Lake Sediment Records of Late Holocene Proglacial Floods from the San Lorenzo Icefield (Patagonia)

LAKE SEDIMENT RECORDS OF LATE HOLOCENE PROGLACIAL FLOODS FROM THE SAN LORENZO ICEFIELD (PATAGONIA)

Stijn Albers Student number: 01402580

Promotor: Prof. Dr. Sébastien Bertrand

Jury: Dr. Benjamin Amann, Prof. Dr. Alberto Araneda

Master’s dissertation submitted in partial fulfilment of the requirements for the degree of master in geology

Academic year: 2018-2019

Preface

If it wasn’t for the many people that have helped and advised me during the past year, this thesis would not be what it is now. It took a lot of time and effort to create it and I would therefore like to thank all the wonderful human beings who supported me during my research, my lab work, my data processing, my countless hours of reading and writing, and above all, during the fun hours in-between.

First of all, I wish to thank my promoter, Sébastien Bertrand, for introducing me to this subject and to the beautiful part of our planet that is Chilean Patagonia. Your critical advice and suggestions really helped my thesis move forward. You were always willing to help and answer any question I had, for which I am must grateful. I also want to thank my supervisor, Elke Vandekerkhove, for helping me uncountable times with my lab work and for giving feedback on my writing. Your never-ending enthusiasm was really encouraging to me. I am very thankful that you were both always there to give me a hand when I needed it or prepared to engage in some scientific discussing whenever I came to your offices.

Next, I want to thank Seb and Loïc Piret for going to Patagonia and spending their time on a boat on some lakes so that someone could work on awesome sediment cores. I want you to know that I am glad to have been that someone. A special thanks goes out to Loïc, for helping me with the CT scanning, core opening and providing the bathymetry data.

The next person that definitely deserves to be mentioned here is Dawei Liu. Our road trip to Stockholm was one of the highlights of my thesis research and I am glad you were my co-pilot during those endless hours of mastering the European highways. I very much appreciated that you managed the GPS, the music, and the occasional snacks. I would once more like to apologise for my driving, which wasn’t always as smooth as it should be, with the occasional detour or stressful moments. And I must also thank you for running your model on my cores. A special shout out to the Swedish border control is also in place, for letting my Chinese passenger into the country; I don’t know how I would have made it to Stockholm if you had detained him a second time. Also, Malin Kylander and the SLAM lab at Stockholm University deserve a big thanks, for welcoming us in their lab and helping us with the XRF core scanning.

The figures in this thesis are the result of spending countless hours of my time on different software programmes, which would probably have taken an even longer time if it wasn’t for the help of Katleen Wils and the organised software classes taught by Katleen, Loïc, Evelien Boes, and Seb. These people therefore also deserve a special thank you.

The following people deserve to be mentioned here as well: Benjamin Amann, for your know-how on flood frequency depiction and your help with the floodplain data; Alberto Araneda, for the dating of a core from Laguna Confluencia and allowing me to use that data; Maarten Van Daele, for helping me with the CT scanning of the cores; Stefanie Van Offenwert, for giving me access to the petrographic microscope; Sarah Stammen, for providing me with the floodplain data from her bachelor project; and everyone from the RCMG who helped me in one way or another.

Spending days on end on scientific research and writing can be exhausting, and I honestly wouldn’t have left my desk during the past few months if not for my friends and family who helped me maintain a social life. So, a massive hug to all of them: to Lotte and Marlies, for the fun times we had in the computer room; to Lotte, Kenneth and Patsy, for the game nights; to Lotte and Patsy, for the afternoon strolls in the forests of the campus; and to all of my friends, for the lunches, the drinks, and for just being there when I needed you. Also, a massive thanks to the Geologica is in place; your activities throughout this year really helped me relieve some stress.

I would finally like to thank my brother, for proofreading this thesis, and my parents, for supporting me and putting up with my stress and grumpiness from time to time. I wish to thank you for giving me the opportunity to study whatever I wanted; you’re maybe still wondering what the hell I did during the past five years, and I hope this thesis is a bit representative of my effort and gained knowledge of five years at university. Een dikke merci!

Table of Contents

The Story of my Research ...... 4

1. Introduction ...... 6

2. Regional setting ...... 9 2.1. Patagonian icefields ...... 9 2.2. Patagonian climate ...... 9 2.3. Holocene glacier variability in Patagonia ...... 11 2.4. Studied lakes and proglacial rivers...... 13

3. Material and methods ...... 15 3.1. Lake bathymetry and sediment core acquisition ...... 15 3.2. Non-destructive analyses ...... 16 3.2.1. X-Ray Computed Tomography imaging ...... 16 3.2.2. Core opening, description, and linescan imaging ...... 16 3.2.3. Geophysical property core scanning ...... 17 3.2.4. X-Ray Fluorescence core scanning ...... 17 3.3. Destructive analyses ...... 18 3.3.1. Smear slide preparation ...... 18 3.3.2. Grain-size analysis ...... 18 3.4. Statistical analysis of geochemical data as grain-size proxy ...... 19 3.5. Chronology ...... 19

4. Results ...... 20 4.1. Laguna Confluencia ...... 20 4.1.1. Lithology ...... 20 4.1.2. Smear slides ...... 23 4.1.3. Density and magnetic susceptibility trends ...... 23 4.1.4. X-Ray Fluorescence ...... 23 4.1.5. Grain size ...... 24 4.1.6. Geochemical data as grain-size proxy ...... 24 4.1.7. Chronology ...... 25 4.2. Lago Juncal ...... 26 4.2.1. Lithology ...... 26 4.2.2. Smear slides ...... 29 4.2.3. Density and magnetic susceptibility trends ...... 29 4.2.4. X-Ray Fluorescence ...... 30 4.2.5. Grain size ...... 30 4.2.6. Geochemical data as grain-size proxy ...... 30

5. Discussion ...... 32 5.1. Flood deposit identification ...... 32 5.1.1. Laguna Confluencia ...... 32

5.1.2. Lago Juncal ...... 34 5.2. Inter-lake comparison of flood record potential ...... 35 5.3. Flooding of Río Tranquilo during the late Holocene ...... 36 5.4. Flood occurrence versus glacier variability of the San Lorenzo Icefield ...... 38 5.5. Flood occurrence versus climate variability in Chilean Patagonia ...... 40 5.5.1. Temperature reconstructions ...... 40 5.5.2. Precipitation reconstruction ...... 42 5.6. Triggering of Río Tranquilo flooding during the past 1200 years ...... 42

6. Conclusions ...... 44

7. References ...... 46

8. Appendices ...... 54

The Story of my Research

It is very well known that planet Earth may not always be the safest place to live. It is home to various natural hazards, each one of them as spectacular as they can be disastrous. The destructive powers of our planet have intrigued me ever since I was a child, so when it came to choosing my thesis topic, I knew that it had to be something related to natural hazards. Obviously, a research project on past river flooding in Chilean Patagonia sparked my interest, not only because of the hazard-related content, but also because of the region itself. Being a part of the ‘Patagonian mud’ research team for a while made me convinced that Patagonia is indeed a vast region of pristine beauty, which I hope to visit myself one day.

It is becoming increasingly clear that flooding in Chilean Patagonia is linked to the growing and melting of the many glaciers in the region. Since this is influenced by climate, it is of scientific interest to look at the occurrence of floods during times of climate change. The main goal of my thesis deals with this subject. To know how often a flood has happened in the past in an area where river monitoring and historical documents are as good as non-existent, I used a specific type of “archive” that can go back several centuries: lake sediments. Though the lakes in the area may be small, they are record holders, nonetheless. Years and years of mud accumulation on the bottom of a lake can provide a distinct record of river flooding, since such events can cause an overflow of river water into the lake, causing small layers of mud to be placed on the lake floor. So, by means of this archive, I was able to reconstruct the past flooding of a Patagonian river fed by glacial meltwater.

During my research, I used various methods to decipher the flood history written in my Patagonian mud records. This allowed me to present a diversity of indicators that show the presence of flood layers, and, when compared to known changes in climate, it can be observed that river flooding was more prominent when the climate was warmer. This relationship points to a possible link with increased meltwater release from glaciers during warmer times, which is interesting in terms of future flood occurrence and the present-day warming climate in Chilean Patagonia. The time I spent on my thesis was very mixed in nature and included countless hours of lab work and late nights filled with data processing, but also flashes of creativity during the creation of maps and figures, and even a road trip to Stockholm. Looking back, the variety of work is what will stick with me the most, and I hope that my research can help in the investigation and future hazard risk assessment of river flooding in Chilean Patagonia.

This figure depicts the occurrence of floods during the past 1200 years as derived from the dating of flood layers. When investigating this flood occurrence in terms of a 101-year running sum, five periods of high flood occurrence can be denoted (blue bars). When comparing the flood occurrence with the changing climate in Chilean Patagonia, it can be seen that temperature (both continental air and sea surface temperature) is more clearly linked to flooding than precipitation. This can indicate that flooding is mainly caused by the temperature-driven melting of glaciers in the region.

1. Introduction

The impact of climate change on natural hazard occurrence and severity has been studied increasingly over the past few decades (Banholzer et al., 2014; IPCC, 2014, 2012; Van Aalst, 2006), with reports forecasting an increase of extreme events in the 21st century. For instance, a warming climate is expected to cause an increase in the frequency and intensity of river flooding (Blöschl et al., 2017; IPCC, 2014; Seneviratne et al., 2012). Considering that floods can cause substantial human and economic losses (e.g. Aon Benfield, 2019; UNISDR and CRED, 2019), their spatial and temporal variability is of growing interest to the scientific community, especially for risk assessment purposes (Merz et al., 2014; Wilhelm et al., 2018). The most accessible approach for flood hazard assessment is the use of instrumental data recorded at river gauging stations (e.g. Dussaillant et al., 2010; Hussain, 2017; Mudersbach et al., 2017). However, these records can be scarce and discontinuous, and are limited in time, seldom spanning more than a century (Hall et al., 2014; Seneviratne et al., 2012). Longer flood records are needed to investigate the occurrence of low-frequency, high-magnitude events (Baker, 2008), and to assess relations with a long-term changing climate. Paleo-flood reconstructions based on historical documents, tree rings, speleothems, fluvial sediments, and lake sediments are therefore increasingly used to study flood occurrence beyond the instrumental record (Wilhelm et al., 2018).

Lake sediments are considered valuable archives for paleo-flood reconstruction since lakes form natural sinks for the deposition of material transported during floods through surface run-off or river inflow (Schillereff et al., 2014; Wilhelm et al., 2018). Lake sedimentary sequences generally contain a continuous record, allowing to identify discrete, high-energy deposits which are enriched in detrital material from catchment erosion, often contrasting with the organic-rich background sediment (Schillereff et al., 2014; Wilhelm et al., 2018, 2015). Dating of such flood layers has allowed to reconstruct river flooding over time periods spanning multiple centuries to even millennia, often at decadal to centennial resolution (Schillereff et al., 2014; Wilhelm et al., 2018). By using a combination of different analytical techniques on lake sediment cores, paleo-flood events have been reconstructed worldwide, including Europe (e.g. Bøe et al., 2006; Czymzik et al., 2010; Moreno et al., 2008), Africa (e.g. Reinwarth et al., 2013), Asia (e.g. Li et al., 2013; Schlolaut et al., 2014), North America (e.g. Brown et al., 2000; Osleger et al., 2009), and South America (e.g. Jenny et al., 2002).

Chilean Patagonia is a nearly untouched, sparsely populated area in South America with unique environments comprising a variety of rivers, lakes, fjords, and glaciers. Patagonian river discharge is influenced by water storage and release from lakes and/or glaciers, with variations in flow regime thus affected by meltwater release and overall glacier dynamics (Dussaillant et al., 2012; Ulloa et al., 2018). This region has also been affected by climate change (Kaltenborn et al., 2010). For instance, records in northern Patagonia show a significant temperature increase and precipitation decrease during the past century (Masiokas et al., 2008; Villalba et al., 2003). Furthermore, icefields and glaciers are showing a drastic recession and thinning in Chilean Patagonia, resulting in significant ice-mass losses, and consequently contributing to sea-level rise (Masiokas et al., 2008; Rignot et al., 2003). Glacial hazards caused by changes in glacier dynamics, such as outburst floods, ice avalanches and lahars, are also known to affect communities in this region (Iribarren Anacona et al., 2015b). Chilean Patagonia is therefore a region of growing scientific interest, especially to study the relationship between climate change, glacier dynamics, and natural hazards.

Given the abundance of rivers, lakes, and ice masses in Chilean Patagonia, river flood events constitute one of the most important natural hazards (Iribarren Anacona et al., 2015b). Generally, floods in this region are triggered by increased rainfall (e.g. Bertrand et al., 2014) or rapid snow and/or ice melt (e.g. Dussaillant et al., 2010). With regards to the latter, glacier shrinkage often results in the formation and growth of proglacial lakes. These lakes are situated in front of outlet glaciers and are dammed by moraines or ice-masses (Iribarren Anacona et al., 2015b). The failure of the dam impounding the lake causes the sudden release of large volumes of water, triggering floods that can reach downstream communities and cause infrastructural damage and loss of livestock and human lives (Carey, 2010; Iribarren Anacona et al., 2015b). These specific types of floods are referred to as Glacial Lake Outburst

Floods (GLOFs) and have occurred frequently in Chilean Patagonia during the past few decades (e.g. Dussaillant et al., 2010; Iribarren Anacona et al., 2015a; Wilson et al., 2019). Due to a changing climate, which causes the accelerated retreat of glaciers and the subsequent growth of glacial lakes (Wilson et al., 2018), it has been suggested that the frequency and/or magnitude of such glacier-related floods is increasing (Iribarren Anacona et al., 2015b; UNESCO, 2007). Understanding the relationship between climate change and flood frequency in Chilean Patagonia is therefore of great importance for future hazard assessment in the region.

Whereas the main focus of glacier-related research in Chilean Patagonia has been on the major ice masses in the region, i.e. the Northern and Southern Patagonian Icefields and the Cordillera Darwin Icefield (e.g. Aniya, 2013, 1996; Bertrand et al., 2017; Glasser et al., 2004; Mercer, 1982; Fig. 1.1), less attention has been paid to smaller ice-covered areas outside these three icefields. This includes Monte San Lorenzo (47°36’S, 72°19’W), an ice-covered mountain on the Chilean-Argentinean border, located about 70 km east of the southern limit of the Northern Patagonian Icefield (Fig. 1.1; Falaschi et al., 2013). Several proglacial lakes can be found in front of Monte San Lorenzo glaciers, feeding proglacial rivers that flow into downstream valleys. As these rivers are susceptible to flooding (Rojas Aldana, 2018), their paleo-flood history is of both socio-economic and scientific interest, especially in relation with glacier variability of the San Lorenzo Icefield and climate change in Chilean Patagonia.

In this thesis, the late Holocene flooding history of two proglacial rivers coming from the San Lorenzo Icefield, i.e. Río Tranquilo and Río del Salto, will be studied. For both rivers, lake sediments from a nearby lake will be used as a paleo-flood archive. The observed flood frequencies will then be compared to glacier and climate variability reconstructions, in order to assess the relations between flood occurrence, glacier dynamics and climate variability in the Monte San Lorenzo area. Essentially, this thesis will address four specific research objectives:

1. Identifying flood deposits in lake sediments by using a multi-proxy data set containing different geophysical, geochemical, and sedimentological variables. Several analytical and statistical techniques will be applied to obtain a diverse array of potential flood indicators, which will be combined to distinguish flood deposits from background lake sediment.

2. Evaluating the potential of each lake for flood reconstruction. The ability of each lake to successfully preserve a distinct imprint of flood events within the sediment sequence will be assessed based on how well flood deposits can be differentiated from background lake sediment and on the hydrological setting of the lake.

3. Reconstructing the frequency of proglacial floods from the San Lorenzo Icefield during the late Holocene. Based on the chronology of the identified flood deposits, a flood history will be established, allowing to investigate changes in flood frequency during the past few centuries.

4. Investigating possible relations between flood occurrence and glacier and climate variability. The reconstructed flood frequency will be discussed in terms of glacier variability of the San Lorenzo Icefield during the late Holocene and will also be compared to changes in temperature and precipitation in Chilean Patagonia.

The sediment variables used in this study will be obtained using a series of non-destructive and destructive analyses which are recognised as the state-of-the-art in paleo-flood research, for instance X-ray attenuation (e.g. Støren et al., 2010), bulk density (e.g. Simonneau et al., 2013), magnetic susceptibility (e.g. Støren et al., 2010), grain size (e.g. Czymzik et al., 2010), and inorganic geochemistry (for instance Si, K, Ti, Rb, and Zr; e.g. Czymzik et al., 2013; Swierczynski et al., 2012).

The results and conclusions of this thesis will contribute to a better understanding of the effects of climate change on proglacial flooding. This can aid in improving flood hazards assessments in the future, which is beneficial for local communities and government agencies, especially since flood research is sparse in the region. Ultimately, this study can add to the growing body of research on glacier-related natural hazards in Chilean Patagonia.

FIGURE 1.1. The major icefields of Patagonia (in black) with indication of the Monte San Lorenzo region, adapted from Bendle (2018).

2. Regional setting

2.1. Patagonian icefields

The Patagonian Andes south of 46°S comprise three major ice masses, i.e. the Northern Patagonian Icefield (NPI), the Southern Patagonian Icefield (SPI), and the smaller Cordillera Darwin Icefield in Tierra del Fuego (Fig. 1.1). The NPI and SPI combined form the largest temperate ice body in the Southern Hemisphere, having an ice volume of approximately 4,756 km³ (Aniya, 2013; Millan et al., 2019). The NPI (46°30’S–47°30’S, 73°39’W) is 120 km long and 40 to 60 km wide, covering an area of approximately 3,674 km2 (Fig. 1.1; Glasser et al., 2004; Meier et al., 2018). The SPI (48°50’S–51°30’S, 73°30’W) is 360 km long and 40 km wide, covering an area of approximately 12,232 km2 (Fig. 1.1; Glasser et al., 2004; Meier et al., 2018). Much of the icefields lie at an elevation of 1,000–1,500 m above sea level (a.s.l.; Aniya, 2013; Millan et al., 2019).

Some smaller ice masses are located outside of the main mountain chain of the Patagonian Andes, such as the San Lorenzo Icefield, which lies on a solitary mountain (3,706 m a.s.l.) and has an ice- covered area of approximately 139 km2 (Falaschi et al., 2013; Figs. 1.1 and 2.1). The snowline on Monte San Lorenzo has been estimated to be 1,700–1,750 m a.s.l. on the western side and 1,800 m a.s.l. on the eastern side (Falaschi et al., 2013). The Monte San Lorenzo region is situated in the Eastern Andes Metamorphic Complex, which consists of polydeformed turbidite successions, with minor limestone bodies and metabasites (Hervé et al., 2008). The mountain itself consists of a low-grade metamorphic basement of Devonian to Lower Carboniferous age and a Mesozoic volcanic and sedimentary sequence (Falaschi et al., 2015). The whole sequence was intruded by the Southern Patagonian batholith to the west, consisting of granites, granodiorites and tonalites that are Jurassic in age. These lithologies mainly crop out in the northern part of the mountain (Sernageomin, 2003). At the eastern side of Monte San Lorenzo, the outcropping magmatic rocks are Miocene granodiorites, diorites and tonalites (Sernageomin, 2003).

2.2. Patagonian climate

The main carriers of precipitation in the study area are the year-round strong westerlies that are part of the Southern Westerly Wind Belt (SWWB; Bertrand et al., 2014; Garreaud et al., 2013). The SWWB interacts with the N-S orientation of the Patagonian Andes, creating a strong, W-E-oriented precipitation gradient across the region (Carrasco et al., 2002; Garreaud et al., 2009). Annual mean precipitation in Patagonia ranges from 5,000–10,000 mm in the west, to less than 300 mm east of the main Andean divide (Garreaud et al., 2013). Overall, the western side of the main divide is characterised by a temperate, hyperhumid climate, whereas the eastern side has a continental, arid climate (Garreaud et al., 2013). Temperature has a less extreme gradient than precipitation, with the mean annual temperature at, for instance, 41°S varying from 10 °C on the western side to 8 °C on the eastern side of the Andes (Villalba et al., 2003).

Due to its location on the leeside of the Andes, the Monte San Lorenzo region has a continental climate (Aravena, 2007). Mean annual precipitation, based on climate modelling, ranges from 700 mm/year on the western flank of Monte San Lorenzo, to 500 mm/year on the eastern flank (Fick and Hijmans, 2017). Mean annual temperature, also based on climate modelling, ranges from –6 °C around the top of Monte San Lorenzo to 7 °C in the surrounding river valleys (Fick and Hijmans, 2017). According to Falaschi et al. (2015), the 0 °C isotherm altitude of the present-day mean annual air temperature is located at 1,725 m a.s.l. for Monte San Lorenzo. At the nearest meteorological station in Cochrane (ca. 45 km northwest of Monte San Lorenzo; Fig. 2.1), the mean annual precipitation during the interval 2010–2018 CE was 581 mm, while the mean annual air temperature during that interval was 7.7 °C (Agromet, 2019). The Monte San Lorenzo region has a narrow range of mean monthly precipitation (35 to 90 mm for 1950– 2000 CE) and a wide range of mean monthly temperature (between 4.3 and 19.6 °C for 1950-2000 CE; Aravena, 2007). Precipitation in the Río Baker basin west of Monte San Lorenzo occurs predominantly as snowfall during austral autumn and winter (April–August; Dussaillant et al., 2012). River discharge in the eastern sub-basins of Río Baker, including the Río del Salto catchment (Fig. 2.1), is controlled by

snowmelt in austral spring and summer (November–February), followed by low discharge during austral autumn and winter (Dussaillant et al., 2012).

FIGURE 2.1. Overview of the Monte San Lorenzo region with indication of the proglacial rivers studied in this thesis and their catchments. The asterisk indicates the location of the floodplain core studied by Rojas Aldana (2018). Glaciers on Monte San Lorenzo are: 1) Calluqueo, 2) Río Tranquilo, 3) Arroyo San Lorenzo, 4) San Lorenzo Norte, 5) San Lorenzo Este, 6) San Lorenzo Sur, 7) Cumbre Sur, and 8) Pedregoso. Map taken from Global Mapper World Imagery with catchments generated from SRTM data.

According to Villalba et al. (2003), temperatures south of 46°S have been anomalously warm during the past century. By using composite tree-ring reconstructions, these authors have observed that the mean annual temperature during the interval 1900–1990 CE was 0.86 °C above the 1640–1899 CE mean, with the rate of temperature increase being the highest between 1850 and 1920 CE (Villalba et al., 2003). Concerning precipitation during the past century, no clear trend could be interpreted for the region given the high spatial precipitation variability (Carrasco et al., 2002). However, an increase in precipitation around 1960 CE for the region east of the Andes between 45° S and 47° S, followed by a decreasing trend after the 1960s, has been reported (Aravena and Luckman, 2009). Garreaud et al. (2013) note a reduction of the westerly flow over the Patagonian Andes in north-central Patagonia during the interval 1968-2001 CE, which coincides with an approximate precipitation decrease of 300 mm per

decade at the main western peaks around the latitude of Monte San Lorenzo (Falaschi et al., 2013). It is concluded by Falaschi et al. (2013) that, given the extreme precipitation gradients in the area, which is approximately 170 km east from the Pacific coast, it is difficult to determine the recent changes in climate that have occurred at Monte San Lorenzo. Their analysis of 1969-2008 CE records from the meteorological station in Cochrane showed no conclusive trends in winter precipitation nor summer temperature. They also note, however, that this meteorological station is at 180 m a.s.l. and therefore not entirely representative of the climatic conditions at higher elevations around Monte San Lorenzo.

During the past millennium, a transition from a wet, warm period to an even wetter, but cold period has occurred in northern Chilean Patagonia as indicated by several studies (e.g. Bertrand et al., 2014, 2005; Lamy et al., 2001; Sepúlveda et al., 2009; Villa-Martínez et al., 2012). An increase in precipitation around 1200–1500 CE is shown to be related to a gradual shift of the SWWB towards the equator (Bertrand et al., 2014). A strong relationship between the latitudinal position of the SWWB and sea surface temperature (SST) variability in the Southern Ocean and south-eastern Pacific is also evident, showing that the equatorward position of the SWWB was related to lower SSTs (Bertrand et al., 2014). A SST reconstruction from the Chilean margin (44°S) by Collins et al. (2019) showed that a large magnitude cooling transition occurred between 850 and 1350 CE, which is also observed in other Chilean margin SST records (e.g. Haddam et al., 2018; Sepúlveda et al., 2009). This cooling is suggested to be associated with a reduction of Southern Ocean deep convection triggered by freshwater from increased sea ice, which is in turn related to stronger El Niño Southern Oscillation variability (Collins et al., 2019).

Several terrestrial temperature reconstructions also exist for Chilean Patagonia. However, differences occur between these records for the timing of warm and cold periods, which is likely due to the spatial variability of temperature in the region or the reconstruction of different variables. For instance, Neukom et al. (2011) statistically reconstructed mean summer surface air temperature in southern South America for the past millennium, which is characterised by warmer periods before 1350 CE, during 1710–1820 CE and after 1940 CE, and colder periods during 1400–1650 CE and during 1820–1940 CE. However, lake sediments have also been used to reconstruct temperature in Chilean Patagonia, including a 1600- year mean annual temperature reconstruction from Laguna Escondida (45°S; Elbert et al., 2013) and a 600-year annual summer temperature reconstruction based on varved lake sediments from a proglacial lake of the NPI (Lago Plomo, 47°S; Elbert et al., 2015). The former shows the presence of warmer intervals during 600–1150 CE and 1450–1700 CE, and colder intervals during 1200–1450 CE and after 1700 CE. The latter indicates warm periods in the 15th century, during 1780–1810 CE and in the 19th century, and colder periods during the 16th, 18th, and the beginning of the 20th century. Given the significant discrepancies between these different reconstructions, it is challenging to get an understanding of the terrestrial temperature changes in the Monte San Lorenzo region during the past centuries.

2.3. Holocene glacier variability in Patagonia

Glacier advances in Chilean Patagonia during the Holocene began around 5600 cal yr BP, which coincides with a strong cooling episode, and is referred to as the Neoglacial interval (Glasser et al., 2004). Different chronologies exist for these Neoglacial glacier advances, i.e. the ‘Mercer-type’ chronology and ‘Aniya-type’ chronology (Fig. 2.2). Both chronologies are mainly based on dendrochronology and radiocarbon dates from moraines in front of outlet glaciers of the SPI. Since different glaciers with different fluctuation histories were studied in order to establish these chronologies, both are equally valid (Glasser et al., 2004). According to Mercer (1982, 1976, 1970), three Neoglacial advances of outlet glaciers occurred, i.e. 5400–4400 cal yr BP, 2750–1900 cal yr BP, and during the 17th–19th centuries (Bertrand et al., 2012; Fig. 2.2). Aniya (1996, 1995) revised this chronology to include four Neoglacial advances, i.e. at 3850 cal yr BP, 2200 cal yr BP, 1450–1300 cal yr BP, and during the 17th–19th centuries (Bertrand et al., 2012; Fig. 2.2). The main differences between the two chronologies are the date of the first neoglaciation and the occurrence of a neoglaciation at 1450–1300 cal yr BP (Aniya, 2013). Glasser et al. (2004) note that these chronologies are best considered as broad regional

trends, since there are examples of glacier advances occurring outside these time intervals (e.g. Glasser et al., 2002).

FIGURE 2.2. Neoglacial advances of the Patagonian glaciers during the mid to late Holocene, depicting the classical schemes proposed by Mercer (1982, 1976, 1970) and Aniya (1996, 1995), and the reviewed scheme by Aniya (2013). It should be noted that the delineation of each neoglaciation is not conclusive, but rather shows the probability of a neoglaciation based on the combined advances of different glaciers.

A more recent chronology based on further research, including on outlet glaciers of the NPI, has been proposed by Aniya (2013). This includes five Neoglacial glacier advances, i.e. 5130–4430 cal yr BP, 3850–3490 cal yr BP, 2770–1910 cal yr BP, 1450–750 cal yr BP, and during the 17th–19th centuries (Bertrand et al., 2017; Fig. 2.2). Dates for earlier glaciations have also been obtained at several glaciers in the NPI and SPI (e.g. Aniya and Shibata, 2001; Clapperton, 1993; Douglass et al., 2005; Harrison et al., 2012; Wenzens, 1999), giving two additional Holocene glaciations at 6400–5600 cal yr BP and 9000–7600 (or 8300) cal yr BP (Aniya, 2013). Two older glaciations, i.e. 9700–9500 cal yr BP and 11,100–10,200 cal yr BP, remain uncertain (Aniya, 2013). It should be noted, however, that the abovementioned Neoglacial glacier advances have only taken into account glacier advances of the NPI and SPI and may therefore not be representative of glacier variability of other icefields in the region, such as the San Lorenzo Icefield.

According to Masiokas et al. (2009), a regional contrast can be found between the NPI and the SPI during the 17th–19th centuries, which coincides with the Little Ice Age (LIA), a colder period identified in the Northern Hemisphere. In the NPI, the LIA maximum mostly occurred during the 19th century, whereas in the SPI it occurred one to three centuries earlier. These authors also noted that some glaciers made two or more advances during the LIA. After the LIA, most glaciers have shown a retreating pattern that has continued until present (Masiokas et al., 2009). Both a significant warming over most of the 20th century and a decrease in precipitation have been discussed as possible forcing mechanisms behind the glacier recession in southern Patagonia (Rignot et al., 2003; Villalba et al., 2003).

The dating of multiple moraines in front of the glaciers on Monte San Lorenzo (Fig. 2.1) has allowed to reconstruct glacier advances during the past centuries. Frontal and lateral moraines at the Calluqueo, Río Tranquilo and Arroyo San Lorenzo Glaciers (Fig. 2.1) have been identified and dated with tree rings, with the resulting ages lying between ca. 1600 CE and the early 1900s (Aravena, 2007). For the Calluqueo Glacier, the glacier tongue experienced extensive down-wasting of more than 100 m of the

ice surface after 1760 CE, with a recession of the ice front of approximately 2,500 m since 1940 CE, and the formation of a proglacial lake as a result of the rapid collapse of the lower portion of the glacier (Aravena, 2007). In the valley of the Río Tranquilo Glacier, moraine groups have been identified, of which the innermost has an age of ca. 5500 yr BP (Sagredo et al., 2017). Even closer to the present glacier tongues of the Río Tranquilo Glacier, several younger moraines have been dated, with the frontal moraine being dated to 1873 CE (Aravena, 2007). In addition to the moraines dated by Aravena (2007), a moraine of the Arroyo San Lorenzo Glacier has been dated with lichenometry to 1536 CE (Morano- Büchner and Aravena, 2013). Overall, for the Calluqueo, Río Tranquilo and Arroyo San Lorenzo Glaciers, Aravena (2007) showed that a glacier advance occurred in the mid-17th century during a period of higher precipitation and below-average temperatures. For the Río Tranquilo Glacier, Sagredo et al. (2017) indicated a higher sensitivity to changes in temperature than precipitation. For the San Lorenzo Este Glacier (Fig. 2.1), the maximum extent has been dated to ca. 5300 cal yr BP (Garibotti and Villalba, 2017; Mercer, 1968). Finally, the San Lorenzo Sur Glacier (Fig. 2.1) shows evidence of at least five different glacier advances during the past 500 years with four outer moraine systems being formed prior to 1665, 1769, 1819 and 1864 CE (García-Zamora et al., 2004). For this glacier, Garibotti and Villalba (2017) dated the maximum extent with lichenometry to ca. 5750 years ago.

Based on aerial photographs, proglacial lakes started to form on Monte San Lorenzo prior to 1969 CE and have expanded ever since (Falaschi et al., 2013). These authors calculated that the total glacier area in the Monte San Lorenzo region has decreased by 18.6% between 1985 and 2008 CE, with 2000– 2008 CE being the period that shows the highest retreat rates with an overall 2.6% ice loss per year. In general, east-facing glaciers experienced the highest retreat rates compared to glaciers that have a different orientation (Falaschi et al., 2013). These authors calculated average retreating rates for glaciers Calluqueo, San Lorenzo Norte, San Lorenzo Este, and San Lorenzo Sur of 25 m/yr, 19 m/yr, 90 m/yr, and 56 m/yr, respectively.

2.4. Studied lakes and proglacial rivers

Several lakes are located near the proglacial rivers coming from the San Lorenzo Icefield (Fig. 2.1). For the purpose of using lake sediments as a paleo-flood archive, two lakes were selected based on their location and geomorphological characteristics (Fig. 2.3). Both lakes contain a vegetated catchment, which is expected to affect the composition of the lake sediment through the supply of terrestrial organic matter.

Laguna Confluencia (47°29’S, 72°32’W; 345 m a.s.l.) is a small (1.22 km2) lake located south of Río Tranquilo (Fig. 2.3a). It has a catchment area of 9.7 km2 and is only fed by three small streams in the southeast (Fig. 2.3a). The outflow stream is located at the north of the lake, through which the lake is connected with Río Tranquilo (Fig. 2.3a). This proglacial river originates from the northern flank of Monte San Lorenzo and is primarily fed by the glacial meltwater of two north-facing glaciers, i.e. the Río Tranquilo and Arroyo San Lorenzo Glaciers (Fig. 2.1). Smaller tributaries coming from the north and south also flow into Río Tranquilo. The catchment area of the Río Tranquilo is approximately 420 km2 (Fig. 2.1).

Lago Juncal (47°20’S, 72°42’W; 195 m a.s.l.) is a small (3.73 km2) lake located west of Río del Salto (Fig. 2.3b). It has a catchment area of 44.8 km2 and is fed by two small streams (Fig. 2.3b). One of the inflow streams, which comes from the southwest, also forms the outflow of Lago Chacabuco, a lake situated ca. 4.5 km to the southwest of Lago Juncal (Fig. 2.3b). The outflow of Lago Juncal is situated at the northeast of the lake, connecting it to Río del Salto (Fig. 2.3b). This proglacial river is primarily fed by glacial meltwater coming from the mountains southwest of Monte San Lorenzo from which the river originates (Fig. 2.1). Meltwater coming from west-facing glaciers on Monte San Lorenzo, i.e. the Calluqueo, Pedregoso and Cumbre Sur Glaciers, also enters the river via Río Pedregoso, a tributary of Río del Salto (Fig. 2.1). Its main tributary, however, is Río Tranquilo coming from the east. Río del Salto therefore also receives meltwater from north-facing glaciers on Monte San Lorenzo. Smaller tributaries that add to the catchment of Río del Salto are coming primarily from the west. Río del Salto forms a

tributary of Río Baker (Fig. 2.1), which has the highest mean annual discharge rate of all rivers in Chile (Dussaillant et al., 2012). The entire catchment area of Río del Salto covers 1270 km2 (Fig. 2.1).

FIGURE 2.3. Hydrological setting of the lakes studied in this thesis, i.e. (a) Laguna Confluencia and (b) Lago Juncal. The dark blue shaded area represents the catchment area of each lake. The yellow line indicates the outline of the flood delta of the lake. Blue arrows indicate the regular inflow and outflow of the lake, yellow arrows indicate the reversed inflow during floods. Maps taken from Global Mapper World Imagery with contours and watershed generated from SRTM data. For exact locations, see Fig. 2.1.

Both Laguna Confluencia and Lago Juncal have a similar morphological characteristic, i.e. a delta feature with no permanent river, presumably shaped by flood-related levee overspill. It is known from oral accounts of inhabitants of the study area that during river floods of both Río Tranquilo and Río del Salto, the lake outflow reverses and becomes an inflow to the lake. This outflow reversal is thought to be enhanced by a narrowing of the river valley downstream from the lake outflow. This constriction is present in the form of rapids at Río Tranquilo (Fig. 2.3a) and a waterfall at Río del Salto (Fig. 2.3b). During flooding, the elevated river water level is impeded by these constrictions, causing water inflow into the lakes. This occasional inflow has resulted in the formation and gradual enlargement of the flood delta (Fig. 2.3).

Río Tranquilo floods thus cause short-lived water inflow into Laguna Confluencia, whereas flooding of Río del Salto causes short-lived water inflow into Lago Juncal. For Lago Juncal, however, it has been observed that this outflow reversal does not only occur during flood events, but also when the discharge of Río del Salto is high enough in summer so that water can flow into the lake from the northeast. Due to the presence of a ridge between Laguna Confluencia and Río del Salto (Fig. 2.3a), the flooding of Río del Salto cannot be recorded in the lake sediments of Laguna Confluencia.

Finally, flood events can also inundate floodplains located along the rivers. In the scope of another master thesis, a peat core (SL17-01) was studied from a floodplain ca. 3.9 km downstream of the confluence between Río Tranquilo and Río del Salto to reconstruct the occurrence of Río del Salto flooding during the late Holocene (Rojas Aldana, 2018; Fig. 2.1).

3. Material and methods

3.1. Lake bathymetry and sediment core acquisition

In order to investigate the proglacial flooding of Río Tranquilo and Río del Salto during the late Holocene, Laguna Confluencia and Lago Juncal were investigated by S. Bertrand and L. Piret during an expedition in January-February 2018 (Piret and Bertrand, 2018). The bathymetry of the lakes was measured using a GARMIN 178 echosounder, which collected 1802 and 3728 data points for Laguna Confluencia and Lago Juncal, respectively (Piret and Bertrand, 2018). The bathymetric data points, along with handpicked lake shorelines from Google Earth, were gridded by L. Piret with Surfer software. A bathymetric map of each lake is shown in Fig. 3.1. The bathymetry of both lakes was used to select coring locations and to have a general understanding of the lake bed morphology.

FIGURE 3.1. Bathymetric maps of the lakes studied in this thesis, i.e. (a) Laguna Confluencia (deepest point = 18.3 m), and (b) Lago Juncal (deepest point = 13.1 m), with indication of the coring locations.

After measuring the bathymetry of the lakes, short sediment cores were retrieved using a UWITEC gravity corer with 4 kg galvanised steel weights (Piret and Bertrand, 2018; Table 3.1). During core acquisition, the corer was operated either with a ball-closing system without hammering (for 60 cm liners) or with a hammering system (for 150 cm liners). In Laguna Confluencia, a core catcher was attached to the liners to retain the sediment within the liner during core retrieval. This measure was not necessary during core retrieval in Lago Juncal. All coring sites were located in flat parts of the lakes to obtain continuous, undisturbed sediment records. The general strategy for core retrieval was to core at least one proximal and one distal site per lake relative to the flood delta. In Laguna Confluencia, which consists of two sub-basins, cores CO18-01 and CO18-02 were retrieved with the hammering system from the deepest, central part of the northern and southern sub-basin, respectively (Fig. 3.1a). In Lago Juncal, four cores were taken at three different coring locations (Fig. 3.1b). Two cores were retrieved

from the deepest part of the lake, one with the normal gravity corer setup (JU18-01B) and one with the additional hammering system (JU18-01). Core JU18-02 was taken in a shallower part of the central basin than JU18-01, and core JU18-03 was retrieved closer to the flood delta. These two cores were also obtained with the hammering system.

TABLE 3.1. Overview of the cores studied in this thesis, their location, water depth and length (Piret and Bertrand, 2018) LAKE CORE LOCATION DEPTH (m) LENGTH (cm) Laguna Confluencia CO18-01* 47°29’20” S 17.7 111.0 72°32’1” W CO18-02 47°29’53” S 16.3 92.6 72°31’56” W CO13 (collected 47°29’15” S 16.6 83.0 by A. Araneda) 7°32’7” W Lago Juncal JU18-01 47°20’36” S 11.7 79.0 72°42’17” W JU18-01B 47°20’36’ S 11.7 29.4 72°’42’17” W JU18-02* 47°20’27” S 11.2 101.0 72°42’12” W JU18-03 47°20’13” S 6.6 86.6 72°41’43” W *Longest core of each lake selected for destructive analysis.

3.2. Non-destructive analyses

3.2.1. X-Ray Computed Tomography imaging

Before splitting the cores, they were scanned by X-Ray Computed Tomography (CT). CT scanning is a non-destructive method that provides a three-dimensional image of sediment cores. It allows to distinguish sedimentary deposits that attenuate a penetrating X-ray differently according to the density and atomic number of the sample constituents (Cnudde et al., 2006). CT images are therefore used to help identify different lithologies in sediment cores. A Siemens SOMATOM Definition Flash medical X- ray CT scanner at Ghent University Hospital was used to image all sediment cores. The scanner was used at 120 kV with an effective tube current time product of 200 mAs and a pitch of 0.45. The resulting voxel sizes are 0.15 mm in the X and Y directions, and 0.30 mm in the Z direction. Visualization of the scans was performed with VG Studio software.

3.2.2. Core opening, description, and linescan imaging

The cores were split at Ghent University using a Geotek Core Splitter. Two vibratory cutters were used to cut through the plastic liner without disturbing the sedimentary content, after which a nylon fishing wire was pulled through the sediment core to obtain clean split core halves. Based on the quality of each core half, an “archive” half (i.e. the highest quality half) and “work” half (i.e. the lowest quality half) were chosen. The archive half would be used for further non-destructive analyses, while the work half would be used for destructive analyses. However, since the overall quality of the work half of each core was quite bad, i.e. containing empty sections and showing loss of sedimentary structure as a result of core splitting, the archive halves were chosen to be used for all further analyses.

A macroscopic description of the archive halves was made after smoothening the split core surface. Specific attention was paid to the grain size and colour of the sediment, as well as to the sedimentary structures and the presence of organic material. In addition, a linescan image with a downcore and crosscore resolution of 50 µm was made with a Geotek Multi-Sensor Core Logger (MSCL) at Ghent University.

3.2.3. Geophysical property core scanning

For the measurement of sediment geophysical properties, i.e. gamma-ray density and magnetic susceptibility (MS), the split cores were covered with cling film and scanned with the Geotek MSCL at Ghent University. These properties were measured with a 2 mm resolution on all cores.

A gamma-ray density scan was carried out using a 137Cs source emitting a gamma ray beam. This scanning method is based on the principle that gamma rays are attenuated as they pass through a split sediment core. The intensity of the gamma ray beam detected on the opposite side of the core depends on its source intensity, the thickness of the split sediment core, and the Compton attenuation coefficient and wet bulk density of the sediment (Zolitschka et al., 2001). This allows to infer the latter from the measured gamma ray attenuation. Wet bulk density values in sediment cores are frequently used to distinguish general lithological changes downcore, which are recorded as variations in density and porosity of the sediment (Zolitschka et al., 2001). Prior to analysis, the gamma ray source was calibrated using a calibration scale consisting of an aluminium element of variable thickness.

Logging of volume-specific MS was carried out using a Bartington MS2E point sensor that is pressed onto the split core surface for a duration of 1 second. An oscillator circuit inside the sensor produces a low-intensity (approximately 80 A/m RMS) non-saturating, alternating (2 kHz) magnetic field. The magnetic susceptibility of a material near the sensor will cause a change in the frequency of the oscillator, which can be measured and converted to volume-specific MS values. A stable iron check piece was used to verify if the sensor was functioning correctly. The volume-specific MS values were afterwards converted to mass-specific MS values by dividing by the sediment wet bulk density. Magnetic susceptibility is a measure of how easily a material can be magnetised and measures the net contribution of all magnetic and non-magnetic components in the sediment (Sandgren and Snowball, 2001). Since the magnetic mineral content in lake sediments can vary as a result of different sedimentation processes and source material, magnetic susceptibility can be used to distinguish sedimentary material deposited under different circumstances (Sandgren and Snowball, 2001).

3.2.4. X-Ray Fluorescence core scanning

The use of X-Ray Fluorescence (XRF) core scanning to analyse lake sediments has significantly increased in the past decades, having become a widely used technique in paleolimnological studies (Croudace et al., 2019; Davies et al., 2015; Löwemark et al., 2019). XRF core scanning allows a rapid, non-destructive and high-resolution geochemical analysis of the inorganic components of sediment cores and is frequently used to assess detrital input and grain-size variations in lake sediments, for instance with Si, K, Ti, Rb, and Zr (e.g. Chawchai et al., 2015; Kylander et al., 2011; Shala et al., 2013).

The archive half of each core was scanned with an Itrax XRF core scanner (Cox Analytical Systems, Gothenburg, Sweden; Croudace et al., 2006) at the Sediment (Lake and Marine) Laboratory at Stockholm University, Sweden. The XRF scans were made at 2 mm resolution using a molybdenum tube with a voltage of 30 kV, a current of 50 mA and an exposure time of 25 seconds. The longest core of each lake (i.e. CO18-01 and JU18-02), however, was selected for XRF analysis at a higher resolution (1 mm). Prior to scanning, a surface scan was performed to account for changes in the topography of the split core surface, and the core surface was covered with thin ultralene foil to prevent contamination of the measuring unit and drying out of the sediment during scanning (Löwemark et al., 2019).

To visualize downcore elemental changes, raw single-element profiles were selected based on the results of a correlation matrix containing different elements created with Microsoft Excel. The ratio of incoherent and coherent scattering (Inc/Coh) and changes in Br, which are generally associated with changes in organic matter content (e.g. Guyard et al., 2007; Huang et al., 2016; Kalugin et al., 2007), are also investigated to be used as organic content proxies since no other analyses related to organic content were performed. It should be noted, however, that these proxies will only be used to investigate relative changes in organic content downcore, as the effects of sediment properties, e.g. water content, on these proxies have not been entirely resolved yet (Woodward and Gadd, 2019).

3.3. Destructive analyses

3.3.1. Smear slide preparation

Smear slides were prepared in order to have a general sense of the different biogenic and minerogenic components present in the sediment cores, and to assess the effectiveness of the chemical preparation for grain-size analysis. Three smear slides were sampled at each different lithology throughout the longest core of each lake (i.e. CO18-01 and JU18-02). For the smear slide preparation, a small amount of wet sediment was spread onto a microscope slide and dispersed with some demineralised water. The slides were then dried on a hotplate and a drop of Norland optical adhesive 61 was added. Afterwards, a thin cover glass was placed on top of the smear slides and they were left to dry in sunlight for a few days. Once the adhesive had hardened, the smear slides were analysed with a petrographic microscope.

3.3.2. Grain-size analysis

Within the scope of this thesis, grain size and grain-size distributions are used to identify and characterise the different sedimentary deposits in the lake sediment. Grain size is a fundamental property in sedimentology that allows to infer information on the dynamics of transport and deposition of sediment (Goossens, 2008), with a higher-energy transport associated with the deposition of coarser sediment (e.g. Lenzi and Marchi, 2000). It can therefore be used to distinguish sedimentary deposits that have different sources or have been deposited under different conditions.

The grain size of the terrigenous content was measured using a Malvern Mastersizer 3000 particle size analyser. This instrument measures particle size using laser diffraction, which is based on the principle that, when a particle diffracts a laser beam, the diffraction angle is inversely proportional to the particle size. The intensity of the diffracted beam is measured at different angles and is used to calculate the volume percentage of particles with a specific size. Two scattering models, i.e. the Fraunhofer and Mie theories, are used to calculate the particle size from the light intensity (Agrawal et al., 1991). Since these theories assume that the particles have a spherical shape, the measured particle size is in fact the diameter of a sphere having an equivalent volume as the actual particle (Matthews, 1991). As laser diffraction measurements based on the Fraunhofer theory tend to underestimate particle sizes close to the wavelength of the laser beam (< 50 µm), measurements based on the Mie theory are preferred and thus used during the grain-size analysis (Jones, 2003).

Samples were taken every 5 mm on the longest core of each lake (i.e. CO18-01 and JU18-02). Around 20 mg of sediment was used to obtain optimal laser obscuration levels during the measurement. An ideal obscuration of the laser beam occurs when the amount of suspended sediment present is sufficient to significantly diffract the laser beam, but not too dense to result in an obstructed penetration of the laser beam and in multiple scattering (Sperazza et al., 2004). Based on the lithological descriptions that were made, the grain size of the lake sediment was expected to represent the clay-silt fraction. Therefore, the obscuration range advised by default, i.e. between 10 and 20% (Malvern, 2015), was not pursued. Instead, the desired range of laser obscuration was set between 2 and 12%.

In order to isolate the terrigenous fraction of the sediment content, the samples were suspended in 10 ml of demineralised water, and organic matter, carbonates, and biogenic silica were chemically removed by boiling with 2 ml H2O2 (30%), 1 ml HCl (10%), and 1 ml NaOH (2N), respectively. After each boiling step, the suspended samples were diluted with demineralised water and decanted once the particles had settled. This method allowed the pH of the solution to become close to neutral after every chemical treatment. Smear slides prepared before and after chemical preparation showed that organic matter, carbonates, and biogenic silica were successfully removed from the samples.

Prior to measuring, the samples were shortly boiled with sodium hexametaphosphate (Calgon, 2%) to assure complete disaggregation of the sediment particles. The Malvern Mastersizer 3000 was used in combination with a HydroMV dispersion unit to ensure sample dispersion during the measurement. Both background and sample measurements were run for 12 seconds with a stirrer rotating at 2500 rpm to

keep the sample in suspension and with an ultrasound running at 10 % of its maximum capacity to reduce agglomeration of the sample. Each sample was measured for a minimum of 3 times for reproducibility, and with both red and blue light to measure the entire particle size range.

During measurement, the Malvern Mastersizer software was used to get a first assessment of the quality of the grain-size distributions. The measurements were considered to be of poor quality when the three consecutive measurements deviated too much and/or when the distribution showed the presence of coarse-grained secondary peaks, therefore needing subsequent processing to improve the quality. This included the adjustment of the absorption index to improve the data fit between the measured scattering pattern and the Mie-modelled scattering pattern, and the removal of the secondary peaks in the grain- size distribution. These peaks showed the presence of high amounts of sediment grains with a coarse grain size (100–2000 µm), clearly separated from the general unimodal distribution seen throughout the cores. Based on the analysis of smear slides, in which no such high amount of coarse material was found, these peaks were identified as artefact peaks (e.g. bubble peaks or thermal peaks) and therefore removed. The grain-size measurement data were then processed using the Malvern Mastersizer software and Gradistat (Blott and Pye, 2001) in order to analyse statistical values such as the mode, median, D10, D90, and geometric mean. Grapher and Surfer by Golden Software were used to visualize downcore trends and the grain-size distributions of individual samples.

3.4. Statistical analysis of geochemical data as grain-size proxy

Given the strong correlation between sediment geochemistry and grain size, single-element XRF counts and elemental log-ratios were investigated for correlation with grain-size by creating correlation matrices with Microsoft Excel. This was done in order to get an assessment of the grain-size variations in the cores that were not sampled for grain-size analysis. Furthermore, a grain-size prediction model using XRF core scanner counts, as recently described by Liu et al. (2019), was applied. This multi-element model attempts to predict a grain-size parameter, e.g. the geometric mean, by using elements that occur in the lithogenic fraction of the sediment and only requires a limited number of grain-size measurements for calibration. A grain-size profile at XRF core scanning resolution is then obtained, which allows the model and selected elements to be used on other cores of the lake to acquire a grain-size prediction.

3.5. Chronology

To construct a core chronology for CO18-01, this core was correlated with a short sediment core collected by A. Araneda in 2013 in the same sub-basin (core CO13; Fig. 3.1; Table 3.1). After acquisition, this core was extruded at 0.5 cm resolution down to a depth of 20 cm, after which slices were taken every 1 cm down to the core bottom. 210Pb, 226Ra, 137Cs and 232Th activities were analysed on eleven samples by gamma-ray spectroscopy in order to date the core using the 210Pb dating method (Sanchez- Cabeza and Ruiz-Fernández, 2012). The measured activities can be found in Appendix A. The Constant Rate of Supply (CRS) model was applied to obtain a sediment accumulation rate for the core, and an error on the accumulation rate was calculated based on the 210Pb activity measurement errors. Furthermore, the 137Cs age marker was used to confirm the constructed age depth model based on the 210Pb dating (Pennington et al., 1973). Since the samples of CO13 were also measured for volume- specific MS, the accumulation rate could be transferred to core CO18-01 by correlating corresponding MS peaks. The resulting accumulation rate for CO18-01 was then used to determine the age of sedimentary deposits at certain depths in the core.

4. Results

4.1. Laguna Confluencia

In the following section, the results obtained for Laguna Confluencia will be presented. The emphasis will be on core CO18-01 (Fig. 4.1), which is the longest core taken in the lake and therefore scanned for XRF at a higher resolution than the other cores and sampled for destructive analysis. The results of core CO18-02 will then be compared to the results of CO18-01 (Fig. 4.2). Finally, the chronology of core CO13 will be presented and transferred to CO18-01.

4.1.1. Lithology

Three different facies can be identified in CO18-01 based on visual descriptions. From the bottom of the core up to a depth of 78 cm, the lithology consists of light grey silty mud (Fig. 4.1). This facies is weakly laminated, contains little to no organic material, and shows a fining upward trend at its top, from 79 to 78 cm. This deposit will hereafter be referred to as facies A1. On top of this facies, dark grey silty mud can be found that is very rich in organic material (Fig. 4.1). This sedimentary sequence will be referred to as facies B1. Light grey, organic-poor silty mud layers with a thickness of < 1 to 17 mm occur throughout this organic-rich deposit. (Fig. 4.1). Their bottom boundary shows a sharp contact with the underlying organic-rich sediment, and in the thicker layers a fining upward trend can be observed. The silty mud layers that have a thickness of > 1 mm are referred to as facies C1 layers. When comparing the lithological description of this core to the CT image, facies A1 and C1 show a higher attenuation (lighter colour) than facies B1 (darker colour; Fig. 4.1).

In core CO18-02, four different facies can be defined, three of which display similarities to the facies defined in CO18-01 (Fig. 4.2). Facies A2 consists of a weakly laminated light grey silty mud and occurs from the bottom of the core up to a depth of 67.2 cm. Facies B2 consists of a black, organic-rich silty mud and can be found on top of facies A2. Light grey, organic-poor silty mud layers with a thickness of < 1 to 11 mm occur throughout facies B2. The layers with a thickness of > 1 mm are referred to as facies C2 layers. Deposits of very fine to fine brown sand, referred to as facies D2, can be observed at a depth of 28.4–29.8 cm and 52.8–63.2 cm (Fig. 4.2). In the latter, a fining upward deposit of brown medium sand occurs at a depth of 54.9–56.4 cm.

To a certain degree, cores CO18-01 and CO18-02 can be correlated with each other (Fig. 4.2). Given their strong similarities, facies A1, B1, and C1 of CO18-01 can be correlated with facies A2, B2, and C2 of CO18-02, respectively. For the sake of simplicity, the correlated facies will hereafter be referred to as facies A, B, and C. However, some differences exist in the lithology and occurrence of the facies between the two cores. Firstly, facies B is more organic-rich in CO18-02 than in CO18-01 and has a darker colour (Fig. 4.2). Facies C layers also occur less frequently in CO18-02 (27 layers) than in CO18- 01 (54 layers), and their thickness varies between the two cores (Fig. 4.2). Not all facies C layers can thus with certainty be correlated between the two cores. Lastly, the facies 2D layers are only found in CO18-02 and have no apparent equivalent in CO18-01 (Fig. 4.2). This facies will from hereon be referred to as facies D.

FIGURE 4.1. Selected results for CO18-01. From left to right: linescan image, CT image, lithological description, wet bulk density, mass-specific MS, selected XRF profiles, and grain-size mode measured with laser diffraction (black) and predicted (grey). XRF data below 109.75 cm are not shown due to too low counts.

FIGURE 4.2. Core correlation between CO18-01 and CO18-02, with (from left to right) linescan image, CT image, lithological description, wet bulk density, Inc/Coh scattering ratio, Zr profile, and grain-size mode (measured (black) and predicted (grey) for CO18-01, predicted for CO18-02). XRF data below 109.75 cm for CO18-01 and below 91.3 cm for CO18-02 are not shown due to too low counts. Question marks indicate uncertain correlations.

4.1.2. Smear slides

The composition of the sediment in CO18-01 varies between the three facies identified in the core (Fig. 4.3). Facies A contains mostly transparent grains and little to no organic material. Facies B, on the other hand, consists mainly of amorphous organic material, with very few diatoms and some very fine-grained mineral grains belonging to the clay to very fine silt fraction. The composition of facies C shows similarities to that of facies A, since it also consists of transparent grains with little to no organic material. The composition of both facies differs notably from that of facies B, which has a higher amount of organic content. In terms of grain size, facies A and C show some slight differences. The grains in facies A have a maximum grain size of 40 µm and belong to the clay to coarse silt fraction. The grains in facies C have a maximum size of 10 µm and therefore belong to the clay to fine silt fraction. As the minerals in the facies are mostly transparent to weakly coloured and show undulatory extinction, they are identified as fine-grained quartz. More strongly coloured to even opaque fine-grained minerals are identified as clay minerals or metal oxides.

FIGURE 4.3. Microscopic images of smear slides from core CO18-01. Facies A, B and C were sampled at a depth of 85.5 cm, 9.5 cm, and 41.5 cm, respectively (see Fig. 4.1).

4.1.3. Density and magnetic susceptibility trends

The wet bulk density profiles in the cores of Laguna Confluencia show sharp variations and can therefore aid in the correlation of both cores (Fig. 4.2). In general, peaks representing higher density values coincide with the aforementioned facies A and C. Facies B displays lower density values, though minor peaks occur in this facies throughout the cores. A slight decrease in density can be observed towards the top of facies A in both cores.

A similar trend can be observed in the mass-specific MS curve of CO18-01 (Fig. 4.1). Higher values also correspond to depths at which facies C layers are present. Facies B has again an overall lower value, though shows a steadier trend when comparing mass-specific MS with wet bulk density. The sediment in facies A displays higher mass-specific MS values compared to facies B and C and shows more variation throughout this deposit as opposed to wet bulk density.

4.1.4. X-Ray Fluorescence

To visualise geochemical variations downcore, the raw, single-element XRF counts were used as these (unprocessed) data would also include the effects of water content, organic content and grain size on the measurement. These factors, especially organic content, can be of importance when discussing the changes in detrital input in lakes, with higher amounts of organic content reflecting less detrital input. A correlation matrix was made in order to quantify the association between certain groups of elements. Elements that did not give sufficiently high counts, i.e. above 500 throughout most of the core, were disregarded, as these low counts could indicate background noise. Strong positive correlations (r ≥ 0.7, p < 0.001) can be found between the elements Si, K, Ti, Rb, and Zr (Table 4.1) indicating a coupling of these elements. A strong negative correlation (r ≤ -0.7, p < 0.001) is found between the abovementioned elements on the one hand, and Br and Inc/Coh on the other hand, indicating a decoupling of these two groups of measured values.

TABLE 4.1. Correlation matrix with Pearson correlation coefficients of selected elements for CO18-01. All correlations are significant at p < 0.001. The full correlation matrix containing all measured elements can be found in Appendix B1. Si K Ca Ti Br Rb Sr Zr Inc/Coh

Si 1 K 0.93 1 Ca 0.84 0.68 1 Ti 0.94 0.97 0.76 1 Br -0.80 -0.82 -0.70 -0.83 1 Rb 0.86 0.95 0.65 0.95 -0.81 1 Sr 0.85 0.72 0.95 0.81 -0.74 0.74 1 Zr 0.87 0.79 0.84 0.87 -0.73 0.83 0.91 1 Inc/Coh -0.89 -0.86 -0.84 -0.90 0.89 -0.86 -0.87 -0.85 1

In CO18-01, the profiles of K, Rb, Ti, Si and Zr display distinctive trends which can be related to the different facies. Higher counts of these elements occur in facies A and C (Fig. 4.1). Regarding the facies C layers, the peaks of the K, Rb and Ti profiles are mostly prominent over their entire thickness, whereas the Si and Zr peaks are most pronounced at the base of the layers. Br counts and Inc/Coh values are lower in facies A and C (Fig. 4.1). The higher Br counts and Inc/Coh values generally occur in facies B, where the K, Rb, Ti, Si and Zr counts are lower.

4.1.5. Grain size

General downcore variations in the grain size of CO18-01 are depicted in Fig. 4.4a. The subdivision in three facies, i.e. facies A, B, and C, is also evident in the grain-size distributions (Fig. 4.4b). Facies A consists of unimodal, poorly sorted mud, with the highest volume percentage belonging to the very fine to fine silt fraction. Facies B consists mainly of unimodal, poorly to moderately sorted mud, with the highest volume percentage occurring in the clay to fine silt fraction. In the top few centimetres of the core, samples in facies B contain a small, coarser mode (Fig. 4.4b). This can also be seen on the average distribution curve of facies B, which shows a tail towards the coarse fraction. The most predominant mode, however, can be found in the clay to fine silt size range. Facies C shows a similar grain-size distribution as facies A but is better sorted (Fig. 4.4b). The highest volume percentage of this facies can also be found in the very fine to fine silt fraction. Since the sampling resolution of the grain- size analysis was 5 mm, only the thickest facies C layers can be observed in the grain-size record. The facies C grain-size measurements as depicted in Fig. 4.4 are therefore an underrepresentation of the actual grain size of the facies C layers in the core.

Given the few bimodal distributions found in the core, which show a dominant, finer mode and a smaller, secondary, coarser mode, the finer mode was chosen to be the best statistical parameter to represent downcore grain-size variations (Fig. 4.4a). The mode shows significant variations throughout the core but is always contained in the very fine silt fraction (4–8 µm). Facies A and C have a coarser average mode (6.3 µm) than facies B (4.9 µm). A decrease of the mode is also present towards the top of the thicker facies C layers and at the top of facies A (Fig. 4.4a).

4.1.6. Geochemical data as grain-size proxy

A correlation matrix with grain size and geochemical data from CO18-01 was used to verify if certain geochemical elements could be used as a grain-size proxy (Appendix C1). Such a proxy could be used to get an estimate of the grain-size variations in core CO18-02, which was not sampled for destructive grain-size analysis. The highest correlation coefficient was found between the grain-size mode and Zr (r = 0.76, p < 0.001), suggesting that Zr counts could be used to estimate grain-size variations (Fig. 4.2). However, Liu et al. (2019) have shown that with a multi-element approach, better grain-size prediction models can be constructed. The results of this grain-size prediction model showed that the mode, as opposed to the geometric mean used in the original model (Liu et al., 2019), was better suited to calibrate

the geochemical data from CO18-01. The elements used for prediction were Ca, Ti, V, Cr, Fe, and Zr. For sediment core CO18-01, the resulting predicted grain-size mode curve shows a strong correlation with the grain-size mode that was measured (r = 0.77, p < 0.001). Both the Zr counts and the model prediction can therefore be used to estimate grain-size mode variations in both cores of Laguna Confluencia (Fig. 4.2). In core CO18-02, both Zr counts and the predicted grain-size mode show higher values in facies A and C compared to facies B, which is similar to the grain-size variation of CO18-01 (Fig. 4.2). The facies D deposits that are only found in CO18-02 show significantly higher values of both Zr and predicted grain-size mode, which is in agreement with the lithological description of that facies, i.e. very fine to medium sand (Fig. 4.2).

FIGURE 4.4. Grain size for CO18-01. (a) Downcore grain-size distribution with the black line indicating the mode, and (b) grain-size distributions of the different facies. Facies C layers have only been sampled in the top 50 cm of the core since the sampling resolution was in many cases bigger than the thickness of these layers. VFS = very fine silt, FS = fine silt, MS = medium silt.

4.1.7. Chronology

Based on the unsupported 210Pb activity profile of core CO13 (Fig. 4.5), a sediment accumulation rate of 0.049 cm/year was calculated. This accumulation rate is supported by the occurrence of the highest 137Cs activity value, which corresponds to the years 1963–1964, in the sample at 2–3 cm depth (Fig. 4.5). Cores CO18-01 and CO13 are closely located to each other, therefore allowing both cores to be correlated with one another. The correlation is based on peaks in volume-specific MS measurements and is depicted in Fig. 4.5. The correlated MS peaks occur at different depths in the cores due to possible differences in sediment accumulation rate at the two coring locations and/or compaction during core acquisition. A correction factor has therefore been calculated to roughly account for the difference in accumulation/compaction in both cores. Based on the difference in depths of the correlated MS peaks and the higher accumulation in the top 3 cm of CO13 compared to CO18-01, a correction factor of 0.89 was obtained. Taking this factor into account, an estimated accumulation rate of 0.044 cm/year was obtained for CO18-01. Additionally, accumulation rate error bars were calculated for CO13 based on the initial errors on the 210Pb activity measurements, and subsequently recalculated for CO18-01 with the correction factor. This resulted in minimum and maximum accumulation rates for CO18-01 of 0.030 and 0.079 cm/year.

FIGURE 4.5. (a) Correlation between CO18-01 and CO13 based on volume-specific MS, with linescan image, CT image and lithological description of CO18-01 (the legend of the lithological description can be found in Fig. 4.1), and (b) activity measurements with error bars of unsupported 210Pb and 137Cs in core CO13. The ages on the Y- axis on the right were calculated based on the accumulation rate estimated with 210Pb dating.

4.2. Lago Juncal

In this next section, the results obtained for Lago Juncal will be presented in the same order as for Laguna Confluencia. The emphasis will be on core JU18-02 (Fig. 4.6), which is the longest core taken in the lake and therefore scanned for XRF at a higher resolution than the other cores and sampled for destructive analysis. The results of cores JU18-01 and JU18-03 will then be compared to the results of JU18-02 (Fig. 4.7). Core JU18-01B will not be discussed considering this core was taken at the same location as JU18-01, which contains a longer sediment record. Since no measurements regarding chronology have been carried out, no age-depth model is available.

4.2.1. Lithology

Two different facies can be identified in JU18-02 based on visual descriptions. The lithology in the core mainly consists of light grey, organic-poor silty mud that shows faint signs of fine lamination (Fig. 4.6). This lithology will be referred to as facies E2. It contains intercalated layers of coarse silt with thicknesses ranging from 3 to 14 mm, which will be referred to as facies F2 layers (Fig. 4.6). These layers are particularly pronounced on the CT image, due to their high attenuation (lighter colour). The upper 3 cm of the core displays orange discolouration (Fig. 4.6).

FIGURE 4.6. Selected results for JU18-02. From left to right: linescan image, CT image, lithological description, wet bulk density, mass-specific MS, selected XRF profiles, and grain-size mode measured with laser diffraction (black) and predicted (grey). XRF data at 21.55-22.25 cm and below 99.35 cm are not shown due to too low counts.

FIGURE 4.7. Core correlation between JU18-01, JU18-02 and JU18-03, with (from left to right) linescan image, CT image, lithological description, mass-specific MS, Zr profile, and grain-size mode (measured (black) and predicted (grey) for JU18-02, predicted for JU18-01 and JU18-03). XRF data are not shown due to too low counts below 77.3 cm for JU18-01, between 21.55-22.25 cm and below 99.35 cm for JU18-02, and below 85.1 cm for JU18-03. Question marks indicate uncertain correlations.

Core JU18-01 also consists of two facies, referred to as facies E1 and F1 (Fig. 4.7). The former consists of faintly laminated, light grey, organic-poor silty mud, whereas the latter consists of coarse silt that is highly attenuated on the CT image. The upper 3.3 cm of the core shows orange discolouration. In JU18- 03, two facies can also be identified, i.e. facies E3 and F3 (Fig. 4.7). From the bottom of the core up to 75 cm depth, facies E3 consists of finely-laminated, coarse-grained, light grey silt that is highly attenuated on the CT image. From 75 cm to the top of the core, this facies consists of finely laminated, finer-grained, light grey silty mud that is also highly attenuated, yet not as high as the bottom part of facies E3. The other facies in this core, facies F3, consists of highly attenuated coarse silt, intercalated in facies E3. The upper 1.5 cm of JU18-03 shows orange discolouration.

Core JU18-01 was taken in close proximity to JU18-02, making it easy to correlate both cores with each other (Fig. 4.7). Since facies E2 and F2 are similar to facies E1 and F1, respectively, they can be correlated with each other. The facies in JU18-02 are also similar to the two facies identified in JU18- 03, with facies E2 and F2 being correlative to facies E3 and F3, respectively. Overall, two different lithologies are thus present in Lago Juncal, namely facies E and F. The amount and thickness of facies F layers differs between the three cores, with JU18-02, JU18-01, and JU18-03 having 9, 8, and 7 facies F layers, respectively. Not all facies F layers can thus with certainty be correlated between the three cores (Fig. 4.7). It is also worth noting that gas bubbles can be observed in all three cores, especially on the CT image (Fig. 4.7). These features are the result of degassing during core retrieval and are therefore not part of the lithology of the sediment.

4.2.2. Smear slides

At the microscopic level, JU18-02 shows little variation in sediment composition throughout the core. The sediment in facies E contains very fine-grained mineral grains with a maximum size of 20 µm, therefore belonging to the clay to medium silt fraction (Fig. 4.8). Some organic material and diatoms are also present. In facies F, the mineral grains have a maximum size of 20–30 µm, thus belonging to the clay to medium silt fraction (Fig. 4.8). Again, little organic material is present. In all smear slides, the minerals are mostly transparent to weakly coloured and show undulatory extinction. The minerals are determined as fine-grained quartz. Some minerals that are more strongly coloured or even opaque are identified as clay minerals or metal oxides.

FIGURE 4.8. Microscopic images of smear slides from core JU18-02. Facies E was sampled at a depth of 27.9 cm, facies F was sampled at a depth of 46 cm (middle) and 89.9 cm (right, see Fig. 4.6).

4.2.3. Density and magnetic susceptibility trends

The wet bulk density profile in core JU18-02 shows a general increase from the bottom of the core up to 60 cm, after which the values decrease again towards the top of the core (Fig. 4.6). Minor peaks are present throughout the density profile, with the largest peaks corresponding to some of the facies F layers.

The mass-specific MS profile for JU18-02 shows a similar, albeit steadier, trend as the wet bulk density profile (Fig. 4.6). Peak values correspond to facies F layers. The mass-specific MS profiles are chosen for correlation since wet bulk density measurements show little variations in the cores of Lago Juncal (Fig 4.7). The presence of prominent peaks, especially in JU18-02 and JU18-01, allows for the

correlation between facies F layers. In general, peaks in mass-specific MS correspond to facies F layers in all three cores.

4.2.4. X-Ray Fluorescence

As for CO18-01, a correlation matrix was made with the elemental XRF values to quantify the association between groups of elements (Table 4.2). Strong positive correlations (r ≥ 0.7, p < 0.001) can be found between K, Rb, Ti, Si, Zr and Ca, indicating a coupling between these elements. No strong negative correlations (r ≤ -0.7, p < 0.001) were found between the above-mentioned elements and Br or Inc/Coh.

TABLE 4.2. Correlation matrix with Pearson correlation coefficients of selected elements for JU18-02. All correlations are significant at p < 0.001. The full correlation matrix containing all measured elements can be found in Appendix B2. Si K Ca Ti Br Rb Zr Inc/Coh

Si 1 K 0.88 1 Ca 0.87 0.79 1 Ti 0.84 0.95 0.84 1 Br -0.30 -0.24 -0.30 -0.25 1 Rb 0.32 0.66 0.14 0.71 -0.15 1 Zr 0.64 0.55 0.58 0.69 -0.25 0.41 1 Inc/Coh -0.13 0.18 -0.67 0.25 0.37 0.62 0.13 1

The XRF profiles of Ca, K, Ti and Si show significant peaks in counts in JU18-02 (Fig. 4.6). Often, the peaks in these element counts correspond to peaks in mass-specific MS and the presence of facies F layers. The variation in Rb and Zr counts is less pronounced, though still present. The scattering ratio profile shows little variation from the bottom of the core up to about 25 cm, after which an increasing trend up to the top is present.

4.2.5. Grain size

In general, the sediment of core JU18-02 consists of a poorly to moderately sorted mud, with the highest volume percentage occurring in the clay to fine silt fraction (Fig. 4.9a). Little variation is visible in the grain-size distribution throughout the core, although sediment becomes finer-grained towards the top (Fig. 4.9a). The grain-size distributions tend to be coarser in facies F layers (Fig. 4.9b). The fine-grained mode is again chosen to be the best statistical parameter to represent grain-size variation (Fig. 4.9b). Small, coarser modes are also visible, but are considered insignificant compared to the predominant finer mode. The mode is contained in the very fine silt fraction (4–8 µm) throughout the core, with facies E having a finer average mode (5.6 µm) than the facies F layers (6.3 µm; Fig. 4.9b). As the sampling resolution of the grain-size analysis was 5 mm, only the thickest facies F layers can be observed in the grain-size record. The facies F grain-size measurements as depicted in Fig. 4.9 are therefore an underrepresentation of the actual grain size of the facies F layers in the core.

4.2.6. Geochemical data as grain-size proxy

Based on a correlation matrix, possible grain-size proxies were selected from the XRF elemental data from JU18-02 (Appendix C2). The strongest correlation was found between the mode and Zr (r = 0.75, p < 0.001). For the grain-size prediction model (Liu et al., 2019), the elements that were applied were K, Ca, Ti, V, Cr, Ni, Rb, Fe, and Zr. The resulting mode prediction has a strong correlation with the measured mode (r = 0.79, p < 0.001). Like for Laguna Confluencia, the Zr counts and the model prediction can thus be used to estimate grain-size mode variations in the three cores of Lago Juncal (Fig. 4.7).

In core JU18-01, both the Zr counts and the predicted mode show higher values for the facies F layers, though peaks are also present in the facies E deposits of both curves (Fig. 4.7). This is also the case in

JU18-03. The coarse silt facies E deposits at the bottom of core JU18-03 correspond to an increase in the values of both Zr counts and predicted mode (Fig. 4.7).

FIGURE 4.9. Grain-size for JU18-02. (a) Downcore grain-size distribution with the black line indicating the mode, and (b) grain-size distributions of the different facies. Facies F layers have only been sampled between 45 and 49 cm since the sampling resolution was in many cases bigger than the thickness of these layers. VFS = very fine silt, FS = fine silt, MS = medium silt.

5. Discussion

5.1. Flood deposit identification

To identify flood deposits in Laguna Confluencia and Lago Juncal, the results of the geophysical, geochemical, and sedimentological analyses have been combined into a multi-proxy data set. The comparison and correlation of these different proxies allows to determine the origin of the sedimentary deposits encountered in each lake.

5.1.1. Laguna Confluencia

The facies identified in section 4.1, i.e. facies A to D (Figs. 4.1 and 4.2), will here be discussed in terms of the examined proxies and interpreted in terms of depositional conditions.

Facies A

This facies shows little variation in both lithology and analysed proxies, suggesting a continuous supply of sediment from a single source during its deposition. The high wet bulk density, mass-specific MS, and X-ray attenuation suggest the presence of large amounts of detrital material (Fig. 4.1). The decreasing density towards the top of this facies is most likely the result of an increase in compaction of the sediment with greater depth (Fig. 4.1; Tenzer and Gladkikh, 2014). The relatively high counts of detrital elements compared to the relatively low Br counts and Inc/Coh values, supported by a strong negative correlation (Table 4.1), suggest a high detrital input during its deposition and a low organic content (Fig. 4.1). The small fluctuations in grain size throughout this deposit further support that a single depositional process was responsible for its formation (Fig. 4.1). Lastly, the relatively large grain-size mode of this facies (~6.3 µm) indicates a high energy during the transport of this sediment (Fig. 4.1). Facies A is therefore interpreted as a deposit formed by continuous sediment input from a proglacial river flowing directly into Laguna Confluencia, transporting glacial mud from mountain glaciers to the lake. The normal grading and the decreasing detrital element counts at the top of this river inflow deposit (Fig. 4.1) suggest deposition under a waning sediment supply (Mulder et al., 2003) and therefore a gradual change in river course.

Based on the examination of this facies, it is hypothesised that Río Tranquilo once flowed into Laguna Confluencia, directly supplying glacial mud to the lake. However, the river course is suggested to have gradually moved away from the lake towards the north, hence by-passing the western end of the lake and disconnecting from it to reach its present-day setting (Fig. 2.3a). This can be observed in the clear transition between facies A and the overlying sediment, i.e. facies B, indicating that the continuous lake inflow from the north was finally cut off from the lake. The river then proceeded to flow west, merging with the small outflow of Laguna Confluencia and into Río del Salto northwest of the lake (Fig. 2.3a). The wide, flat morphology of the lower section of the Río Tranquilo valley (Fig. 2.3a) would have allowed such a change in river course. Additional research on paleo-river features in the morphology of the Río Tranquilo valley might further elucidate the history of this river and the suggested change of its course.

Facies B

Compared to facies A, facies B has a lower wet bulk density, mass-specific MS, X-ray attenuation and intensity of detrital-element counts (Fig. 4.1). The higher Br counts and Inc/Coh values suggest a higher amount of organic material, which can also be observed in the darker colour of this facies (Fig. 4.1). The grain size of this facies is also finer than facies A (Fig. 4.4), which was interpreted as direct river inflow deposits, suggesting a lower hydrodynamic energy during the deposition of facies B. This facies is therefore interpreted to represent the background sedimentation of Laguna Confluencia after the change in river course of Río Tranquilo.

The calmer conditions would have allowed a higher authigenic lake productivity and the settling of organic material. The observation that facies B has a darker colour in the southern sub-basin (CO18- 02) than in the northern sub-basin (CO18-01; Fig. 4.2), and therefore has a higher organic content,

indicates that this background sediment is seemingly less diluted by sediment input in the southern sub- basin than in the northern sub-basin. The input of allogenic particles would only be the result of surface run-off or input via the small inflows in the southeast of the lake (Fig. 2.3a), justifying the overall finer grain-size. This also explains the larger thickness of facies B deposits between facies C layers in the southern sub-basin (CO18-02) compared to the northern sub-basin (CO18-01; Fig. 4.2).

Facies C

The lithology and the physical, chemical, and sedimentological properties of facies C are similar to those of facies A, suggesting similar depositional conditions for both facies. The high detrital and low organic content of facies C compared to the background sediment of facies B (Fig. 4.1) indicates a change to an increased sediment supply in the lake. This is further supported by the coarser grain size of facies C compared to the background sediment (Fig. 4.4). Facies C is therefore interpreted to be deposited under conditions related to an enhanced proglacial river inflow. Since these deposits are more frequent and thicker in the northern sub-basin (CO18-01) than in the southern sub-basin (CO18-02; Fig. 4.2), the inflow responsible for these deposits is likely coming from the north, eliminating the south-eastern streams as possible inflows for this enhanced sediment supply (Fig. 2.3a). The outflow reversal that is known to happen during river floods of Río Tranquilo at the north of the lake is therefore suggested to cause the deposition of these layers. The facies C layers are thus interpreted as proglacial flood deposits.

The normal grading that is apparent in the thicker facies C layers (Fig. 4.1) suggests the decrease in inflow discharge during flood events (Mulder et al., 2003). This fining upward can also be deduced from the detrital-element counts (Fig. 4.1). Si and Zr both have the highest counts at the base of the flood layers and decrease towards the top, since these elements are representative of the coarser silt-sized sediment (Dypvik and Harris, 2001; Kylander et al., 2011). The counts in Ti, which is representative for finer silt-sized sediment (Kylander et al., 2011), are also decreasing towards the top of the deposit. Finally, K and Rb, which are associated with clay-sized sediment (Cuven et al., 2010; Dypvik and Harris, 2001), have higher counts towards the top. In general, the decreasing Zr counts towards the top are an indicator of the decreasing grain size in the flood deposits (Fig. 4.2). These flood layers are slightly finer grained than the direct river inflow deposit (facies A), suggesting that the coarser fraction of the sediment transported during a flood event may have been deposited before it reaches the lake basin. The flood delta is suggested to be the location of the deposition of this coarser-grained fraction. The finer fraction of the transported sediment is than dispersed in the lake as a suspended sediment flow. Another possible explanation for the difference in grain size between the flood layers and the direct river inflow deposit may be that the sediment inflow in the lake during flooding got finer over time.

The presence of a shallow ridge between the northern and southern sub-basins of the lake (Fig. 3.1a) likely blocks the underflows carrying the sediment within the lake during floods, and only overflow plumes, or underflow plumes that are large enough to overtop the ridge, would be recorded in the southern sub-basin. This explains the smaller amount and thickness of facies C layers in the southern sub-basin (Fig. 4.2). Core CO18-01 is therefore better suited for paleo-flood reconstruction, since the deposited sediment in the northern sub-basin would record proglacial floods independently of the type of sediment flow. It is possible that the thin silty mud layers recognised throughout facies B with a thickness of < 1 mm might also be the result of flood inflow in the lake. However, the small thickness of these layers makes it hard to delineate their boundaries, and they are therefore not identified as flood deposits in this thesis. The amount of facies C layers identified in Laguna Confluencia can therefore be an underestimation of the actual amount of flood layers deposited in the lake.

Facies D

The sedimentary deposits belonging to facies D only occur in the southern sub-basin (CO18-02) of Laguna Confluencia (Fig. 4.2). These sandy deposits are characterised by high wet bulk density values, high Zr counts and low Inc/Coh values, suggesting a high amount of detrital material and low amount of organic matter (Fig. 4.2). The predicted grain size of these deposits is also notably coarser than the

direct river inflow deposit (facies A), the background sediment (facies B), and the flood deposits (facies C; Fig. 4.2). The facies D layers are therefore interpreted as deposits inherent to the southern sub-basin, resulting from the transport and deposition of coarse sediment during non-flood conditions. As the inflow in the lake during non-flood conditions only occurs through the small streams in the southeast, the facies D layers may have been deposited during periods of coarse sediment transport via these inflows. Possible triggering of this coarse sediment transport might be a sudden increase in precipitation, mobilising sandy sediment in the catchment of the streams. Deposition due to the instability and failure of steep slopes in the southern sub-basin is also a possible explanation for the presence of this facies. Especially the deposition of the thick sandy deposit at 52.8–63.2 cm (Fig. 4.2) is more likely to be caused by slope failure than by transport via the inflow streams, given the relatively small size of these inflows.

5.1.2. Lago Juncal

The facies identified in section 4.2, i.e. facies E and F (Figs. 4.6 and 4.7), will here be discussed in terms of the examined proxies and interpreted in terms of depositional conditions.

Facies E

This facies shows little significant variation in wet bulk density, Rb and Zr counts, and Inc/Coh values (Fig. 4.6). The decreasing density towards the top of JU18-02 is most likely the result of an increase in compaction of the sediment layers with greater depth (Fig. 4.6; Tenzer and Gladkikh, 2014). The increase in Inc/Coh values in the top part of that core is interpreted as the result of either an increase in water content related to the decreased compaction towards the top (Woodward and Gadd, 2019), or an increase in organic content. The overall little variation in Inc/Coh values from 25 cm downward, however, suggests that little change in organic content is present throughout this facies (Fig. 4.6). The variation in detrital elements and mass-specific MS is most likely the result of changes in mineral content in the lake sediment. Overall, this facies appears finely laminated with no large contrasts in X-ray attenuation, except at the top, where attenuation is low suggesting the presence of more organic content or water due to less compaction (Fig. 4.6). The grain size also shows little variation, with slightly finer sediment towards the top and coarser sediment towards the bottom (Fig. 4.6). This facies is interpreted as silty mud deposited by water inflow via the small streams entering Lago Juncal from the south (Fig. 2.3b), with no particular periods of low/high sediment input and/or low/high lake productivity discernible. However, closer to the lake outflow in the northeast (JU18-03), this facies seems to contain more highly attenuated layers, showing that this part of the lake also has a high detrital input (Fig. 4.7).

Facies F

The main difference between this facies and facies E is the X-ray attenuation (Fig. 4.6). The higher X- ray attenuation of the facies F layers would indicate that these layers have a higher density and/or contain less organic material. This is also supported by the higher mass-specific MS values and coarser grain size, and slightly higher wet bulk density and detrital-element counts (Fig. 4.6). Most notably, the thickest facies F layer (at 45–46.3 cm depth in JU18-02) has a distinct peak in mass-specific MS values and grain-size mode (Fig. 4.6), suggesting a higher input of detrital material during deposition of this layer. The Inc/Coh values of this facies show no apparent difference compared to facies E, therefore also showing no specific variations in organic content (Fig. 4.6). The facies F layers are thus interpreted as proglacial flood layers, deposited during an enhanced inflow discharge. The outflow reversal that is known to happen during river floods of Río del Salto at the northeast of Lago Juncal is suggested to be the cause of the deposition of these flood layers. The small inflow streams at the south of the lake are thought to only account for the regular continuous background sedimentation in the lake during non- flood periods. Compared to the facies F layers, facies E is interpreted as being the background sedimentation in the lake.

5.2. Inter-lake comparison of flood record potential

Sediment layers interpreted as flood deposits have been identified in both Laguna Confluencia and Lago Juncal. For both lakes, the flood deposits are characterised by a high detrital content and relatively coarse grain-size distribution. The nature of the background sediment, however, differs between the two lakes. In Laguna Confluencia, the background sediments are organic-rich and relatively fine-grained, whereas in Lago Juncal, they are detrital-rich and do not differ that much in grain size from the identified flood layers. Flood deposits are therefore much more distinguishable from background sediment in Laguna Confluencia than in Lago Juncal.

When comparing the grain size of the different facies in the lake sediments, it can be observed that the grain-size mode of the flood deposits in Laguna Confluencia (facies C) and the flood deposits in Lago Juncal (facies F) are in fact equal, i.e. 6.3 µm, and that their grain-size distributions are very similar (Fig. 5.1). This suggests that these flood deposits have a similar, proglacial origin. Furthermore, the mode of the direct river inflow deposit in Laguna Confluencia (facies A) is also equal to that of the flood deposits in both lakes (Fig 5.1). The suggested origin of facies A as a direct proglacial river input deposit is in agreement with the proglacial nature of the flood deposits. Facies A, however, is more poorly sorted, suggesting that the depositional conditions for this deposits were not entirely the same as for the flood deposits.

FIGURE 5.1. Comparison of average grain-size distributions of the different lithological facies in Laguna Confluencia (solid lines) and Lago Juncal (dashed lines). The grain-size modes are 6.3 µm (facies A, C, and F), 4.9 µm (facies B), and 5.6 µm (facies E).

The difference between the two lakes in the distinction between flood and background deposits can also be illustrated by examining the grain size of the different facies (Fig. 5.1). It can be observed that the mode of the background deposits in Lago Juncal (facies E, 5.6 µm) is finer than the modes of the direct river inflow deposit of Laguna Confluencia and the flood deposits in both lakes (facies A, C, and F, respectively; 6.3 µm), yet coarser than the mode of the background deposits in Laguna Confluencia (facies B, 4.9 µm; Fig. 5.1). This is interpreted to be the result of the high detrital content of the background sediment of Lago Juncal, making these deposits similar to the sediment that is deposited during flood events or during direct river inflow. The background sediment of Laguna Confluencia, on the other hand, has a relatively low detrital content and is observed to be finer grained than all the other lithological facies in the lakes. The background deposits in Lago Juncal are therefore suggested to be the result of a supply of coarse-grained material to the lake during enhanced inflow, yet also of the settling of finer-grained material during calm conditions.

The difference in the nature of the background deposits of the two lakes is interpreted to be the result of their hydrological settings. Both Laguna Confluencia and Lago Juncal share the characteristic of an outflow reversal during flood events, resulting in a high-energy inflow of sediment-laden water coming from Río Tranquilo and Río del Salto, respectively. This leads to the deposition of the detrital material

that characterises the proglacial flood layers identified in both lakes. Given that the background sediment in Lago Juncal is also detrital rich, it is interpreted to be the result of a sedimentation process comparable to that of flood layer deposition. It has been observed that the outflow reversal of Lago Juncal also occurs during non-flood conditions when the discharge, and consequently water level, of Río del Salto is high enough to allow the outflow stream of the lake to act as an inflow. It is therefore suggested that the detrital-rich background sediment is the result of the deposition of sediment carried by this inflow from Río del Salto. Since the discharge of Río del Salto is highest during austral spring and summer due to snowmelt (Dussaillant et al., 2012), the inflow from Río del Salto would most likely occur during this warm season. This regular inflow during non-flood conditions can also explain the large flood delta of Lago Juncal, which is over ten times larger than the flood delta of Laguna Confluencia (0.5 km2 and 0.04 km2, respectively; Fig. 2.3). Furthermore, organic-rich deposits as a result of lake bioproductivity are most likely diluted by the supply of detrital material coming from Río del Salto, and therefore not distinctive in the lake.

As explained above, the deposits in Lago Juncal are interpreted to be mainly the result of the supply of detrital material coming from Río del Salto, combined with the sediment coming from the two other inflow streams to the southwest and southeast of the lake. Since the flood deposits identified in the lake (facies F) show only small differences in detrital content and grain size compared to the background sediment (facies E), they are interpreted as the result of large floods that significantly enhanced the inflow coming from Río del Salto. Smaller floods would not increase the river discharge enough to result in the deposition of a detrital-rich flood layer that could be distinguished from the already detrital-rich background deposits. This weak contrast between background deposits and flood layers in Lago Juncal makes this lake less suitable to be used as a paleo-flood archive. For this thesis, Laguna Confluencia is better suited to reconstruct the flood frequency of proglacial river flooding, since it holds a record of detrital flood deposits that are distinctive from the organic-rich background sediment in the lake. From here on, the discussion will therefore solely focus on the flooding of Río Tranquilo as recorded in Laguna Confluencia, since river flooding of Río del Salto does not affect this lake.

The flood deposits in Laguna Confluencia are considered to be turbidites deposited by underflows in the lake formed during the inflow of sediment-laden water during floods. This turbidite deposition is assumed to only be the result of river flooding. Seismic activity is not expected to result in turbidite formation in the lake as the region is located south of the Chile Triple Junction, where seismicity is low as opposed to regions north of the triple junction (Hayes et al., 2015). Additionally, no earthquakes have been registered within 100 km of Laguna Confluencia during the past two millennia (USGS Earthquake Catalog, 2019).

5.3. Flooding of Río Tranquilo during the late Holocene

Between the top of the direct river inflow deposits (facies A) in CO18-01 and the top of the core, 54 distinctive flood deposits (facies C) have been identified (Fig. 4.1). Based on the sediment accumulation rate calculated for the upper part of CO18-01, these flood layers were deposited during the past +558 1220 (-540 ) years, i.e. after the suggested shift in river course of Río Tranquilo. The relatively low +0.035 accumulation rate in Laguna Confluencia (0.044 (-0.014 ) cm/year in CO18-01) is thought to be the result of a generally low sediment input from the small inflow streams in the southeast and the small ratio of catchment to lake area (~ 8:1). If it is assumed that the accumulation rate in the lake has remained constant over time, the deposition age of every flood deposit can be calculated. Given that they are considered as instantaneous deposits in terms of geological time, their thickness is not taken into account when calculating the age at a certain depth in the core. It has to be noted that the calculated accumulation rate is only an estimate of sediment accumulation in the lake, and that accumulation can vary over time. Better age dating, especially at the bottom of the core where errors are larger, is therefore needed to fine-tune the chronology of CO18-01.

Based on age calculation, it can be deduced that the 54 flood layers have been deposited between +540 798 (-558 ) and 2018 CE (Fig. 5.2a), with an average return period of 22 years. The flood frequency

ranges from 1 to 12 floods every 101 years, with an average of 4 (Fig. 5.2b). Five periods of high flood +395 frequency (≥ 4 floods per 101 years) can be identified, occurring from ~850 to 1125 (-408 ) CE, from +336 +258 +199 +174 +127 +90 1260 (-347 ) to 1435 (-267 ) CE, from 1569 (-205 ) to 1624 (-180 ) CE, from 1731 (-131 ) to 1815 (-93 ) CE, and +43 from 1920 (-45 ) to ~1970 CE (Fig. 5.2b). Given the large range of uncertainties on these ages, it must be emphasised that the relative age of the high flood frequency periods is poorly constrained. For the sake of simplicity, all high flood frequency periods will from hereon be mentioned without their errors and rounded to the nearest 10 years. The most notable period with a high flood frequency is from 1260 to 1440 CE, with 13 floods occurring during this period, having an average return period of 9 years.

FIGURE 5.2. (a) Flood occurrence and flood layer thickness in Laguna Confluencia during the past 1200 years (core CO18-01, this study) with the dashed line indicating median flood layer thickness (4 mm), (b) flood frequency in Laguna Confluencia based on a 101-year running sum, and (c) flood occurrence during the past 1200 years and flood layer thickness in the Río del Salto floodplain (core SL17-01; Rojas Aldana, 2018; Stammen, 2019). The blue bars represent periods of high flood frequency as recorded in Laguna Confluencia (≥ 4 floods per 101 years).

In addition to reconstructing flood frequency, sediment archives can also be used to estimate flood intensity. Indeed, flood layer thickness can be interpreted to represent the amount of sediment that was deposited during a flood and can therefore be used as a flood intensity proxy (Jenny et al., 2014; Schiefer et al., 2011; Wilhelm et al., 2015, 2012). In CO18-01, the thickness of facies C layers varies between 1 and 17 mm, with a median value of 4 mm (Fig. 5.2a). In addition, grain size has also been used as to assess flood intensity (e.g. Wilhelm et al., 2013, 2012). Based on the measured grain-size mode of the flood deposits (Fig. 4.1), it can be noted that the thickest flood layers generally have a coarser mode, indicating a possible positive relation between flood intensity, flood layer thickness, and grain-size. However, this relation remains uncertain for the entire flood record, since the finest flood layers were most likely not sampled for grain size due to the sampling resolution (5 mm). In the

reconstructed flood record, it can be seen that flood layers thicker than the median (4 mm) are more frequent in periods of high flood frequency, with the thickest flood layers occurring from 850 to 1130 CE, from 1260 to 1440 CE, and from 1920 to 1970 CE (Figs. 5.2a and b). These results therefore suggest that high-intensity floods are more common during periods of high-frequency flooding.

To verify the regional significance of these results, the CO18-01 record was compared to a flood record obtained from floodplain deposits along Río del Salto located ca. 3.9 km downstream of the confluence of Río del Salto and Río Tranquilo (core SL17-01; Fig. 2.1; Rojas Aldana, 2018; Stammen, 2019). Since Río Tranquilo is a tributary of Río del Salto, significant flooding of the former may also cause a flooding of the latter downstream of their confluence. Six flood events are recorded in floodplain core SL17-01 between 798 and 2017 CE (Fig. 5.2c). It can be observed that two out of the six flood events that occurred during the past 1200 years at the Río del Salto floodplain took place during a period of high flood frequency recorded in Laguna Confluencia from 1260 to 1440 CE (Figs. 5.2b and c). This might indicate that the high-intensity flood events of Río Tranquilo during this period also resulted in a significant rise in the discharge and water level of Río del Salto, leading to inundation of the floodplain. Four other flood events recorded in the del Salto floodplain do not fall within high flood frequency periods of Río Tranquilo. However, given the large age uncertainties of the high flood frequency periods, it might still be possible that these Río del Salto floods coincide with periods of increased flooding of Río Tranquilo. This might be the case for the Río del Salto floods recorded right after the high flood frequency periods of 1260–1440 CE and 1730–1820 CE (Figs. 5.2b and c). For the two Río del Salto floods that took place around 1700 CE, it can be suggested that these can also coincide with the high flood frequency period of 1730–1820 CE (Figs. 5.2b and c). Still, it can be concluded that the flood layers recorded in the del Salto floodplain are not necessarily the result of river flooding of Río Tranquilo but may also be caused by the flooding of Río del Salto alone.

5.4. Flood occurrence versus glacier variability of the San Lorenzo Icefield

The paleo-flood record presented in section 5.3 has allowed to reconstruct flood events of Río Tranquilo during the past 1200 years. As it has recently been suggested that glacier retreat might result in an increase in GLOF occurrence in Chilean Patagonia (Iribarren Anacona et al., 2015b), the relationship between proglacial floods in the Río Tranquilo valley and glacier dynamics of the San Lorenzo Icefield is therefore investigated in this discussion. In the following paragraphs, flood occurrence and, to a lesser extent, flood intensity (Figs. 5.3a and b), will be discussed in terms of glacier variability. Since no complete glacier variability reconstruction exists for the San Lorenzo Icefield, the neoglaciation scheme for the NPI and SPI according to Aniya (2013) and moraine ages of San Lorenzo glaciers (Aravena, 2007; Morano-Büchner and Aravena, 2013) are used to reconstruct glacier variability during the late Holocene (Fig. 5.3c). Two north-facing glaciers on Monte San Lorenzo, i.e. the Río Tranquilo and Arroyo San Lorenzo Glaciers, are of particular importance here, since they are the only glaciers of the San Lorenzo Icefield that add to the meltwater catchment of Río Tranquilo (Fig. 2.1).

Flood events in the Río Tranquilo valley have occurred during the last two neoglaciations, i.e. before 1200 CE (Neoglaciation IV) and during the 17th–19th century (Neoglaciation V), as well as in the subsequent periods without glacier advance (Figs. 5.3a and c). Most notably, the period of high flood frequency and intensity from 1260 to 1440 CE occurs after Neoglaciation IV (Figs. 5.3a, b and c). It can also be noted that the period after Neoglaciation V is marked by a high flood frequency and intensity, whereas during this glaciation, flood frequency and intensity are generally lower (Figs. 5.3a, b and c). The opposite regarding flood frequency seems to be the case for Neoglaciation IV, with a decrease in flood frequency towards the end of this glaciation, followed by a period of low flood frequency (Figs. 5.3b and c). However, since the flood record in Laguna Confluencia doesn’t cover the entire duration of Neoglaciation IV, no information is available regarding flood frequency during earlier stages of this glaciation. Also, due to the large age uncertainties of the high flood frequency periods in this part of the paleo-flood reconstruction, no clear relation with Neoglaciation IV can be determined. It can furthermore be remarked that the delineation of these neoglaciations is based on trends of the NPI and SPI, and that the glaciers of the San Lorenzo Icefield do not necessarily correspond to this.

FIGURE 5.3. Comparison between the flood reconstruction from Laguna Confluencia and glacier and climate variability in Chilean Patagonia during the past 1200 years. (a) Flood occurrence and layer thickness in CO18-01 (this study, dashed line: median thickness), (b) flood frequency in CO18-01 (101-year running sum), (c) neoglaciations of the NPI and SPI according to Aniya (2013), with indication of moraine ages of Monte San Lorenzo glaciers (Arroyo San Lorenzo (asterisk) and Río Tranquilo (circle); Aravena, 2007; Morano-Büchner and Aravena, 2013), (d) summer surface air temperature anomaly for 47.25°S, 72.25°W (Neukom et al., 2011; thick line: 31-point running average), (e) alkenone SST from core MD07-3093 on the southern Chilean margin (Collins et al., 2019), (f) alkenone SST from core CF7-PC33 in Jacaf fjord (Sepúlveda et al., 2009; thick line: 3-point running average), and (g) Fe/Al from core PC29A as proxy for changes in precipitation seasonality in Quitralco fjord (Bertrand et al., 2014). Blue bars represent periods of high flood frequency (≥ 4 floods per 101 years).

The dated moraine ages for the Río Tranquilo and Arroyo San Lorenzo Glaciers can be linked to Neoglaciation V (Fig. 5.3c). For the Río Tranquilo Glacier, minimum ages for moraine stabilisation are 1632, 1669, 1726, 1770, and 1873 CE, whereas for the Arroyo San Lorenzo Glacier, these are 1675, 1802, and 1820 CE (Aravena, 2007). However, another moraine of the Arroyo San Lorenzo Glacier has been dated to 1536 CE (Morano-Büchner and Aravena, 2013), which occurs prior to Neoglaciation V. Still, these moraine ages are suggested to represent maximum glacier advances and can give an indication of glacier variability of the San Lorenzo Icefield. Some of the glacier advances are followed by periods of high flood frequency, i.e. 1570–1620 CE, 1730–1820 CE and 1920–1970 CE (Figs 5.3b and c). This might suggest that high flood frequency periods in the Río Tranquilo valley have occurred during periods of glacier retreat of north-facing glaciers on Monte San Lorenzo. However, this association is not entirely solid, since glacier advances also occur during the high flood frequency period from 1730 to 1820 CE (Figs. 5.3b and c).

Generally, it seems that flood frequency increases after a neoglaciation or maximum glacier advance. This is the case for (a) the high flood frequency between 1260 and 1440 CE, which occurred after Neoglaciation IV, (b) the high flood frequency period between 1920 and 1970 CE, which occurred after Neoglaciation V and a maximum glacier advance of the Río Tranquilo Glacier, and (c) the high flood frequency between 1570 and 1620 CE, which occurred after a maximum glacier advance of the Arroyo San Lorenzo Glacier. It is therefore suggested that an increased meltwater release resulting from glacier retreat on the San Lorenzo Icefield can significantly increase the discharge of Río Tranquilo, leading to more frequent flood events. However, the high flood frequency periods from 850 to 1130 CE and from 1730 to 1820 CE both occurred during a neoglaciation and may therefore not be associated with glacier retreat on the San Lorenzo Icefield.

5.5. Flood occurrence versus climate variability in Chilean Patagonia

During the past 1200 years, Chilean Patagonia has experienced changes in temperature and precipitation (e.g. Bertrand et al., 2014; Neukom et al., 2011; Villalba et al., 2003). These two climatic factors are of importance to proglacial flood occurrence, since possible triggering mechanisms for river flooding include summer temperature and the amount of winter snowfall, which control the amount of snow/ice susceptible to melting, and the occurrence of heavy rainfall (e.g. Bøe et al., 2006; Støren et al., 2010). The flood occurrence and, to a lesser extent, flood intensity in the Río Tranquilo valley (Figs. 5.3a and b) will therefore also be discussed in terms of changes in temperature and precipitation.

5.5.1. Temperature reconstructions

By combining 22 proxies from natural and historical archives, Neukom et al. (2011) statistically reconstructed the summer (December–February) surface air temperature for South America south of 20°S back to 900 CE. This included the combination of tree ring, ice core, and lake and marine sediment records, as well as instrumental measurements. Their results have been combined in a 0.5° x 0.5° grid, which allows to spatially reconstruct surface air temperature anomalies with respect to the calibration period 1931–1995 CE. Such a temperature anomaly reconstruction for the Monte San Lorenzo area is depicted in Fig. 5.3d. Flood occurrence and intensity as observed in the paleo-flood record of Laguna Confluencia can thus be interpreted with regard to this temperature reconstruction (Figs. 5.3a, b and d).

In general, high flood frequency periods seem to coincide with relatively warmer episodes (Figs. 5.3b and d). However, some deviations from this apparent relationship can be observed, such as the high flood frequency period from 1260 to 1440 CE, which for a large part coincides with the strong cooling observed after 1350 CE (Figs 5.3b and d). The high flood frequency from 850 to 1130 CE also seems to show little significant variation during the cold spells of 1000 CE and 1120 CE observed in the temperature reconstruction (Figs. 5.3b and d). It has to be noted, however, that due to the large age uncertainty of the flood frequency periods, they can still coincide with warmer periods before the abovementioned cold periods. The occurrence of high-intensity floods seems to show no clear link to temperature variability, with floods of higher intensity occurring during periods of both warming, e.g. the 20th century, and cooling, e.g. the 14th century (Figs. 5.3a and d).

Other terrestrial temperature reconstructions in the region can differ from the trends observed by Neukom et al. (2011). For instance, a 600-year summer temperature record from lake sediments of Lago Plomo, located about 60 km NE of Laguna Confluencia (Elbert et al., 2015), indicates the presence of a warmer period during the 15th century (Elbert et al., 2015). This warm interval coincides partially with the high flood frequency period between 1260 and 1440 CE, therefore possibly confirming the positive relation between temperature and flood frequency for this time interval. However, other deviations from the temperature reconstruction by Neukom et al. (2011) do not show any relation to flood occurrence. For instance, Elbert et al. (2015) also indicate the presence of an 18th century colder period and a 19th century warmer period, which is opposite to the reconstruction by Neukom et al. (2011). Additionally, a 1600-year mean annual temperature record from Laguna Escondida (45°S; Elbert et al., 2013), shows the presence of a colder period between 1200 and 1450 CE and warmer periods culminating around 1480 CE, 1560 CE and 1680 CE. It can therefore be concluded that terrestrial temperature reconstructions in Chilean Patagonia can show significant differences from each other. Possible explanations for the discrepancies between them are the difference in reconstructed variables (summer versus annual temperature), the use of different proxies for temperature reconstruction (tree rings and instrumental records versus lake sediments), and the spatial variability in temperature.

Besides terrestrial temperature records, which often only cover short (multiple centuries) time scales, SST records can also give an indication of temperature variability in Chilean Patagonia (e.g. Collins et al., 2019; Haddam et al., 2018; Sepúlveda et al., 2009). Since the interpretation of terrestrial records may be complicated by their sensitivity to precipitation (Boninsegna et al., 2009), and differences between terrestrial records may reflect the spatial variability of temperature, SST records can be used to improve the spatial and temporal resolution of temperature reconstructions (Collins et al., 2019). Several alkenone SST reconstructions exist for Chilean Patagonia, including one by Collins et al. (2019) along the southern Chilean margin at 44°S (Fig. 5.3e), and one by Sepúlveda et al. (2009) from Jacaf fjord (44°S, Fig. 5.3f).

There is no clear link between the flood frequency of Río Tranquilo and SST variability as reconstructed by Collins et al. (2019), since periods of high flood frequency coincide with periods of both warmer and cooler SSTs (Figs. 5.3b and e). Of the five high flood frequency periods identified in this thesis, two occur during periods of decreasing SSTs (850–1130 CE and 1730–1820 CE), two occur during periods of cold SSTs (1260–1440 CE and 1920–1970 CE), and one occurs during a period of high SSTs (1570– 1620 CE). Still, it may be noted that the decrease in flood frequency until 1260 CE corresponds to a SST cooling (Figs. 5.3b and e). Like high-frequency flooding, high-intensity flooding also occurs during both warmer and coolers SST periods (Figs 5.3a and e).

An obvious link, however, exists between the flood frequency of Río Tranquilo and the fjord SST reconstruction by Sepúlveda et al. (2009), i.e. periods of high flood frequency coincide with periods of relatively warmer fjord SST (Figs. 5.3b and f). This coupling cannot be confirmed for the most recent high flood frequency period (1920–1970 CE), since the SST reconstruction does not go beyond 1874 CE. Nevertheless, flood frequency thus seems to relate better with the Jacaf fjord SST reconstruction than with the Chilean margin SST reconstruction. Most noteworthy is that the period between 1260 and 1440 CE, which is characterised by the highest flood frequency of the entire record, coincides with a strong warming trend observed in the fjord SST reconstruction (Figs. 5.3b and f), which is contradictory to the cold period observed by Collins et al. (2019; Figs 5.3b and e). High-intensity floods are also shown to be more frequent during warmer periods in the fjord SST reconstruction whereas they also occur during colder periods in the reconstruction by Collins et al. (2019).

A possible explanation for the better link with fjord SST than with open ocean SST may be that fjord systems record mainly continental changes due to a variability in precipitation, in this case related to variations in the position of the SWWB, whereas open ocean sites are also influenced by ocean currents, in this case the Antarctic Circumpolar Current (Sepúlveda et al., 2009). It therefore seems that in Chilean Patagonia, fjord SST changes can also give a clue of temperature and/or precipitation changes on the continent. When comparing the SST reconstruction with the terrestrial temperature reconstruction by

Neukom et al. (2011), it can be observed that warm periods in the two reconstructions are to a certain degree co-occurring (Figs. 5.3d and f). This is not the case, however, for the 13th century, which is a cold period in the fjord SST reconstruction, but a characteristic warm period in the terrestrial temperature reconstruction (Figs 5.3d and f). Given the lack of data from Chilean Patagonia used by Neukom et al. (2011) in his terrestrial temperature reconstruction, the fjord SST reconstruction by Sepúlveda et al. (2009) is presumed to better represent temperature changes in the Monte San Lorenzo region and is therefore better suited to investigate the relation between flood occurrence and temperature. The positive link between a high flood frequency and a warm climate is thought to be the result of an increased supply of meltwater coming from snowmelt and/or retreating glaciers due to higher temperatures

5.5.2. Precipitation reconstruction

Precipitation is a second climatic factor that can be considered when discussing factors influencing the occurrence of proglacial floods in Chilean Patagonia, especially since it has proven to be one of the main drivers of Patagonian glacier variability (e.g. Bertrand et al., 2012). A reconstruction of changes in precipitation seasonality from Quitralco fjord (46°S; Bertrand et al., 2014) will be used to investigate a possible relation with flood occurrence in the Río Tranquilo valley. This reconstruction indicates that changes in Fe/Al reflect changes in the occurrence of seasonal floods, which is interpreted as changes in precipitation seasonality due to variations in the latitudinal position of the SWWB (Bertrand et al., 2014; Fig. 5.3g). Most notably, a decrease in precipitation seasonality, i.e. an increase in year-round precipitation, between 1200 and 1500 CE has been observed on several reconstructions during the last millennium, which is related to a northward movement of the SWWB (Bertrand et al., 2014). Other precipitation reconstructions based on pollen records exist in the region (e.g. Villa-Martínez et al., 2012) but show longer-term reconstructions that are rather insensitive to the relatively small precipitation changes that occurred during the Holocene.

The occurrence of floods in the Río Tranquilo valley shows no clear link to long-term changes in precipitation, except for a weak positive relation between flood frequency and precipitation seasonality from 850 to 1130 CE (Figs. 5.3a, b and g). It can also be noted that the period of highest flood occurrence (1260–1440 CE) corresponds to a period of year-round precipitation increase due to a northward shift of the SWWB (Bertrand et al., 2014). The following two periods of high flood frequency (1570–1620 CE and 1730–1820 CE) occur during a period of high year-round precipitation (1500–1950 CE, Figs. 5.3b and g). However, some intermediate periods of low flood frequency also occur during this period of high year-round precipitation. High-intensity floods appear to occur during periods of change in precipitation seasonality (1260–1440 CE and 1920–1970 CE, Figs. 5.3a and g). This relationship is not always solid, however, with high-intensity floods also occurring during periods marked by more stable precipitation regimes. Nevertheless, increased year-round precipitation is suggested to affect river discharge through increased rainfall or snowfall susceptible to melting, resulting in increased river flooding. Since the relation between flood frequency and year-round precipitation is not clear for the entire flood record, other factors might have to act in combination with an increased precipitation to result in increased flood occurrence.

5.6. Triggering of Río Tranquilo flooding during the past 1200 years

Based on the discussion in the previous sections, some comments can be made regarding flood occurrence in the Río Tranquilo valley and the general glacial and climatic trends responsible for these floods.

The increase in proglacial river flooding during glacier retreat suggests a connection between proglacial flood occurrence and glacier variability, i.e. more meltwater is generated when the mass balance of glaciers is negative, which in turn seems to occur during warmer periods. Increased meltwater production related to glacier retreat on the San Lorenzo Icefield may significantly enhance the discharge of Río Tranquilo and cause flood events. An increased snow/ice melt elsewhere in the Río Tranquilo catchment due to higher temperatures may also trigger river flooding. Furthermore, the increased

meltwater production from the San Lorenzo Icefield can result in proglacial lake formation if the meltwater is captured in a dammed basin. Floods in the Río Tranquilo valley may therefore also be linked to proglacial lakes in front of the north-facing glaciers of the San Lorenzo Icefield. The sudden release of this meltwater from the lake basin, triggered by dam instability, could then increase the discharge of Río Tranquilo significantly to induce a flood event, more specifically a GLOF. Depending on the volume of collected meltwater present in the proglacial lakes before dam failure, such GLOFs may be especially high magnitude floods. Both a direct increased meltwater release into Río Tranquilo triggered by warmer temperatures and the emptying of proglacial lakes are therefore suggested to be the main triggering mechanisms behind flood occurrence in the Río Tranquilo valley.

Precipitation changes, however, may also induce flood events in combination with changes in temperature. The remarkably high flood frequency period from 1260 to 1440 CE, for instance, is suggested to be the result of increased year-round precipitation combined with increased meltwater release. It is suggested that precipitation alone is not a determining factor for flood occurrence, since no apparent relationship exists between precipitation and flood frequency that is valid for the entire flood record. Periods of increased rainfall are therefore not considered to be a main cause behind the flooding of Río Tranquilo.

6. Conclusions

This thesis aimed to reconstruct proglacial river flooding in Chilean Patagonia during the late Holocene. Lake sediments were used as a paleo-flood archive to investigate the flooding of two rivers that receive meltwater from glaciers of the San Lorenzo icefield, i.e. Río Tranquilo and Río del Salto. For each proglacial river, a lake receiving meltwater only during proglacial floods was selected, i.e. Laguna Confluencia and Lago Juncal, respectively. Overall, this thesis contained four research objectives, which are evaluated in the following paragraphs:

1. In Laguna Confluencia and Lago Juncal, flood deposits were mainly distinguished from background sediments based on higher X-ray attenuation, density, magnetic susceptibility, and detrital element counts, low organic content, and coarser grain size. These flood layer characteristics are attributed to the increased discharge and higher suspended sediment concentration of the proglacial rivers during flooding, which results in higher-energy conditions during sediment transport in the lake compared to the calm conditions represented by background sedimentation.

2. The distinction between flood and background deposits was investigated and shown to be the clearest in Laguna Confluencia, in which background sediments were organic-rich compared to the detrital-rich flood deposits. In Lago Juncal, this difference was less evident due to the high detrital content of the background sediment, most likely related to the inflow of sediment-laden water from Río del Salto in Lago Juncal during non-flood conditions. Lago Juncal was therefore not used for flood reconstruction in this thesis.

3. Fifty-four flood layers identified in core CO18-01 allowed to reconstruct river flooding of Río Tranquilo during the past 1200 years. Five periods of high flood frequency were identified, i.e. 850–1130 CE, 1260–1440 CE, 1570–1620 CE, 1730–1820 CE, and 1920–1970 CE. However, due to large dating uncertainties, the exact age of these high flood frequency periods remains imprecise, especially towards the base of the record.

4. River flooding was shown to increase after glacier advances, suggesting that flood events are related to meltwater production during glacier retreat. The main causes for river flooding are hypothesised to be increased snow/ice melt during climate warming or the sudden drainage of proglacial lakes, i.e. GLOFs. Precipitation was observed to have only a limited influence on flood frequency but could still affect flood frequency in combination with temperature. Glacier dynamics of the San Lorenzo Icefield are therefore suggested to be controlled by changes in temperature rather than precipitation.

This research demonstrated the value of lake sediments as paleo-flood archives in Chilean Patagonia. Although this study has shown clear relations between flood occurrence and glacier and climate variability, our conclusions are limited by the precision of the chronology of sediment core CO18-01. Further dating techniques, e.g. 14C dating, should therefore be applied to construct a more robust age- depth model and ultimately assess the glaciological and climatological triggers of San Lorenzo proglacial floods more precisely.

This research has also shown that lake sediments have the potential to be used for flood risk assessment in the region, which is beneficial for local communities and government agencies alike. In future research, when selecting lakes that potentially hold a paleo-flood record, two factors are to be considered: (1) changes in the hydrological setting, which characterises the lake in/outflow during flooding and non-flooding, and (2) the amount of vegetation in the lake catchment, which affects the organic content of the background sediment.

Further research on flood occurrence in the Monte San Lorenzo region could include the use of river gauging stations to monitor floods. Given the link with temperature and glacier retreat, the occurrence of GLOFs can also require further investigation. Proglacial lakes have been forming in front of the glaciers of the San Lorenzo Icefield and may therefore be susceptible to sudden outburst caused by

dam failure. The investigation of the changes in size of these lakes, and the evaluation of their outburst potential, can aid in the assessment of flood risks.

As it shown in this research that there is an apparent link between flood occurrence and temperature, flooding in Río Tranquilo is not expected to decrease in the near future given the anomalously warm conditions of the past century. This is further supported by the increasing flood frequency towards the end of the reconstruction. Paleo-flood research should therefore be encouraged in Chilean Patagonia to improve the understanding of flood occurrence in the context of glacier and climate variability.

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8. Appendices

Appendix A: Measured nuclides in C013. The numbers in parentheses are the measured errors.

Depth (cm) 210Pb 226Ra unsupported 210Pb 137Cs K (mBq/g) (mBq/g) (mBq/g) 228Th (mBq/g) (mBq/g) (%) 0.0 - 1.0 102 (11) 59 (3) 43 (12) 28 (2) 3.3 (1.0) 3.8% 1.0 - 2.0 57 (7) 29 (2) 29 (8) 26 (1) 2.9 (0.6) 6.7% 2.0 - 3.0 163 (16) 37 (2) 126 (16) 30 (2) 49.2 (2.8) 6.9% 3.0 - 4.0 101 (10) 35 (2) 67 (10) 35 (2) 33.1 (1.9) 6.7% 4.0 - 5.0 53 (5) 49 (1) 5 (5) 51 (1) 8.6 (0.6) 12.0% 6.0 - 7.0 51 (6) 55 (2) -4 (6) 64 (1) 0.5 (0.5) 14.4% 8.0 - 9.0 49 (5) 57 (1) -8 (5) 69 (1) 0.1 (0.3) 16.2% 10.0 - 11.0 58 (5) 59 (1) -1 (5) 69 (1) -0.4 (0.3) 15.1%

Appendix B1: Correlation matrix for single-element counts and scattering ratio in CO18-01, with Pearson correlation coefficient (r) and two-tailed test of significance (p); p ≥ 0.05 is indicated with ***, 0.01 < p < 0.05 is indicated with **, 0.001 < p < 0.01 is indicated with *, p < 0.001 is indicated with no asterisks.

CO18-01 Al Si P S Cl Ar K Ca Ti V Cr Mn Fe Ni Cu Zn Ga Br Rb Sr Y Zr Ba Gd Tb Tm Pb Inc/Coh

Al 1 0.77 0.09* -0.27 0.11 0.28 0.76 0.53 0.74 0.65 0.42 -0.04*** -0.07** 0.33 0.43 0.6 0.59 -0.65 0.68 0.53 0.6 0.59 0.44 -0.01*** -0.15 -0.2 0.3 -0.67

Si 0.77 1 -0.04*** -0.42 0.2 0.48 0.93 0.84 0.94 0.67 0.28 -0.15 -0.33 0.36 0.51 0.84 0.81 -0.8 0.86 0.85 0.81 0.87 0.5 -0.02*** -0.24 -0.25 0.38 -0.89

P 0.09* -0.04*** 1 0.15 0.07** -0.06** -0.05*** -0.07** -0.06*** 0.02*** 0.08* -0.01** 0.27 0*** -0.1* -0.11 -0.07** 0*** -0.05*** -0.07** -0.03*** -0.06** 0.09 -0.03*** 0.06** 0.07** 0.11 0.02***

S -0.27 -0.42 0.15 1 0.07** -0.25 -0.38 -0.41 -0.4 -0.23 -0.02*** -0.06** 0.23 0.01*** -0.24 -0.38 -0.35 0.37 -0.35 -0.37 -0.31 -0.37 0.03 -0.06 0.01 0.22 -0.04*** 0.38

Cl 0.11 0.2 0.07** 0.07** 1 0.23 0.18 0.2 0.18 0.08** -0.03*** -0.14 -0.25 0.1 0.18 0.24 0.21 -0.11 0.19 0.22 0.18 0.21 0.06 -0.06 -0.22 -0.1* 0.04*** -0.17

Ar 0.28 0.48 -0.06** -0.25 0.23 1 0.49 0.46 0.52 0.29 0.07** -0.18 -0.48 0.11 0.4 0.57 0.56 -0.52 0.56 0.51 0.51 0.52 0.14 -0.05 -0.37 -0.22 0.12 -0.57

K 0.76 0.93 -0.05*** -0.38 0.18 0.49 1 0.68 0.97 0.82 0.52 -0.17 -0.34 0.44 0.63 0.88 0.86 -0.82 0.95 0.72 0.81 0.79 0.53 -0.02*** -0.29 -0.27 0.33 -0.86

Ca 0.53 0.84 -0.07** -0.41 0.2 0.46 0.68 1 0.76 0.33 -0.13 -0.01*** -0.32 0.17 0.31 0.73 0.67 -0.7 0.65 0.95 0.68 0.84 0.4 0.03*** -0.14 -0.24 0.43 -0.84

Ti 0.74 0.94 -0.06*** -0.4 0.18 0.52 0.97 0.76 1 0.79 0.43 -0.19 -0.38 0.45 0.6 0.89 0.87 -0.83 0.95 0.81 0.85 0.87 0.55 -0.03*** -0.28 -0.27 0.37 -0.9

V 0.65 0.67 0.02*** -0.23 0.08** 0.29 0.82 0.33 0.79 1 0.67 -0.11 -0.1 0.48 0.55 0.64 0.63 -0.64 0.79 0.39 0.64 0.54 0.54 0.01*** -0.21 -0.17 0.28 -0.65

Cr 0.42 0.28 0.08* -0.02*** -0.03*** 0.07** 0.52 -0.13 0.43 0.67 1 -0.24 0.09* 0.39 0.38 0.3 0.34 -0.34 0.5 -0.04*** 0.32 0.15 0.35 -0.1 -0.11 -0.04*** 0.07** -0.25

Mn -0.04*** -0.15 -0.01** -0.06** -0.14 -0.18 -0.17 -0.01*** -0.19 -0.11 -0.24 1 0.2 -0.25 0.02*** -0.24 -0.27 0.09* -0.29 -0.22 -0.3 -0.28 -0.1 0.47 0.18 -0.32 -0.03*** 0.08**

Fe -0.07** -0.33 0.27 0.23 -0.25 -0.48 -0.34 -0.32 -0.38 -0.1 0.09* 0.2 1 -0.22 -0.54 -0.54 -0.5 0.1 -0.4 -0.37 -0.31 -0.43 0.15 0*** 0.47 0.33 0.3 0.18

Ni 0.33 0.36 0*** 0.01*** 0.1 0.11 0.44 0.17 0.45 0.48 0.39 -0.25 -0.22 1 0.36 0.44 0.37 -0.28 0.46 0.27 0.42 0.36 0.32 -0.01*** -0.42 0.1* 0.38 -0.31

Cu 0.43 0.51 -0.1* -0.24 0.18 0.4 0.63 0.31 0.6 0.55 0.38 0.02*** -0.54 0.36 1 0.67 0.62 -0.38 0.64 0.32 0.45 0.46 0.21 0.02*** -0.33 -0.67 -0.04*** -0.43

Zn 0.6 0.84 -0.11 -0.38 0.24 0.57 0.88 0.73 0.89 0.64 0.3 -0.24 -0.54 0.44 0.67 1 0.86 -0.73 0.91 0.78 0.79 0.8 0.43 -0.06** -0.32 -0.31 0.34 -0.82

Ga 0.59 0.81 -0.07** -0.35 0.21 0.56 0.86 0.67 0.87 0.63 0.34 -0.27 -0.5 0.37 0.62 0.86 1 -0.73 0.89 0.75 0.8 0.82 0.45 -0.06*** -0.32 -0.3 0.24 -0.77

Br -0.65 -0.8 0*** 0.37 -0.11 -0.52 -0.82 -0.7 -0.83 -0.64 -0.34 0.09* 0.1 -0.28 -0.38 -0.73 -0.73 1 -0.81 -0.74 -0.75 -0.73 -0.55 0.02*** 0.11 0.17 -0.48 0.89

Rb 0.68 0.86 -0.05*** -0.35 0.19 0.56 0.95 0.65 0.95 0.79 0.5 -0.29 -0.4 0.46 0.64 0.91 0.89 -0.81 1 0.74 0.85 0.83 0.55 -0.08** -0.3 -0.26 0.34 -0.86

Sr 0.53 0.85 -0.07** -0.37 0.22 0.51 0.72 0.95 0.81 0.39 -0.04*** -0.22 -0.37 0.27 0.32 0.78 0.75 -0.74 0.74 1 0.79 0.91 0.48 -0.05*** -0.18 -0.18 0.46 -0.87

Y 0.6 0.81 -0.03*** -0.31 0.18 0.51 0.81 0.68 0.85 0.64 0.32 -0.3 -0.31 0.42 0.45 0.79 0.8 -0.75 0.85 0.79 1 0.86 0.55 -0.07** -0.23 -0.16 0.41 -0.82

Zr 0.59 0.87 -0.06** -0.37 0.21 0.52 0.79 0.84 0.87 0.54 0.15 -0.28 -0.43 0.36 0.46 0.8 0.82 -0.73 0.83 0.91 0.86 1 0.48 -0.06** -0.27 -0.24 0.36 -0.85

Ba 0.44 0.5 0.09 0.03 0.06 0.14 0.53 0.4 0.55 0.54 0.35 -0.1 0.15 0.32 0.21 0.43 0.45 -0.55 0.55 0.48 0.55 0.48 1 -0.03*** 0.05*** -0.03*** 0.49 -0.59

Gd -0.01*** -0.02*** -0.03*** -0.06 -0.06 -0.05 -0.02*** 0.03*** -0.03*** 0.01*** -0.1 0.47 0*** -0.01*** 0.02*** -0.06** -0.06*** 0.02*** -0.08** -0.05*** -0.07** -0.06** -0.03*** 1 -0.04*** -0.1* -0.02*** -0.01***

Tb -0.15 -0.24 0.06** 0.01 -0.22 -0.37 -0.29 -0.14 -0.28 -0.21 -0.11 0.18 0.47 -0.42 -0.33 -0.32 -0.32 0.11 -0.3 -0.18 -0.23 -0.27 0.05*** -0.04*** 1 -0.07** 0.17 0.17

Tm -0.2 -0.25 0.07** 0.22 -0.1* -0.22 -0.27 -0.24 -0.27 -0.17 -0.04*** -0.32 0.33 0.1* -0.67 -0.31 -0.3 0.17 -0.26 -0.18 -0.16 -0.24 -0.03*** -0.1* -0.07** 1 0.12 0.21

Pb 0.3 0.38 0.11 -0.04*** 0.04*** 0.12 0.33 0.43 0.37 0.28 0.07** -0.03*** 0.3 0.38 -0.04*** 0.34 0.24 -0.48 0.34 0.46 0.41 0.36 0.49 -0.02*** 0.17 0.12 1 -0.58

Inc/Coh -0.67 -0.89 0.02*** 0.38 -0.17 -0.57 -0.86 -0.84 -0.9 -0.65 -0.25 0.08** 0.18 -0.31 -0.43 -0.82 -0.77 0.89 -0.86 -0.87 -0.82 -0.85 -0.59 -0.01*** 0.17 0.21 -0.58 1

Appendix B2: Correlation matrix for single-element counts and scattering ratio in JU18-02, with Pearson correlation coefficient (r) and two-tailed test of significance (p); p ≥ 0.05 is indicated with ***, 0.01 < p < 0.05 is indicated with **, 0.001 < p < 0.01 is indicated with *, p < 0.001 is indicated with no asterisks.

JU18-02 Al Si P S Cl Ar K Ca Ti V Cr Mn Fe Ni Cu Zn Ga Br Rb Sr Y Zr Ba Gd Tb Tm Pb Inc/Coh

Al 1 0.84 0.24 -0.05*** 0.04*** 0.26 0.82 0.7 0.75 0.37 0.36 -0.2 0.37 0.24 0.09* 0.26 0.32 -0.24 0.4 0.41 0.44 0.46 0.58 -0.17 0.14 -0.05*** 0.27 0.01***

Si 0.84 1 0.17 -0.14 -0.02*** 0.26 0.88 0.87 0.84 0.32 0.29 -0.28 0.27 0.1 0.06*** 0.12 0.33 -0.3 0.32 0.46 0.52 0.64 0.69 -0.22 0.08* -0.06** 0.19 -0.13

P 0.24 0.17 1 0.25 0.17 0.1* 0.24 0.09* 0.25 0.2 0.17 -0.15 0.13 0.35 0.15 0.28 0.28 -0.21 0.26 0.12 0.21 0.1* 0.12 -0.21 -0.07** -0.06** 0.3 0.08*

S -0.05*** -0.14 0.25 1 0.28 0.07** -0.02*** -0.17 -0.01*** 0.13 0.14 0.01*** 0.1* 0.34 0.16 0.27 0.16 -0.03*** 0.19 0.04*** 0.05*** -0.13 -0.09* -0.13 -0.19 0.01*** 0.21 0.19

Cl 0.04*** -0.02*** 0.17 0.28 1 0.11 0.07** -0.06*** 0.07** 0.14 0.13 -0.07** 0.07** 0.27 0.18 0.24 0.16 -0.09* 0.19 0.08** 0.09* -0.02*** 0.02*** -0.17 -0.15 -0.03*** 0.17 0.14

Ar 0.26 0.26 0.1* 0.07** 0.11 1 0.4 -0.05*** 0.47 0.33 0.37 -0.02*** 0.43 0.06*** 0.18 0.36 0.33 0.01*** 0.6 0.65 0.48 0.5 -0.04*** -0.05*** 0.2 0.12 -0.09* 0.67

K 0.82 0.88 0.24 -0.02*** 0.07** 0.4 1 0.79 0.95 0.6 0.57 -0.22 0.59 0.35 0.18 0.48 0.49 -0.24 0.66 0.58 0.56 0.55 0.73 -0.18 0.16 0*** 0.3 0.18

Ca 0.7 0.87 0.09* -0.17 -0.06*** -0.05*** 0.79 1 0.84 0.15 0.07** -0.25 0.11 0.01*** -0.04*** -0.06*** 0.23 -0.3 0.14 0.66 0.41 0.58 0.61 -0.2 0*** -0.06*** 0.12 -0.67

Ti 0.75 0.84 0.25 -0.01*** 0.07** 0.47 0.95 0.84 1 0.62 0.56 -0.27 0.6 0.34 0.22 0.51 0.55 -0.25 0.71 0.71 0.63 0.69 0.72 -0.22 0.16 0.02*** 0.25 0.25

V 0.37 0.32 0.2 0.13 0.14 0.33 0.6 0.15 0.62 1 0.53 -0.02*** 0.7 0.42 0.23 0.65 0.39 0.01*** 0.7 0.47 0.33 0.25 0.21 -0.05*** 0.09* 0.14 0.21 0.45

Cr 0.36 0.29 0.17 0.14 0.13 0.37 0.57 0.07** 0.56 0.53 1 0*** 0.69 0.34 0.24 0.57 0.3 0.09* 0.63 0.39 0.3 0.22 0.34 -0.03*** 0.15 0.09* 0.14 0.46

Mn -0.2 -0.28 -0.15 0.01*** -0.07** -0.02*** -0.22 -0.25 -0.27 -0.02*** 0*** 1 0.18 -0.13 0.13 0.06*** -0.33 0.54 -0.2 -0.08** -0.36 -0.29 -0.28 0.72 -0.03*** -0.07** -0.15 0.39

Fe 0.37 0.27 0.13 0.1* 0.07** 0.43 0.59 0.11 0.6 0.7 0.69 0.18 1 0.43 0.14 0.65 0.18 0.33 0.67 0.52 0.27 0.15 0.21 0.14 0.19 0.23 0.29 0.65

Ni 0.24 0.1 0.35 0.34 0.27 0.06*** 0.35 0.01*** 0.34 0.42 0.34 -0.13 0.43 1 0.19 0.64 0.39 -0.12 0.47 0.15 0.21 -0.15 0.01*** -0.17 -0.15 0.09* 0.78 0.25

Cu 0.09* 0.06*** 0.15 0.16 0.18 0.18 0.18 -0.04*** 0.22 0.23 0.24 0.13 0.14 0.19 1 0.45 0.28 -0.03*** 0.33 0.21 0.13 0.16 0.19 0*** 0.05*** -0.55 0.02*** 0.32

Zn 0.26 0.12 0.28 0.27 0.24 0.36 0.48 -0.06*** 0.51 0.65 0.57 0.06*** 0.65 0.64 0.45 1 0.54 0*** 0.81 0.51 0.29 0.09* 0.09* -0.03*** 0.2 0*** 0.36 0.67

Ga 0.32 0.33 0.28 0.16 0.16 0.33 0.49 0.23 0.55 0.39 0.3 -0.33 0.18 0.39 0.28 0.54 1 -0.41 0.63 0.46 0.43 0.4 0.24 -0.31 0.12 -0.08** 0.23 0.24

Br -0.24 -0.3 -0.21 -0.03*** -0.09* 0.01*** -0.24 -0.3 -0.25 0.01*** 0.09* 0.54 0.33 -0.12 -0.03*** 0*** -0.41 1 -0.15 -0.04*** -0.25 -0.25 -0.29 0.44 -0.01*** 0.14 -0.18 0.37

Rb 0.4 0.32 0.26 0.19 0.19 0.6 0.66 0.14 0.71 0.7 0.63 -0.2 0.67 0.47 0.33 0.81 0.63 -0.15 1 0.68 0.48 0.41 0.34 -0.19 0.27 0.06*** 0.2 0.62

Sr 0.41 0.46 0.12 0.04*** 0.08** 0.65 0.58 0.66 0.71 0.47 0.39 -0.08** 0.52 0.15 0.21 0.51 0.46 -0.04*** 0.68 1 0.54 0.66 0.28 -0.11 0.19 0.12 -0.06*** 0.56

Y 0.44 0.52 0.21 0.05*** 0.09* 0.48 0.56 0.41 0.63 0.33 0.3 -0.36 0.27 0.21 0.13 0.29 0.43 -0.25 0.48 0.54 1 0.56 0.31 -0.3 0.03*** 0.07** 0.23 0.19

Zr 0.46 0.64 0.1* -0.13 -0.02*** 0.5 0.55 0.58 0.69 0.25 0.22 -0.29 0.15 -0.15 0.16 0.09* 0.4 -0.25 0.41 0.66 0.56 1 0.42 -0.23 0.08** -0.01*** -0.2 0.13

Ba 0.58 0.69 0.12 -0.09* 0.02*** -0.04*** 0.73 0.61 0.72 0.21 0.34 -0.28 0.21 0.01*** 0.19 0.09* 0.24 -0.29 0.34 0.28 0.31 0.42 1 -0.25 0.19 -0.29 0.08** -0.6

Gd -0.17 -0.22 -0.21 -0.13 -0.17 -0.05*** -0.18 -0.2 -0.22 -0.05*** -0.03*** 0.72 0.14 -0.17 0*** -0.03*** -0.31 0.44 -0.19 -0.11 -0.3 -0.23 -0.25 1 0.1* 0.01*** -0.12 0.25

Tb 0.14 0.08* -0.07** -0.19 -0.15 0.2 0.16 0*** 0.16 0.09* 0.15 -0.03*** 0.19 -0.15 0.05*** 0.2 0.12 -0.01*** 0.27 0.19 0.03*** 0.08** 0.19 0.1* 1 -0.31 -0.05*** 0.22

Tm -0.05*** -0.06** -0.06** 0.01*** -0.03*** 0.12 0*** -0.06*** 0.02*** 0.14 0.09* -0.07** 0.23 0.09* -0.55 0*** -0.08** 0.14 0.06*** 0.12 0.07** -0.01*** -0.29 0.01*** -0.31 1 -0.02*** 0.13

Pb 0.27 0.19 0.3 0.21 0.17 -0.09* 0.3 0.12 0.25 0.21 0.14 -0.15 0.29 0.78 0.02*** 0.36 0.23 -0.18 0.2 -0.06*** 0.23 -0.2 0.08** -0.12 -0.05*** -0.02*** 1 -0.01***

Inc/Coh 0.01*** -0.13 0.08* 0.19 0.14 0.67 0.18 -0.67 0.25 0.45 0.46 0.39 0.65 0.25 0.32 0.67 0.24 0.37 0.62 0.56 0.19 0.13 -0.6 0.25 0.22 0.13 -0.01*** 1

Appendix C1: Correlation matrix for elemental ratios, log-ratios and single-element counts and measured grain-size mode in CO18-01, with Pearson correlation coefficient (r) and two-tailed test of significance (p); p ≥ 0.05 is indicated with ***, 0.01 < p < 0.05 is indicated with **, 0.001 < p < 0.01 is indicated with *, p < 0.001 is indicated with no asterisks.

CO18-01 Si/Ti Zr/Ti Si/K Si/Rb Zr/K Zr/Rb Ti/K Ti/Rb log(Si/Ti) log(Zr/Ti) log(Si/K) log(Si/Rb) log(Zr/K) log(Zr/Rb) log(Ti/K) log(Ti/Rb) Si Zr Ti K Rb Mode

Si/Ti 1 0.2* 0.24 0.25 0.06*** 0.4 -0.46 -0.24 0.65 0.5 0.69 -0.24 0.36 0.34 0.1*** -0.43 0.34 0.46 0.25 0.26 0.38 0.3

Zr/Ti 0.2* 1 -0.33 -0.28 -0.05*** 0.29 -0.68 -0.54 0.63 0.81 0.69 0.1*** 0.4 0.2* 0.35 -0.9 -0.14** 0.18* -0.2* -0.21* 0.04*** 0***

Si/K 0.24 -0.33 1 0.88 0.27 0.48 0.29 0.38 0.41 -0.07*** 0.34 0.59 0.24 0.48 -0.1*** 0.37 0.89 0.72 0.81 0.8 0.71 0.7

Si/Rb 0.25 -0.28 0.88 1 -0.11*** 0.32 0.16** 0.63 0.43 -0.19* 0.33 0.74 -0.05*** 0.32 -0.19* 0.44 0.87 0.58 0.79 0.79 0.6 0.6

Zr/K 0.06*** -0.05*** 0.27 -0.11*** 1 0.37 0.16** -0.33 0.08*** 0.31 0.1*** -0.27 0.58 0.32 0.13** -0.2* 0.02*** 0.26 -0.02*** -0.04*** 0.13*** 0.17**

Zr/Rb 0.4 0.29 0.48 0.32 0.37 1 0.04*** -0.12*** 0.53 0.7 0.63 0.19* 0.9 0.97 0.52 -0.31 0.55 0.84 0.47 0.41 0.55 0.63

Ti/K -0.46 -0.68 0.29 0.16** 0.16** 0.04*** 1 0.6 -0.65 -0.53 -0.56 0.03*** -0.06*** 0.1*** 0.33 0.74 0.09*** -0.04*** 0.16** 0.07*** -0.08*** 0.15**

Ti/Rb -0.24 -0.54 0.38 0.63 -0.33 -0.12*** 0.6 1 -0.29 -0.65 -0.3 0.48 -0.46 -0.08*** 0.01*** 0.78 0.32 -0.03*** 0.35 0.32 0*** 0.16**

log(Si/Ti) 0.65 0.63 0.41 0.43 0.08*** 0.53 -0.65 -0.29 1 0.69 0.96 0.39 0.45 0.45 0.03*** -0.61 0.54 0.68 0.43 0.45 0.6 0.46

log(Zr/Ti) 0.5 0.81 -0.07*** -0.19* 0.31 0.7 -0.53 -0.65 0.69 1 0.79 -0.11*** 0.83 0.64 0.46 -0.89 0.08*** 0.52 0*** -0.02*** 0.29 0.25

log(Si/K) 0.69 0.69 0.34 0.33 0.1*** 0.63 -0.56 -0.3 0.96 0.79 1 0.28 0.57 0.54 0.29 -0.68 0.48 0.69 0.37 0.35 0.55 0.48

log(Si/Rb) -0.24 0.1*** 0.59 0.74 -0.27 0.19* 0.03*** 0.48 0.39 -0.11*** 0.28 1 -0.11*** 0.19* -0.15** 0.26 0.63 0.42 0.6 0.6 0.46 0.43

log(Zr/K) 0.36 0.4 0.24 -0.05*** 0.58 0.9 -0.06*** -0.46 0.45 0.83 0.57 -0.11*** 1 0.88 0.53 -0.53 0.26 0.68 0.21* 0.15** 0.41 0.44

log(Zr/Rb) 0.34 0.2* 0.48 0.32 0.32 0.97 0.1*** -0.08*** 0.45 0.64 0.54 0.19* 0.88 1 0.46 -0.21* 0.55 0.81 0.5 0.44 0.56 0.6

log(Ti/K) 0.1*** 0.35 -0.1*** -0.19* 0.13** 0.52 0.33 0.01*** 0.03*** 0.46 0.29 -0.15** 0.53 0.46 1 -0.31 -0.07*** 0.23 -0.06*** -0.19* -0.04*** 0.22*

log(Ti/Rb) -0.43 -0.9 0.37 0.44 -0.2* -0.31 0.74 0.78 -0.61 -0.89 -0.68 0.26 -0.53 -0.21* -0.31 1 0.23 -0.17* 0.29 0.29 -0.03*** 0.05***

Si 0.34 -0.14** 0.89 0.87 0.02*** 0.55 0.09*** 0.32 0.54 0.08*** 0.48 0.63 0.26 0.55 -0.07*** 0.23 1 0.85 0.96 0.95 0.89 0.71

Zr 0.46 0.18* 0.72 0.58 0.26 0.84 -0.04*** -0.03*** 0.68 0.52 0.69 0.42 0.68 0.81 0.23 -0.17* 0.85 1 0.82 0.78 0.9 0.76

Ti 0.25 -0.2* 0.81 0.79 -0.02*** 0.47 0.16** 0.35 0.43 0*** 0.37 0.6 0.21* 0.5 -0.06*** 0.29 0.96 0.82 1 0.99 0.93 0.66

K 0.26 -0.21* 0.8 0.79 -0.04*** 0.41 0.07*** 0.32 0.45 -0.02*** 0.35 0.6 0.15** 0.44 -0.19* 0.29 0.95 0.78 0.99 1 0.93 0.61

Rb 0.38 0.04*** 0.71 0.6 0.13*** 0.55 -0.08*** 0*** 0.6 0.29 0.55 0.46 0.41 0.56 -0.04*** -0.03*** 0.89 0.9 0.93 0.93 1 0.64

Mode 0.3 0*** 0.7 0.6 0.17** 0.63 0.15** 0.16** 0.46 0.25 0.48 0.43 0.44 0.6 0.22* 0.05*** 0.71 0.76 0.66 0.61 0.64 1

Appendix C2: Correlation matrix for elemental ratios, log-ratios and single-element counts and measured grain-size mode in JU18-02, with Pearson correlation coefficient (r) and two-tailed test of significance (p); p ≥ 0.05 is indicated with ***, 0.01 < p < 0.05 is indicated with **, 0.001 < p < 0.01 is indicated with *, p < 0.001 is indicated with no asterisks.

JU18-02 Si/Ti Zr/Ti Si/K Si/Rb Zr/K Zr/Rb Ti/K Ti/Rb log Si/Ti log Zr/Ti log Si/K log Si/Rb log Zr/K log Zr/Rb log Ti/K log Ti/Rb Si Zr Ti K Rb Mode

Si/Ti 1 0.24 0.99 0.98 0.02*** 0.58 -0.11*** 0.8 0.74 -0.12*** 0.69 0.81 -0.28 0.34 -0.67 0.87 0.98 0.61 0.79 0.85 0.29 0.58

Zr/Ti 0.24 1 0.33 0.25 0.96 0.88 0.61 0.42 -0.13*** 0.31 -0.12*** -0.06*** 0.29 0.4 0.13*** 0.24 0.21* 0.85 0.33 0.15** 0.34 0.53

Si/K 0.99 0.33 1 0.99 0.14*** 0.67 -0.03*** 0.83 0.73 -0.05*** 0.68 0.8 -0.2* 0.41 -0.56 0.9 0.97 0.69 0.8 0.82 0.28 0.66

Si/Rb 0.98 0.25 0.99 1 0.06*** 0.62 -0.1*** 0.84 0.74 -0.07*** 0.69 0.82 -0.22* 0.41 -0.58 0.93 0.99 0.64 0.81 0.84 0.24 0.63

Zr/K 0.02*** 0.96 0.14*** 0.06*** 1 0.79 0.69 0.28 -0.3 0.35 -0.26 -0.24 0.4 0.37 0.39 0.1*** 0.02*** 0.76 0.19* -0.04*** 0.27 0.46

Zr/Rb 0.58 0.88 0.67 0.62 0.79 1 0.41 0.69 0.24 0.31 0.25 0.34 0.24 0.61 -0.03*** 0.65 0.58 0.97 0.59 0.43 0.25 0.74

Ti/K -0.11*** 0.61 -0.03*** -0.1*** 0.69 0.41 1 0.39 -0.67 -0.37 -0.68 -0.58 -0.25 -0.33 0.21* 0.02*** -0.11*** 0.39 0.33 0.1*** 0.64 0***

Ti/Rb 0.8 0.42 0.83 0.84 0.28 0.69 0.39 1 0.31 -0.37 0.26 0.44 -0.44 0.12*** -0.49 0.91 0.81 0.69 0.91 0.85 0.52 0.48

log Si/Ti 0.74 -0.13*** 0.73 0.74 -0.3 0.24 -0.67 0.31 1 0.3 0.99 0.99 0.13*** 0.59 -0.42 0.63 0.73 0.26 0.31 0.45 -0.29 0.52

log Zr/Ti -0.12*** 0.31 -0.05*** -0.07*** 0.35 0.31 -0.37 -0.37 0.3 1 0.39 0.24 0.97 0.86 0.55 -0.1*** -0.1*** 0.27 -0.39 -0.46 -0.62 0.45

log Si/K 0.69 -0.12*** 0.68 0.69 -0.26 0.25 -0.68 0.26 0.99 0.39 1 0.98 0.24 0.66 -0.3 0.6 0.68 0.26 0.26 0.36 -0.36 0.55

log Si/Rb 0.81 -0.06*** 0.8 0.82 -0.24 0.34 -0.58 0.44 0.99 0.24 0.98 1 0.07*** 0.6 -0.45 0.74 0.81 0.35 0.43 0.54 -0.21* 0.57

log Zr/K -0.28 0.29 -0.2* -0.22* 0.4 0.24 -0.25 -0.44 0.13*** 0.97 0.24 0.07*** 1 0.79 0.73 -0.2* -0.24 0.21* -0.46 -0.58 -0.62 0.38

log Zr/Rb 0.34 0.4 0.41 0.41 0.37 0.61 -0.33 0.12*** 0.59 0.86 0.66 0.6 0.79 1 0.28 0.42 0.37 0.58 0.06*** -0.01*** -0.47 0.72

log Ti/K -0.67 0.13*** -0.56 -0.58 0.39 -0.03*** 0.21* -0.49 -0.42 0.55 -0.3 -0.45 0.73 0.28 1 -0.43 -0.59 -0.07*** -0.51 -0.75 -0.4 0.02***

log Ti/Rb 0.87 0.24 0.9 0.93 0.1*** 0.65 0.02*** 0.91 0.63 -0.1*** 0.6 0.74 -0.2* 0.42 -0.43 1 0.9 0.64 0.82 0.8 0.19* 0.6

Si 0.98 0.21* 0.97 0.99 0.02*** 0.58 -0.11*** 0.81 0.73 -0.1*** 0.68 0.81 -0.24 0.37 -0.59 0.9 1 0.63 0.84 0.89 0.32 0.61

Zr 0.61 0.85 0.69 0.64 0.76 0.97 0.39 0.69 0.26 0.27 0.26 0.35 0.21* 0.58 -0.07*** 0.64 0.63 1 0.68 0.53 0.4 0.75

Ti 0.79 0.33 0.8 0.81 0.19* 0.59 0.33 0.91 0.31 -0.39 0.26 0.43 -0.46 0.06*** -0.51 0.82 0.84 0.68 1 0.95 0.71 0.46

K 0.85 0.15** 0.82 0.84 -0.04*** 0.43 0.1*** 0.85 0.45 -0.46 0.36 0.54 -0.58 -0.01*** -0.75 0.8 0.89 0.53 0.95 1 0.65 0.36

Rb 0.29 0.34 0.28 0.24 0.27 0.25 0.64 0.52 -0.29 -0.62 -0.36 -0.21* -0.62 -0.47 -0.4 0.19* 0.32 0.4 0.71 0.65 1 0.02***

Mode 0.58 0.53 0.66 0.63 0.46 0.74 0*** 0.48 0.52 0.45 0.55 0.57 0.38 0.72 0.02*** 0.6 0.61 0.75 0.46 0.36 0.02*** 1

Appendix D: Depth, thickness and age of flood deposits in CO18-01. The numbers in parentheses indicate the age uncertainties in years.

Depth (cm) Thickness (mm) Age (yr AD) Depth (cm) Thickness (mm) Age (yr AD) +12 +316 1.2 - 1.5 3 1991 (-12 ) 44.6 - 44.9 3 1306 (-326 ) +22 +320 2.5 - 3.2 7 1968 (-23 ) 45.3 - 46 7 1296 (-330 ) +36 +348 4.6 - 5.2 6 1936 (-37 ) 48.8 - 49 2 1233 (-359 ) +42 +367 5.8 - 6.4 6 1922 (-44 ) 50.9 - 51.2 3 1189 (-379 ) +44 +383 6.6 - 7 4 1918 (-46 ) 52.8 - 53.3 5 1153 (-395 ) +84 +394 10.9 - 11.1 2 1829 (-86 ) 54.4 - 55 6 1128 (-407 ) +92 +411 11.9 - 12.3 4 1811 (-95 ) 56.7 - 57 3 1089 (-425 ) +106 +418 13.7 - 14 3 1779 (-109 ) 57.7 - 58 3 1073 (-432 ) +113 +424 14.7 - 15 3 1763 (-117 ) 58.6 - 58.9 3 1060 (-438 ) +121 +443 15.8 - 16.4 6 1745 (-125 ) 60.7 - 60.9 2 1019 (-457 ) +128 +449 17.1 - 17.4 3 1729 (-132 ) 61.5 - 62.4 9 1005 (-463 ) +158 +461 20.4 - 20.8 4 1661 (-163 ) 63.6 - 63.9 3 978 (-476 ) +177 +463 22.7 - 23 3 1617 (-183 ) 64.1 - 64.6 5 973 (-478 ) +189 +465 23.6 - 24 4 1604 (-183 ) 64.8 - 64.9 1 969 (-480 ) +198 +466 25.4 - 25.8 4 1572 (-204 ) 65 - 65.1 1 966 (-481 ) +213 +467 27.3 - 27.5 2 1538 (-220 ) 65.2 - 65.4 2 964 (-482 ) +252 +480 31.4 - 31.7 3 1449 (-260 ) 66.7 - 67 3 935 (-495 ) +272 +490 33.7 - 34 3 1403 (-281 ) 68 - 68.3 3 912 (-506 ) +276 +498 34.4 - 35.3 9 1394 (-285 ) 69.1 - 69.3 2 894 (-514 ) +281 +503 35.8 - 36.2 4 1383 (-290 ) 69.8 - 70.3 5 882 (-519 ) +284 +506 36.5 - 36.8 3 1376 (-293 ) 70.6 - 71 4 875 (-522 ) +285 +517 36.9 - 38.4 15 1374 (-294 ) 72.1 - 73.2 11 850 (-534 ) +286 +518 38.5 - 38.7 2 1372 (-296 ) 73.3 - 73.8 5 848 (-535 ) +287 +533 38.8 - 39.2 4 1369 (-297 ) 75.3 - 75.7 4 814 (-550 ) +289 +534 39.4 - 39.6 2 1365 (-299 ) 75.8 - 76.2 4 812 (-551 ) +292 +536 39.9 - 40.4 5 1358 (-302 ) 76.4 - 77.3 9 807 (-554 ) +292 40.4 - 42.1 17 1358 (-302 )