

70 Fe*3

60 P*20

As*3 50 Si

40 Mn/6 Ba*10

30 Ca Conentration (ppm) Conentration Mg 20 V

10 Ni

0 30 80 130 180 230 Depth (cm)

Figure 70: Hot spring concentrations (ppm) based on sediment composition with depth. Concentrations of certain elements were normalized for scaling purposes.

For element concentrations in the volcanic fluids, similar patterns to the fluxes are observed. However, based on these analyses, silica expresses a more pronounced concentration dip in concentration during the presumed period of peak hydrothermal activity. The origin of this dip is unclear; because of the constant sum issue, more Fe in the samples means less silica. While the silica in the sediment could be both hydrothermal silicic matter and diatom opal, in the end, both are derived from the hydrothermal silica inputs. 118

The surface lake-water temperature is strongly influenced by surface heat exchange (Pasternack and Varekamp, 1997), so it typically bears little relationship to the temperature of the hydrothermal fluid inputs. The hydrothermal fluid temperature can be approximated using silica thermometry equations (e.g., Verma, 2000). The estimated concentration of SiO2 in the fluids was used (Equation 6):

(Equation 6)

According to this equation, the temperature of the hydrothermal fluids is about 139

ºC. Such hot waters would be stable at the pressures correlating with the mean depth of the lake. The ways in which this heat input would impact the temperature of the hypolimnion over a winter season remains to be determined.

119

5. Conclusions

Paulina Lake’s subaqueous hydrothermal fluids contribute to a dynamic and unique lacustrine system. The organic and inorganic geochemical processes that occur within most common lakes are regulated from the ‘top down,’ (e.g. through riverine inputs, equilibration with the atmosphere, etc.). In PL, on the other hand, the main mechanisms informing its geochemical dynamics are derived from the subaqueous hydrothermal fluid inputs: that is, from the ‘bottom up.’ In this study, the resulting

‘bottom up’ chemical dynamics of PL were analyzed through water, gas, and sediment analyses. Given the reasonably strong evidence for chemical steady state in the lake, the pathways of several groups of elements that have concomitant cycles through the lake system were, subsequently, defined.

The sediments consist of three general components: biogenic (biogenic silica, organic matter, magnesium-carbonates), volcanic (external input of ashes), and chemical precipitates (mainly Fe, some Si, and a host of trace elements). The biogenic and chemical precipitates in particular are closely linked, since most of PL’s ingredients for biological productivity are also derived from the volcanic fluid inputs.

Volcanic constituents that remain within the lake system are either utilized by biota in the photic zone or precipitate in the water column before falling back to the lake bottom sediments (Figure 71). Other hydrothermal components are wholly removed from the PL system; these outfluxes include the PCR outlet and gas evasion into the atmosphere. Figure 71 illustrates the mechanisms by which volcanic subaqueous injections both sink into PL sediments and are removed from the PL system.

120

Gas evasion PCR outflux

↑ More oxic ↓ Less oxic

Biotic uptake and sedimentation

Mineral precipitation

Volcanic inputs

Figure 71: Basic schematic for the interplay between PL’s geothermal and biogeochemical mechanisms. This illustration incorporates all sinks into the sediment as well as outfluxes from the PL system.

121

With the system conceptualized in this manner, four element groups, each with distinguished pathways through the PL system, can be characterized as such:

 Conservative ions (Na, K, Cl, and several tracers such as Rb and Cs): These elements enter the lake through the hydrothermal fluids, reside solely in the water, and are eventually carried off through the PCR outflux. For these essentially conservative elements, PCR outflux equals the hot springs influx. 3- 3=  Hot Springs Carbonate Input (Ca, Mg, Sr, H2CO3, HCO , CO ): After entering the lake, some CO2 is used for organic productivity or carbonate precipitation, while some escapes through the gas evasion flux or through PCR. Ca-Mg-Sr is partially precipitated in the sediment, and the remainder is carried off through the PCR outflux.  Hydrothermal Silica: Hydrothermally-derived silica is either chemically precipitated after mixing and cooling in PL waters, or it is used by the diatoms to make BSi. Through both of these pathways, silica falls into lake bottom sediments, and any remaining constituents in the water leave the system via PCR.  Sediment precipitates (Fe, P, Mn, As, and a host of traces, including Ba and Ni): These elements enter the lake system through the subaqueous hot springs and do not manifest in PL waters. Instead, they are almost immediately precipitated into the sediment. There is some uncertainty about the pathway of P, as it may enter the lake and become incorporated into biogeochemical cycles. However, P levels are not particularly high in the PL water column.

Figure 58 (See: Discussion Section 4) shows how, specifically, some of these hydrothermally-derived chemicals might cycle through PL and precipitate into minerals. Upon injection into PL’s water column, reduced Fe2+ probably becomes sequestered into the sediment as iron oxides and/or hydroxides, once they are mixed with oxic waters. The hydrothermal silica might be sequestered predominantly as diatom frustules, although a separate hydrothermal silica precipitate is also likely, especially around spots of hot spring emanation.

Vivianite was identified in PL sediments with high levels of confidence, and its formation is probably interrelated with these processes. Once precipitated 122

minerals, unto which As and P may be absorbed, return to the more reducing sediments, these sorbed elements might become enriched in the pore waters. Such conditions would be ideal for diagenetic vivianite formation. Within the scope of this study, whether or not vivianite is a host phase for arsenic has yet to be determined.

Once the ash contents were removed from all sediment samples, elemental fluxes and concentrations for the hydrothermal fluids over time were constrained by integrating this schema into first-order mass balance calculations. Ash-free sediments from between 0 and 500 B.C. were enriched in hydrothermal chemical constituents, providing evidence for enhanced hydrothermal inputs into PL during that time.

Succeeding silica geothermometry calculations estimated hot spring temperatures at

~140ºC, which would constitute stable waters at the pressures expected at PL mean depths. It has yet to be determined whether this heat input impacts the temperature of the hypolimnion over the winter season.

Paulina Lake sediments, alternating in Fe and Si enrichments, share many characteristics with Precambrian BIFs. Both the PL sediment and BIFs have a hydrothermal origin that is responsible for the Fe-Si enrichments, as well as the occurrence of other minor and trace elements. While PL sediments have not shown evidence of microbanding layers, future lithologic study should attempt to gauge similarities to Early Proterozoic assemblages, which were oolitic iron deposits characteristic of shallower ocean basin settings. Then, PL sediments could, perhaps, be described as “IFs,” or Iron Formations (Klein, 2005). Whether PL should be associated with IFs or BIFs, Archaean, Early Proterozoic, and modern PL sediment assemblages are all similar in that their Fe-Si patterns were hydrothermally-derived 123

(Klein, 2005). This comparison is salient, then, even if PL sediments don’t reflect

REE patterns and element ratios in PL waters, just as the Precambrian BIF compositions reflected Precambrian seawater.

Figure 72 provides a final visual synopsis of the four different chemical pathways summarized in this section, knowledge of which were attained by the synthesis of sediment, water, and gas data into a lake-wide chemical mass balance schematic. In carrying out this analysis, this study both substantiated the occurrence of these geochemical dynamics and, furthermore, assigned to them theoretical calculations for concentrations and fluxes (Table 5, See: Discussion Section 7). On a longer temporal scale, these quantifications resulted in the possible discovery of enhanced hydrothermal activity within the Newberry system ~2000 years ago. The geochemical modelling techniques implemented herein provide an example of how much can be learned from the study of a small crater lake.

124

Figure 72: Summary of hydrothermal geochemical dynamics in PL.

125

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Additional Data Tables

Table 8: Field data

Sample Depth Field Temp Alkalinity pH (m) (∘C) (mmol/L) CMPPLW1-0 0 18.8 6.8 8.62 CMPPLW1-10 10 13.4 6.8 8.53 CMPPLW1-20 20 5.3 6.8 7.47 CMPPLW1-30 30 4.35 6.82 7.5 CMPPLW1-40 40 4.1 6.78 7.4 CMPPLW1-50 50 4.1 6.86 7.4 CMPPLW1-55 55 4.1 6.86 7.4 CMPPLW2-0 0 18.8 6.6 8.62 CMPPLW2-10 10 13.4 6.74 8.53 CMPPLW2-20 20 5.3 6.96 7.47 CMPPLW2-30 30 4.35 6.84 7.5 CMPPLW2-40 40 4.1 6.86 7.4 CMPPLW2-50 50 4.1 6.89 7.4 CMPPLW2-55 55 4.1 6.89 7.4 Field temperature (∘C), alkalinity (mmol/L), and pH of 2017 Paulina Lake water samples.

Table 9: Stable isotopes in water 2 18 13 Sample Depth δ H-H2O δ O-H2O δ C-DIC (m) (VSMOW, ‰) (VSMOW, ‰) (VPDB, ‰) CMPPLW1-0 0 -89.1 -10.64 0.65 CMPPLW1-10 10 -92.8 -11.47 0.09 CMPPLW1-20 20 -92.4 -11.36 -1.87 CMPPLW1-30 30 -91.1 -11.14 -1.64 CMPPLW1-40 40 -93.6 -11.35 -1.37 CMPPLW1-50 50 -92.2 -11.16 -1.56 CMPPLW1-55 55 -92.1 -11.25 -1.63 CMPPLW2-0 0 -88.2 -10.26 0.42 CMPPLW2-10 10 -91.3 -10.89 0.18 CMPPLW2-20 20 -89.3 -10.65 -0.90 131

CMPPLW2-30 30 -92.9 -11.29 -1.39 CMPPLW2-40 40 -90.5 -11.04 -1.16 CMPPLW2-50 50 -91.8 -11.11 -1.00 CMPPLW2-55 55 -91.3 -11.17 -1.20 PLHS hot springs -105.0 -13.62 -5.00 Stable isotope values for 2017 Paulina Lake water samples, in ‰.

Table 10: Major anions and cations 2- Sample Depth (SO4) Cl- Ca2+ K+ Mg2+ Si4+ Na+ (m) ppm CMPPLW1-0 0 0.73 3.8542 8.5 6.0 44.3 21.3 53.6 CMPPLW1-10 10 0 3.3322 22.5 5.9 42.6 20.5 51.3 CMPPLW1-20 20 0 3.2119 28.6 6.0 42.6 20.8 51.2 CMPPLW1-30 30 0.68 2.8632 28.9 6.1 43.0 21.0 51.8 CMPPLW1-40 40 0.18 0 28.7 6.1 42.7 21.0 51.6 CMPPLW1-50 50 1.07 0 28.9 6.1 43.0 21.2 51.9 CMPPLW1-55 55 0 3.4349 28.9 6.2 43.0 21.2 52.0 CMPPLW2-0 0 3.25 0 17.1 6.5 44.9 21.5 54.6 CMPPLW2-10 10 2.33 3.3896 13.9 6.1 43.1 20.6 52.0 CMPPLW2-20 20 0 3.155 28.9 6.3 43.8 21.2 53.1 CMPPLW2-30 30 2.82 3.5216 28.9 6.1 43.0 20.9 52.0 CMPPLW2-40 40 2.18 3.4889 21.1 6.2 42.7 21.0 52.1 CMPPLW2-50 50 2.60 0 24.6 6.1 42.9 21.1 52.0 CMPPLW2-55 55 0.49 3.7141 18.5 6.1 43.3 21.2 52.1 PLHS hot springs 46.2 16.2 58.9 85.5 137.3 Concentrations of dissolved solids in 2017 Paulina Lake water samples, in ‰.

Table 11: Stable isotopes and concentrations of chamber and ambient CO2 gas samples Sample Lab 13 18 ID Latitude Longitude 휹 CVPDB 휹 OVPDB CO2 Chamber CO2 (decimal (decimal degs., N) degs., W) ‰ ‰ ppmv ppm CMPPL3 43.71467 121.22253 -10.95 -10.50 527 455.81 CMPPL8 43.71636 121.26915 -11.71 -10.35 535 466.33 CMPPL11 43.71459 121.26401 -11.93 -9.40 544 471.32 CMPPL15 43.71288 121.24832 -11.17 -10.07 536 467.01 CMPPL18 43.72311 121.24954 -12.09 -9.73 567 450.33 CMPPL30 43.72982 121.24717 -9.97 -9.96 608 609.05 CMPPL31 43.72988 121.24784 -10.93 -4.48 484 698.76 132

CMPAIR1 43.70409 121.31767 -9.97 -9.94 423 N/A CMPAIR2 43.70581 121.30006 -10.56 -10.23 436 N/A CMPAIR3 43.69955 121.37725 -11.46 -11.39 458 N/A Stable isotopes of CO2 gas samples expressed in ‰, VPDB and respective field concentration measurements, expressed in ppm.

Table 12: CO2 Flux Data