The palaeo-environmental history of equatorial East Africa ... · East Africa: Implications from...

FACULTY OF SCIENCES

Master of Science in geology

Academic year 2015–2016

Master’s dissertation submitted in partial fulfillment of the requirements for the degree of Master in Science in Geology

Promotor: Dr. I. Meyer

Jury: Prof. Dr. D. Verschuren, Prof. Dr. G. Weltje

The palaeo-environmental history of equatorial East Africa: Implications from mineralogy and

particle-size distributions

Niels Tanghe

Cover image. View on Lake Challa (Credit: www.tanzania-experience.com)

http://www.tanzania-experience.com/

Acknowledgements

This thesis would have never been possible without the help and support from a lot of people, and

therefore I would like to seize the opportunity to thank them.

First, I would like to thank my promoter, Dr. Inka Meyer, for giving me the chance to work on this

very interesting topic. She was always available for help, whether it regarded discussion of my

results, correcting my text or some help in the lab when two hands were just not enough. Her help

and support guided me to the finish line, and I appreciate that very much. I would also like to wish

her a lot of success in her upcoming project(s).

I would also like to thank Ann-Eline Debeer, for her professional explanation of the heavy liquid

separation method and for her help with the SEM.

I much enjoyed working together with Jonas during the last few months, and therefore I would like to

thank him personally. I also very much appreciate him for letting me use his data in this thesis.

I would also like to dedicate some words to my classmates and friends. These last five years wouldn’t

have been the same without them, and therefore I would like to thank them for all the fun moments.

In particular, I would like to thank Thomas, Tycho and Loic for the great times during the last five

years.

Finally, I would like to thank my parents and sister for their endless support and love during the last

five years, and for allowing me to follow my own path.

Table of contents

CHAPTER I. Introduction and research objectives .................................................................................. 1

CHAPTER II. Study area ........................................................................................................................... 4

2.1. Geological setting .................................................................................................................... 4

2.2. Present day climatic setting .................................................................................................... 6

CHAPTER III. East African environmental variability .............................................................................. 8

3.1. Environmental evolution of equatorial East Africa since the LGM ......................................... 8

3.2. Lake Challa proxies ..................................................................................................................... 11

CHAPTER IV. Material and methods ..................................................................................................... 14

4.1. Grain-size analysis ...................................................................................................................... 14

4.2. End-member modelling analysis ................................................................................................ 17

CHAPTER V. Results .............................................................................................................................. 19

5.1. Grain-size analysis ...................................................................................................................... 19

5.2. End-member modelling .............................................................................................................. 21

5.2.1. End-member Analysis (EMA) ............................................................................................... 21

5.2.2. End-member variations ....................................................................................................... 24

CHAPTER VI. Discussion ........................................................................................................................ 29

6.1. Identification of the end members ............................................................................................ 29

6.1.1. End member 1 ..................................................................................................................... 30

6.1.2. End member 2 ..................................................................................................................... 30

6.1.3. End member 3 ..................................................................................................................... 32

6.1.4. End member 4 ..................................................................................................................... 33

6.1.5. End member 5 ..................................................................................................................... 35

6.2. Paleo-environmental implications of the dust fraction ............................................................. 36

CHAPTER VII. Conclusions ..................................................................................................................... 38

REFERENCE LIST ..................................................................................................................................... 40

APPENDIX .............................................................................................................................................. 44

Chapter I – Introduction and research objectives

1

CHAPTER I. Introduction and research objectives

Terrigenous particles from sedimentary records have proven to yield valuable information when

reconstructing paleoclimatic conditions and paleo-environments (Hamann et al., 2008; Holz et al.,

2007; Stuut et al., 2002). By definition terrigenous sediments derive from on-land sources and are

transported by different mechanisms to the side of deposition. Their initial formation is influenced by

the dominating weathering regime on land. Cold/arid conditions results typically in more physical

weathering of rocks, while warm/moist conditions are characterized by increased chemical

weathering. Rivers are the major mechanism for transportation of terrestrial sediments on a global

scale (Milliman and Syvitski, 1992). However, aeolian transport is also an important transportation

mechanism, especially in (semi-)arid environments. Wind-blown sediments can be transported over

very large distances depending on the wind speed and shear stress. For example, Saharan dust is

partly transported to the Bahamas (Ott et al., 1991) or to South America (Prospero et al., 1981).

Sediment transport through ice is the third mechanism and commonly results in a poorly sorted

debris comprising different grain-size classes. However, this process is restricted to areas where ice

masses are present. Figure 1.1 illustrates different mechanisms which lead to deposition of clastic

particles into a basin. Variations in physical properties, like sediment texture, particle shape and

variations in grain-size of terrigenous sediments were frequently used in order to gain information

about sediment transport mechanisms, depositional processes and sediment provenance (Krumbein,

1941; Passega, 1957; Visher, 1969). However, the information that can be extracted only from the

grain size of particles is limited, since the clastic particles in a basin are often a mixture from

sediments with different provenance and/or they are deposited by different transportation

mechanisms (Weltje and Prins, 2007; Weltje and von Eynatten, 2004). This mixture of signals is

expressed by a typical polymodal grain-size distribution of clastic samples (Holz et al., 2007).

Figure 1.1.: Sketch illustrating different mechanisms for transportation of clastic sediments to a basin. 1) transport of aeolian sediments. 2)

sediment transport by rivers. 3) transport of sediment by ice. 4) alluvial fan transport.

One way to overcome this problem is applying a complex statistical end-member model. This method

allows to identify distinct sub populations (end members) in the sediments based on their grain-size

distributions. Each identified end member is representing a sediment fraction which has the same

provenance and/or which was deposited by the same mechanisms. Unmixing of the terrigenous

fraction into end members helps understanding depositional processes and sediment provenance for

various environments (Paterson and Heslop, 2015; Weltje and Prins, 2003). In Africa, sediment

unmixing studies mostly concerned reconstructing wind and rainfall patterns using the aeolian


2

fraction in the sediments (Meyer et al., 2013; Stuut et al., 2002), although reconstructions of

sedimentation processes were also frequently performed (e.g. Holz et al., 2004).

This study aims to reconstruct paleo-environmental conditions in tropical East Africa using the clastic

sediment fraction from Lake Challa, a freshwater lake situated on the border between Tanzania and

Kenya. Sedimentary records in equatorial Africa hold important information on low-latitude climate

processes. Moreover, they provide answers to enduring questions on how external climate forcing

created climate changes in this area. For example, precessional-driven variations in summer

insolation at the Equator seems to have generated variations in monsoonal rainfall which were in

phase with the orbital forcing mechanisms (Verschuren et al., 2009). Lake Challa provides an ideal

study location to answer these questions. Due to its equatorial position (3.3° S), both Northern and

Southern Hemisphere climatic signals are recorded. The Intertropical Convergence Zone (ITCZ) passes

twice a year over the lake (fig 1.2 A), resulting in a bimodal rainfall pattern of two wet seasons and

two dry seasons (fig 1.2 B). This results in two monsoonal seasons, from a SE direction during

Northern Hemisphere summer and from NE direction during winter. The latitudinal range in the

migration of the ITCZ is maximal in East Africa, which enhances the monsoonal dynamics. Since Lake

Challa is always situated east of the Congo Air Boundary (CAB), the influence of Atlantic moisture in

the lake is minimal (Tierney et al., 2011b). High-latitude Northern Hemisphere climatic signals, which

were transported to lower latitudes by the Atlantic thermohaline circulation, do not reach the lake as

a consequence. Hence, Equatorial East Africa remained relatively unaffected to signatures of

Northern Hemisphere ice sheets.

Figure 1.2.: A) Position of the Intertropical Convergence Zone (ITCZ) and the Congo Air Boundary (CAB) in July (Northern Hemisphere

summer) and January (Southern Hemisphere summer). The location of Lake Challa is indicated with C. The red square indicates the area

that is shown in figure 2.3 (modified from Verschuren et al., 2009). B) Climatogram for Challa, Kenya (data from

www.worldweatheronline.com).

Extensive studies have been performed on Lake Challa and its sedimentary infill, mainly in framework

of the CHALLACEA research project (2005-2008). Reconstruction of the lake-level fluctuations

revealed that Lake Challa experienced some major episodes of desiccation, but never dried out

completely, providing therefore one of the few continuous paleo-records from equatorial East Africa

(Moernaut et al., 2010). These observations were acknowledged by reconstruction of the local

monsoon rainfall (Verschuren et al., 2009). Other studies mainly focused on the biogenic fraction

which is present in the lake, since this is the main component in the lake sediments (up to ~70 %)

http://www.worldweatheronline.com/


3

(Barker et al., 2013, 2011; Kristen, 2010; Milne, 2007; Sinninghe Damsté et al., 2011). The

comprehensive study of Lake Challa confirms the excellent quality of the sedimentary record. Lake

Challa will be the subject of an ICDP drilling later this year, further underlining the importance of this

research.

The terrigenous fraction in the sediments from Lake Challa have never been studied however, mainly

because of the rather low abundance of detrital minerals in the sediments. The processes which led

to transportation and deposition of the different terrigenous particles to the lake are still unclear,

and therefore this study aims to solve the following questions.

(i) What are the different transportation mechanisms which led to the deposition of

terrigenous particles in Lake Challa?

(ii) Are we able to document variations of aeolian input versus run-off of clastic particles in

Lake Challa during the last 25 ka?

(iii) Can we link the obtained results with other relevant proxies in order to extract

information about climatic variations in equatorial East Africa?

In order to find answers to these research questions, high-resolution grain-size analysis on the

CHALLACEA composite core will be performed using laser diffractometry on 178 distinct samples.

Following, the obtained grain-size distributions will be unmixed into representative sub populations

using the end-member modeling software AnalySize (Paterson and Heslop, 2015). The identified end

members will represent sediment fractions which share the same provenance and transportation

mechanisms. With the findings obtained by this study we will be able to close an important gap

which is still existing in the Lake Challa record.

This dissertation is divided into multiple chapters. Chapter II regards the regional setting of Lake

Challa, as well as the present-day climate that is present in Equatorial East Africa. The environmental

evolution of East Africa since the Last Glacial Maximum is described in chapter III, since the

sedimentary record of this study spans this time frame. Chapter III further summarizes some

important studies that have already been performed on Lake Challa, in order to give a

comprehensive view on how this study fits into the broad framework that already has been

established on the lake. A detailed explanation of the used material and methods is given in chapter

IV. Results are given in chapter V, as well as a step-by-step walkthrough on how the end-member

modelling was applied in this study. A comparison of the obtained results with other proxies is given

in chapter VI. All identified end members are interpreted and discussed in terms of paleo-

environmental implications for the study area. Finally, a conclusion will be made and the used

literature will be referenced. The appendix can be found at the end of this thesis.

Chapter II – Study area

4

CHAPTER II. Study area

2.1. Geological setting

Lake Challa is a small freshwater lake filling a volcanic caldera on the lower east slope of Mt.

Kilimanjaro (3.3°S; 37.7°E), right on the border between Tanzania and Kenya (Fig 2.1). The lake has a

surface area of ~4.2 km² and is situated at 880 m altitude. Lake Challa has a water depth of

approximately 95 m, with fluctuations between 92 and 98 m during the period 1999-2008 (Moernaut

et al., 2010). The caldera has a Pleistocene age and is situated within the rocks of the Kilimanjaro

complex. This volcanic complex is mainly composed of trachy-basalts, resting on a metamorphic

basement rock predominantly consisting of gneisses (Petters, 1991). These gneisses outcrop south

and east of Lake Challa. On top of the trachy-basalts lies a calcite-cemented tuffaceous breccia which

formation is most likely related to the formation of the crater (Downie and Wilkinson, 1972). This

“calcareous tuffaceous grits” are found on the south-eastern crater walls, while the rest of the crater

rim is mostly build out of the trachy-basalts from the Kilimanjaro complex (Kristen, 2010).

Figure 2.1.: Satellite image of the study area and its surroundings. Lake Challa is situated on the lower east slope of Mt. Kilimanjaro. The

yellow dotted line indicates the border between Tanzania in the West and Kenya in the East. The yellow square indicates the area shown in

figure 2.2A. The inset shows the location of the study area within the African continent, indicated with a red square (Satellite image:

Google Earth).

The crater lake is filled with a sedimentary succession of ~210 m thickness, covering the last

~250,000 years (Moernaut et al., 2010). Lake Challa is not fed by any rivers and there are also no

rivers draining the lake. Hence the water balance of the lake is controlled by subsurface in- and

outflow, local rainfall of ~600 mm/yr and evaporation of ~1700 mm/yr. Subsurface inflow of water

originates from precipitation water percolating from the montane forests upslope of Mt. Kilimanjaro.

This percolated water reaches Lake Challa within approximately 3 months (Moernaut et al., 2010).

Based on the artificial injection of tritium isotopes, Payne (1970) estimated the subsurface in- and

outflow of Lake Challa to be 12.5 x 106 m³ and 8.2 x 106 m³, respectively.


5

Figure 2.2.: A) Satellite image of Lake Challa and bathymetrical map of the lake. Contour intervals are every 10 m, with a maximal depth of

94 m during time of acquisition. The white dotted line represents the crater catchment area. The yellow dotted line is the border between

Tanzania in the West and Kenya in the East. The red dot in the centre of the lake is the coring location of the CHALLACEA core used in this

study. The red line indicates the location of the 3D seismic profile shown in figure B, the red arrow shows the viewing direction on this

seismic profile (Satellite image: Google Earth; bathymetric map modified from Moernaut et al., 2010). B) 3D visualization of the

sedimentary infill of Lake Challa. The virtual surfaces were created by interpolation of seismic reflectors. The basement surface is

characterized by the presence of volcanic cones which separate the basin into three depressional areas, from which two are visible. The

green line indicates the projected location of the CHALLACEA core (modified from Moernaut et al., 2010).


6

The lake is surrounded by steep crater walls, reaching a height of 170 m above the modern lake

surface at certain places. These walls also limit the lake catchment area (1.38 km²) (Buckles et al.,

2014). The catchment can only be enlarged in the northwestern corner of the crater, were a 300 m

long creek is present. However, a contribution from the creek most likely only occurred during

periods of very heavy rainfall (Kristen, 2010).

The water masses of Lake Challa are stratified during the two wet seasons which run from October to

December and from March to May, respectively, classifying Lake Challa as a meromictic lake. The

epilimnion is limited to the upper 20 m in the water column. During the dry and more windy season

between January to February and June to September, the epilimnion drops to depths of about 50-60

m (Kristen, 2010). These seasonal variations are also reflected in the sediments of Lake Challa. The

deep mixing of the water layers from June to September provides nutrients, which results in a diatom

bloom during these months. Detrital minerals are mainly deposited during wet seasons.

Moernaut et al. (2010) acquired a dense grid of high-resolution seismic profiles in Lake Challa during

a field campaign in 2003. Further, the bathymetry of the lake was acquired during this campaign (fig

2.2A). Lake Challa has a bowl-shaped bathymetry with a maximal water depth of 94 m at the time of

data acquisition. The upper slopes are dipping between 30° and 90°, while the sediments at the

center of the lake are slightly dipping (1 – 5°). The break in slope between the sediments on the outer

parts and the sediments on the inner parts of the lake is relatively sharp and is situated at 60 – 70 m

water depth. A 3D view (fig 2.2 B) across the lake was created by Moernaut et al. (2010) and shows

the presence of volcanic cones on the bottom of the lake, which initially separated the basin into

three depositional areas (depression 1-3). Two of the three depositional areas are visible in figure 2.2

B.

2.2. Present day climatic setting

Due to the tilt of Earth’s axis, solar insolation reaches a peak twice a year at the Equator, causing two

monsoonal systems. During the periods of maximal solar insolation, the African continent warms up,

resulting in a low pressure area above the continent. Moist air is brought in from the Indian Ocean,

resulting in monsoonal rains above the African continent. Shifting of the peak solar insolation also

results in migration of the Intertropical Convergence Zone (ITCZ) and the Congo Air Boundary (CAB),

northward during Northern Hemisphere’s summer and southward during Southern Hemisphere’s

summer (fig 1.2 A). The local climate at Lake Challa is tropical semi-arid with a bimodal rain

distribution (fig 1.2 B). From October to December the monsoon coming from a northeasterly

direction, providing “short rains” to East Africa (fig 2.3). From March to May, the monsoonal winds

are provided from a southeasterly direction (fig 2.3), providing “long rains” which are characterized

by more rainfall, although the total amount of rainfall is still relatively low (less than 300 mm during

the March – May season) due to the equatorial location (Camberlin and Okoola, 2003). Nicholson

(1996) described that the monsoonal winds in East Africa are rather dry. Dry NE monsoonal winds are

associated with its passing over the eastern Sahara and with cool waters in the Arabian Sea. The SE

monsoonal wind loses humidity along the East African coast due to friction with the shoreline. The

“short rains” in equatorial East Africa are positively correlated with the El Niño Southern oscillation

(ENSO) and with large-scale sea surface temperatures (SST) anomalies in the Indian Ocean (Black et

al., 2003; Mutai and Ward, 2000), while the “long rains” show little to no correlation with ENSO or

SST-anomalies (Camberlin and Philippon, 2002). Varve thickness variations from Lake Challa indicated


7

that El Niño years are characterized by increased rainfall, while La Niña years tend to be more dry

(Wolff et al., 2011).

The shifting position of the ITCZ and the CAB is shown in figure 1.2 A. The CAB is the convergence

zone of moisture brought from the Atlantic vs. Indian Ocean. Zonal patterns in the hydroclimate of

equatorial Africa can be explained by the CAB. A dry climate in central equatorial Africa during a

contemporary wet climate in East equatorial Africa can be explained by a reduced moisture transport

into the continent, caused by the CAB (Tierney et al., 2011). Due to its location east of the CAB (see

fig 1.2 A), the influence of Atlantic-Ocean moisture is minimal in Lake Challa.

Figure 2.3.: Position of the ITCZ during January, April, August and November. Monthly precipitation (based on gauge data) is indicated in

green with contours intervals of 50 mm. Wind fields for the 925 hPa pressure level are indicated with arrows. The arrow length is

proportional to the wind speed. Lake Challa is indicated with a yellow dot. The northeastern monsoonal direction is visible in January, the

southeasterly monsoonal direction in August (http://iridl.ldeo.columbia.edu, from Verschuren et al., 2009).

http://iridl.ldeo.columbia.edu/

Chapter III – East African environmental variability

8

CHAPTER III. East African environmental variability

3.1. Environmental evolution of equatorial East Africa since the LGM

Several studies concerning the environmental variations in equatorial East Africa during the late

Quaternary were carried out in the last decades. This chapter will summarize the general

environmental evolution for the last 25 ka, roughly starting with the Last Glacial Maximum (LGM).

Figure 3.1 shows a map of East Africa with study locations discussed in this chapter. Table 3.1

contains the name of these locations, the coordinates and the reference paper.

Figure 3.1.: Map of East Africa and the different study sites cited in this chapter. The yellow dot C indicates the location of Lake Challa.

Satellite image from OpenAerialMap.

Table 3.1.: Site details discussed in the text and plotted in figure 3.1.

Nr Location Latitude Longitude Reference

1 Lake Malawi 11°41'1.26"S 34°34'29.73"E Barker and Gasse (2003); Nicholson et al. (2013)

2 Lake Masoko 9°20'3.17"S 33°45'18.41"E Barker and Gasse (2003); Garcin et al. (2007); Kiage and Liu (2006)

3 Lake Rukwa 7°55'31.68"S 32°06'58.56"E Barker and Gasse (2003); Kiage and Liu (2006)

4 Lake Manyara 3°35'34.43"S 35°49'13.66"E Barker and Gasse (2003)

5 Mt. Kenya 0° 09'0.00"S 37°19'0.00"E Barker et al. (2001); Kiage and Liu (2006)


9

6 Lake Albert 1°42'2.71"N 30°57'51.60"E Beuning et al. (1997); Kiage and Liu (2006)

7 Lake Kivu 1°58'46.71"S 29°09'29.31"E Gasse (2000)

8 Lake Victoria 1° 04'26.49"S 33°00'55.68"E Gasse (2000); Kiage and Liu (2006)

9 Lake Magadi 1°53'43.61"S 36°14'44.32"E Gasse (2000); Roberts et al. (1993)

10 Lake Naivasha 0°45'57.24"S 36°20'57.45"E Gasse (2000); Kiage and Liu (2006); Verschuren et al. (2000)

11 Lake Nakuru 0°21'45.89"S 36°05'22.34"E Gasse (2000)

12 Lake Elmenteita 0°26'31.24"S 36°14'32.46"E Gasse (2000)

13 Lake Turkana 3°41'5.19"N 36°04'2.53"E Gasse (2000); Kiage and Liu (2006)

14 Lake Tanganyika 6°21'6.06"S 29°36'22.63"E Gasse (2000); Kiage and Liu (2006); Nicholson et al. (2013); Tierney and Russell (2007); Tierney et al. (2008, 2010)

15 Lake Mahoma 0°21'48.83"N 29°53'0.83"E Kiage and Liu (2006)

16 Kamiranzovu 2°29'35.31"S 29°08'46.52"E Kiage and Liu (2006)

17 Mt Kilimanjaro 3° 04'2.73"S 37°21'20.26"E Kiage and Liu (2006); Schüler et al. (2012)

18 Lake Simbi 0°22'5.84"S 34°37'45.69"E Kiage and Liu (2006)

19 Sacred Lake 0° 02'52.28"N 37°31'39.95"E Kiage and Liu (2006)

20 Lake Bogoria 0°15'53.42"N 36°06'0.92"E Kiage and Liu (2006)

21 Loboi Swamp 0°23'31.00"N 36°02'43.00"E Kiage and Liu (2006)

22 Cherangani Hills 1°15'5.75"N 35°27'1.87"E Kiage and Liu (2006)

23 Lake Kimilili 0°57'41.43"N 34°35'15.29"E Kiage and Liu (2006)

24 Lake Abiyata 7°36'33.81"N 38°35'59.51"E Kiage and Liu (2006)

25 Lake Emakat 3°11'35.82"S 35°32'11.86"E Ryner et al. (2007)

The LGM coincides with a period of minimal Northern Hemisphere summer insolation (see fig 3.2),

resulting in a reduced NE monsoonal circulation as well as dry and cold conditions in equatorial East

Africa. Several lakes experienced severe low stands during the LGM due to the weaker global

hydrological cycle, as evident in Lake Tanganyika (fig 3.1, nr. 14), Lake Albert (6) and Lake Victoria (8)

(Gasse, 2000). However, since the precessional-driven summer insolation values are in antiphase

between the Northern and Southern Hemisphere (Berger, 1978), Southern Hemisphere summer

insolation reached a maximum during the LGM. Hence, monsoonal circulation was expected to be

enhanced in the Southern Hemisphere during the LGM. Sedimentological, hydrological and

palynological observations in the southern East African tropics however contradict this hypothesis by

also showing indications for a dry and cold LGM in these areas (Barker and Gasse, 2003; Gasse, 2000;

Kiage and Liu, 2006). Barker and Gasse (2003) suggest that the dry conditions during the LGM can be

explained by low SSTs and the presence of ice sheets in the Northern Hemisphere, having a more

dominant effect on the East African climate system than insolation-induced monsoonal precipitation.

After the LGM, the climate remained rather cool with episodes of prolonged desiccation causing

further low stands of the tropical East African lakes at the beginning of the deglacial period (Beuning

et al., 1997; Kiage and Liu, 2006). Palynological data from Mt. Kilimanjaro (17) however indicate a

shift towards more humid conditions (Schüler et al., 2012), which points out a contradictory signal

between different proxies. Between 17 and 15 ka ago, a wetting/warming phase was established in

equatorial Africa, although the exact onset of this phase is still much debated (Gasse, 2000; Schefuß

et al., 2005). Tierney et al. (2010) detected a rise in land surface temperatures shortly after the LGM,

using molecular proxies and pollen assemblages from Lake Tanganyika (14), reflecting changes in

vegetation cover. This rise in temperature coincides with an increase in Northern Hemisphere

summer insolation, although the exact cause is still uncertain (Tierney et al., 2008). Hydrological


10

changes occurred around 15 ka, with a rather drastic transition from arid to humid conditions. As a

consequence of this transition, lake levels in equatorial East Africa rose, as evident in Lake Albert (6),

Lake Emakat (25) and Lake Tanganyika (14) (Gasse, 2000; Ryner et al., 2007; Tierney et al., 2010).

During Northern Hemisphere cold events like the Younger Dryas (ca 12.9 - 11.7 ka BP) and Heinrich

event 1, wind-driven mixing in East African lakes was reduced. This was most likely the effect of a

southern displacement of the ITCZ and a weakened NE monsoon (Tierney and Russell, 2007). Further

these cold spells were shown to be generally arid as a result of a lower Indian Ocean SST (Schefuß et

al., 2011). Low lake levels were observed during the Younger Dryas in Lake Kivu (7), Lake Victoria (8)

and Lake Magadi (9) (Gasse, 2000; Schefuß et al., 2005; Talbot et al., 2007). Garcin et al. (2007) and

Roberts et al. (1993) issued that the NE monsoonal system recovered rapidly at the end of the

Younger Dryas, as evident from climate records of Lake Malawi (1) and Lake Masoko (2). This

resumption of the NE monsoonal system also implied more pronounced migrations of the ITCZ over

the African continent, as described by Garcin et al. (2007).

The early- and mid-Holocene (roughly from 11 - 5 ka BP) in Northern and Eastern Africa were

characterized by extremely humid conditions, with more precipitation compared to modern times

(Tierney et al., 2011a). This period is known as the African Humid Period (AHP) and is generally

assumed to be the result of increased summer insolation (DeMenocal et al., 2000). Whereas the

causing dynamics in Northern and especially Northwestern Africa are relatively well known, the

underlying mechanisms in East Africa, where a similar trend to more humid conditions were shown,

are less understood. Tierney et al. (2011) issued that the East African Humid Period was most likely

the consequence of a reduced precipitation seasonality, caused by an orbitally-induced increase in

precipitation during the dry season. Evidence for the warm and moist conditions during the early and

mid-Holocene is found in pollen assemblages from the sedimentary record of Lake Turkana (13), Lake

Victoria (8) and Lake Naivasha (10) and in vegetation changes on Mt Kilimanjaro (17) (Kiage and Liu,

2006; Schüler et al., 2012). In equatorial East Africa, a high precipitation/evaporation ratio was

reached during the early Holocene as evident in a diatom record from Lake Victoria (8). From the

mid-Holocene until today, precipitation gradually lowered (Gasse, 2000). The transition from a humid

early Holocene to a drier late Holocene occurred stepwise according to Jung et al. (2004), with a first

aridification step at 8.5 ka and a second step from 6 to 3.8 ka, which marked the end of the AHP.

Thompson et al. (2002) described ice core records from Mt Kilimanjaro (17), which reveal three

abrupt climate events at 8.3 ka, 5.2 ka and 4 ka. The event at 8.3 ka reflects a period of strong aridity,

which resulted in a rapid drop of East African lake levels. This event is coincident with decreasing

methane concentrations observed in Greenland ice cores, suggesting that these two events were

closely linked to each other (Gasse, 2000). At 5.2 ka, an abrupt cooling event occurred, which is

recorded in the ice cores by a change in oxygen isotopes. Extremely cold and dry conditions at 4 ka

resulted in the deposition of a dust layer in one of the ice cores. However, due to the higher

resolution of the ice-core record these findings could not be confirmed by other studies so far (Kiage

and Liu, 2006).

Nicholson et al. (2013) describe the temperature variations that occurred over the African continent

in the last two millennia. Records from Lake Tanganyika (14) shows evidence for warm conditions

during the Medieval Climate Anomaly (MCA), which corresponds to the period AD 900 to AD 1200

(Jones et al., 2001). The highest temperatures were reached during the late MCA, roughly 1100 years


11

ago. This warmer period was followed by a cooling during the Little Ice Age (LIA), as evident from

palynological data and organic biomarkers from Lake Malawi (1). The LIA corresponds with the

period spanning from AD 1550 to AD 1900 (Jones et al., 2001). Since the last ~250 years, a gradual

warming trend is clearly evident in the climate records, although it was interrupted by a short cooling

event during the mid-20th century. Temperature variations Lake Naivasha (10) during the last

millennium indicate drier conditions during the MCA and wetter conditions during the LIA.

Furthermore, the MCA seems to be coeval with a phase of high solar radiation, while wetter periods

are coeval with periods of low solar radiation (Verschuren et al., 2000).

3.2. Lake Challa proxies

A lot of studies have already been performed on Lake Challa and its sedimentary infill during the last

couple of years, mainly in framework of the CHALLACEA project (Kristen, 2010; Moernaut et al.,

2010; Verschuren et al., 2009). This section will discuss studies that are relevant for this dissertation,

since some data and interpretations from these studies will be used later.

In 2003 a high-resolution seismic survey was performed in order to investigate the sedimentary infill

of the lake (Moernaut et al., 2010). The seismic-stratigraphic data was used to define the coring

location of the CHALLACEA core. The seismic data of Lake Challa were further used to reconstruct

lake-level fluctuations over the last 140 ka. Uniformly draped sediments are considered to be

deposited during periods were the lake level was high, while sediment packets which were deposited

during low lake-level stands are considered to be more concentrated at the centre of the basin, in

the deeper parts of the lake. The reconstructed lake-level fluctuations reflect the moisture-balance

variations caused by environmental variations in equatorial East Africa. Lake Challa experienced

some major episodes of desiccation, but never dried out completely, providing therefore one of the

few continuous paleo-records from equatorial East Africa. The most important lake level variations

since the LGM are shown in figure 3.2b and are discussed below.

A long period of high lake levels persisted in Lake Challa from ~97 ka BP until ~20.5 ka BP. During the

LGM and the beginning of the late-glacial period, the lake level was generally low (fig 3.2), although

periods of more severe low stands had occurred before in the lake (Moernaut et al., 2010). The LGM

related low stand was present until ~14.5 ka BP, with an extreme drought period between ~16.9 and

16.3 ka BP, corresponding to the H1 event. The lake level generally remained high during the last

14.5 ka, except for a few episodes during which the lake levels dropped: from ~12.9 – 12.0 ka BP,

corresponding to the Younger Dryas period, and some mid-Holocene low stands, corresponding with

droughts described by Barker et al. (2001) on Mt. Kenya.

In 2005, in framework of the CHALLACEA-project, gravity cores with a undisturbed sediment-water

interface were recovered from the centre of the lake (fig 2.2 A), together with three parallel piston

cores using a UWITEC hammer-driven piston coring platform. Overlapping successions in the

sediment piston cores were cross-correlated with each other in order to create a composite core.

The CHALLACEA composite core has a total length of 21.6 meter and is now stored in the Ledeganck

complex of Ghent University (Department of Biology – Limnology group). The core sediments are

mainly composed of finely laminated organic mud, alternating between dark and light colors and rich

in diatom silica. Five turbidites are present in the core that consist of reworked crater slope material.

They are situated at 4.87-5.13 m, 6.77-7.09 m, 18.99-19.04 m, 19.18-19.24 m and 20.24-20.39 m core

depth.


12

Figure 3.2.: Comparison of Lake Challa records and climate data for equatorial East Africa. Green bars correspond with Northern Hemisphere high-latitude influences, blue bars with Southern Hemisphere high-latitude influences. YD: Younger Dryas, ACR: Antarctic Cold Reversal, H1: Heinrich event 1, LGM: Last Glacial Maximum. a) Rainfall reconstruction at Lake Challa based on the BIT-index variations. The bold line is the three-point moving average (modified from Verschuren et al., 2009). b) Reconstructed lake-level fluctuations based on the seismic-stratigraphic survey from Moernaut et al. (2010). c) Insolation values at the equator during March and September (Berger and Loutre, 1991).

An age-depth model for the CHALLACEA core was established by Blaauw et al. (2011), shown in figure

3.3. It was derived from AMS 14C analysis of 164 bulk organic carbon samples, combined with 210Pb

dating of recent sediments. The ~22 m long core has an age of 25000 years. The age-depth model

was corrected for reservoir ages by analyzing the difference between 14C ages of bulk organic carbon

and the derived age from the 210Pb chronology in that sediment interval, and by wiggle-matching

sequences of bulk-organic 14C dates. The carbon reservoir age evolves from ~450 years during the

LGM towards ~200 years in the early and middle Holocene and ~250 years today. The age-depth

curve was smoothed using a smooth spline function.


13

Figure 3.3.: Age-depth model for the CHALLACEA core, based on AMS 14C dating on 164 bulk organic carbon samples. Grey areas indicate the turbidite levels (modified from Blaauw et al., 2001).

The observations described by Moernaut et al. (2010) are supported by the study of Verschuren et al.

(2009), who measured the branched and isoprenoid tetraether index (BIT-index) on the Challa

sediments. The BIT-index is an organic biomarker which is a proxy for local monsoonal rainfall. Higher

BIT-values represent wetter conditions and increased surface run-off. A good correlation between

the BIT-index and the moisture balance variations is observed. Monsoonal rainfall seems to vary at a

half-precessional cycle (~11,500 years), in phase with orbital insolation forcing. When the summer

insolation gradient between the Northern and Southern Hemisphere reached a maximum, the

monsoons in this area were intensified, resulting in wetter conditions. A minimal insolation gradient

corresponds with drier periods and no strengthening of the monsoons. Intensification of the SE

monsoon is observed from ~16.5 ka BP and continued into the Holocene, although an interruption

occurred during the Younger Dryas period. In general, the BIT-index shows periods of relative

drought from 20-16.5 ka BP and from 8.5-4.5 ka BP.

Other studies on Lake Challa sediments included high-resolution geochemical studies described by

Kristen (2010), measuring the deuterium/hydrogen ratio (δDwax) of higher plant leaf waxes in order to

investigate the influence of the Indian Ocean on the paleohydrology in equatorial East Africa (Tierney

et al., 2011b), measuring variations in varve thickness to research interannual rainfall variability

(Wolff et al., 2011) and comparing sediment trap data with the light-dark laminations to examine

modern seasonality in the lake and how this is reflected into the sedimentary record (Wolff et al.,

2014).

Chapter IV – Material and Methods

14

CHAPTER IV. Material and methods

4.1. Grain-size analysis

This thesis topic aims to identify sub populations of detrital mineral material in the sediments from

Lake Challa. In order to obtain this subpopulations, a high-resolution grain-size analysis was

performed on the ~22 meter long CHALLACEA composite core.

Samples for grain-size analysis were taken at constant 12 cm composite-depth intervals over the

entire core length, with a higher sampling resolution around the Younger Dryas. Turbidite intervals in

the core were skipped. The used sampling resolution is the same as the sampling resolution used for

the BIT-analysis from Verschuren et al. (2009). Appendix A indicates the analyzed samples and their

core depth. Every sample consist of a 4-cm core increment, where the sampling depth is considered

to be the central depth in this 4-cm core increments. The sample name includes the central

composite depth of the 4-cm core increment. For example, sample “CHALLA05- 002” consists of the

core increment from 0 to 4 cm composite depth, whose central value is at 2 cm composite core

depth. Approximately 5 gram of sediment were sampled from the cores, whereof 1 gram of bulk

(wet) sediment was used for the grain-size analysis.

The samples were chemically pre-treated in order to remove organic matter and carbonates. For the

removal of organic matter, the samples were put in 10 ml of distilled water and 2 ml H2O2 (30 %) was

added. The mixture was boiled until the reaction stopped. Distilled water was added to the sample in

order to prevent the sample from drying out while boiling. After boiling, the mixture was filled up

again to 10 ml and put aside to cool down. Removal of calcium carbonate (CaCO3) was done by

adding 1 ml HCl (10 %) and boiling the mixture for one minute on a hot plate. The samples were then

filled up to 100 ml and put aside to settle down. They were decanted when the water was clear. This

procedure of filling the sample and decanting when clear was repeated twice in order to create PH-

neutral values. The procedure for removal of organic material and carbonates that was used is

considered to be the standard procedure for grain-size sample preparation.

The standard procedure to remove biogenic silica from the sediments is by adding 1 ml NaOH (2 N)

to the samples in 10 ml of distilled water and boil the samples for 10 minutes. This standard grain-

size pre-treatment has proven to be insufficient during early tests in the lab for the samples in this

study, due to the presence of high amounts of very resistant diatoms in the Lake Challa sediments.

Treatment of the samples solely by adding NaOH was insufficient to completely destroy the diatoms,

even when more NaOH was added. Treatment by adding NaOH and putting the samples in a hot bath

for 7 hours did remove the diatoms present, but this procedure affected also the clay fraction of the

sediments, which is undesirable when the particle-size distribution is wanted. This method was

ultimately not used to remove the diatoms in the sediment.

For this study, the presence of the resistant diatoms in the lake sediments was a big issue since they

influence the grain-size distribution of the clastic sediment fraction. Consequently, a different

procedure was needed in order to remove the biogenic silica present in the lake sediments. Madella

et al. (1998) described a reliable method to extract opal phytoliths from sediments by using Sodium

polytungstate (Na6(H2W12O40)H20), a non-toxic heavy liquid. Sodium polytungstate (also called LST

Fastfloat) has a density of 2.8 g/ml. For this study, the Fastfloat heavy liquid was diluted with distilled

water until it reached a density of 1.9 g/ml, because the detrital mineral fraction present in the


15

sediments have a higher density and the diatom fraction has a lower density. Due to the density

differences, the heavy liquid method will cause the denser detrital mineral fraction to separate from

the lighter biogenic silica fraction.

Before the sample was separated into a light fraction and a heavy fraction using the LST Fastfloat,

removal of clays from the samples is necessary. Clay particles in the Challa sediments have a

relatively low density and would float in the Fastfloat if they are not removed before the heavy liquid

separation. Removal of the clay fraction is based on the method of gravitational settling. After

removal of organic matter and carbonates, the sample was put into a beaker of 250 ml, which served

as an Atterberg column. The beaker was filled up with distilled water until there was a water column

of 10 cm height in the beaker. The clay fraction, which was still in suspension after 116 minutes, was

decanted into a big beaker (1 L). This time interval depends on the height of the water column in the

beaker (10 centimeter height) and on the temperature of the distilled water (21°C), and was

calculated using Stokes’ Law. The decanted fraction is finer than 4 µm, while the coarser/heavier

fraction was settled to the bottom of the beaker. The procedure of clay decanting was repeated four

times in order to be sure that all the clay minerals present in the samples were removed. The beaker

with the decanted clay fraction was put aside to settle. The heavier fraction, which includes both the

detrital minerals and the biogenic silica, was put into a sediment oven to dry at 60°C. When the clay

fraction had settled in the large beakers, the water was decanted until about 200 ml was left. This

remaining 200 ml was centrifuged in order to let the clay particles settle. The samples were

centrifuged at a speed of 2600 rpm for 15 – 20 minutes. Some samples were centrifuged twice

because settling was insufficient after one round of centrifuging. The detrital mineral material was

separated from the biogenic silica using the LST Fastfloat heavy liquid. The general set-up for the

heavy liquid separation is shown in figure 4.1. Fastfloat was added into a glass tap. The dry sample

was homogenized by shaking the vial before pouring it into the fastfloat. Subsequently, the sample

was stirred using a glass rod. The heavier fraction, consisting of the detrital mineral material, sank to

the bottom due to its higher density than the fastfloat density (1.9 g/ml). The biogenic silica has a

lower density than the fastfloat and floated to the top of the liquid. When separation into a heavier

and a lighter fraction had occurred, the tap was opened quickly to let the heavier fraction pass and

immediately closed again. The fraction which escaped from the tap was captured onto a filter with

pore size 12-15 µm. After the fastfloat passed through the filter, the filter was rinsed with distilled

water in order to get all the remaining fastfloat crystals out of the filter. Since LST Fastfloat is an

expensive chemical, every fastfloat crystal needed to be recycled. The lighter fraction, which contains

the diatoms, was captured onto another filter. This filter was also rinsed with distilled water in order

to recover the fastfloat crystals.


16

Fig 4.1.: Set-up of the heavy liquid separation method.

When the filter with the heavier fraction was dry, the sediment was added to the clay fraction. The

samples were poured into a 100 ml beaker and 1 ml of sodium hexametaphospate (2%) was added.

This mixture was boiled shortly on a hot plate. Sodium hexametaphospate disintegrates all

aggregates which are present in the samples. After boiling, the samples were put aside to cool down

to room temperature.

Particle-size distributions were obtained with the Malvern Mastersizer 3000, using the principle of

laser diffraction (fig. 4.2). Samples were inserted into the Malvern Mastersizer 3000 using a Hydro

Volume. The principle of laser diffraction is based on the scattering of light when the laser is

obstructed by a particle (i.e. sediment grain in this case). Larger particles scatter the light at smaller

diffraction angles than finer particles, but with a higher intensity. As a result, a variation in light

intensity reaches the detectors. Samples are measured with both a red (633 nm) and blue laser (470

nm), to avoid Rayleigh scattering. Rayleigh scattering occurs when the sediment particles are too

small. If a particle is smaller than 1/10 of the laser wavelength, the laser light will be scattered in all

directions with no angular variation. By using a blue laser, which has a lower wavelength than the red

laser, the Rayleigh scattering will shift to a smaller size. Particle-size distributions are obtained by

measuring the angular variations in light intensity using the Mie-theory of light scattering and are

expressed as the frequency of a certain class weight (http://www.malvern.com, 2016). An incident

laser beam ray can be reflected, refracted or absorbed by the sediment particle, and the Mie-theory

takes all these pathways into account. The grain-size distributions of sediment samples are better

described using the Mie-theory than using the Fraunhofer-theory (Eshel et al., 2004), which only

takes diffraction of the laser beam into account.

http://www.malvern.com/


17

Fig 4.2: Pathways of the red and blue lasers inside the Malvern Mastersizer 3000. Variations in light

intensity reaching the detectors results in the calculation of the grain-size distribution.

Prior every measurement, the device was initialized and the background was measured. The

instrument makes a measurement during the background check. The sample cell is filled with

distilled water during this background check but no sediment is present. Like that the diffraction of

the laser beam caused by the distilled water is determined, which is subtracted from the following

measurement with sediment, resulting in the solely particle induced diffraction. Afterwards, the

sediment sample was carefully mixed and added into the Malvern using a pipette until the laser

obscuration was in range (between 5 and 20%). Three measurements were performed for every

sample. The obtained grain-size distributions were checked visually if they were stable. If the

distributions did not show good overlap, other measurements were performed until the results were

stable.

When all samples were measured, the average grain-size distribution was calculated for each sample.

The calculated average of the different samples was used as an input for the end-member analysis.

The mean grain size was calculated using the GRADISTAT software package v8.0 (Blott and Pye,

2001).

4.2. End-member modelling analysis

The clastic fraction accumulated in aquatic basins consists of a mixture of sediment population

supplied from different sources and are transported by different mechanisms to the site of

deposition (e.g. Weltje and Prins, 2003). This results in often polymodal grain-size distributions,

which are hard to interpret in terms of sediment transport dynamics or paleo-environmental

interpretations. Therefore mathematical-statistical end-member models are frequently applied in

order to define the different sub populations within the sediments (Meyer et al., 2013; Stuut et al.,

2002; Weltje and Prins, 2007). The method aims to establish a physical mixing model that describes

the measured data in a limited number of nonnegative and unimodal sub populations which can be


18

individually interpreted. The calculated end members yield important information about depositional

processes and sediment provenance (Weltje, 1997). In this study, unmixing was performed using the

AnalySize modelling algorithm developed by Paterson and Heslop (2015). AnalySize is a free GUI

software package which uses an algorithm inspired by hyperspectral image analysis for unmixing of

grain size distribution (https://github.com/greigpaterson/AnalySize).

When grain-size distributions are imported into AnalySize, the software allows to browse through the

different samples in order to visually check how the grain-size distributions change through the

complete composite core sequence. End-member analysis (EMA) can be performed by a non-

parametric approach or by a parametric approach. The non-parametric approach estimates the end

members from the initial raw data, based on covariability in the grain-size distributions. This

approach gives fast results but is often not able to identify single sediment sources in the data set,

since polymodal or negative end members are obtained. Therefore, Paterson and Heslop (2015)

introduced an alternative, the parametric approach, which unmixes the grain-size distributions into

parametric distributions, therefore being able to identify unimodal grain-size sub populations. The

end members are estimated using a least squares approach and are then fitted to the individual

grain-size distributions. Further explanation about the use of the AnalySize software is given in the

results chapter.

https://github.com/greigpaterson/AnalySize

Chapter V - Results

19

CHAPTER V. Results

5.1. Grain-size analysis

The variations in grain size as a function of the downcore composite depth is shown in Fig 5.1.

Generally, the sediment has a silty/clayey texture. The mean grain size is fluctuating between 8 and

15 µm and the dominant mode is situated around 10 µm. The upper 7.5 m and the core interval

between roughly 12 and 17 m have a very fine/fine silty compositions (5-10 µm), which is a slightly

finer grain size compared to the rest of the core, with a fine to medium silty composition (10-15 µm).

Six zones (I - VI) are identified in which the mean grain size remains approximately the same (see Fig

5.1). In the upper 2.5 meter of the core (zone I), the mean grain size is ~10 µm, with an abrupt

increase in grain size at 2.5 m. Zone II is situated between 2.5 – 8 m depth and is characterized by a

slightly finer grain size (mean around 9 µm) with a lower volume density compared to zone I. The

interval between ~8 – 9.5 m (zone III) is characterized by a clearly coarser grain size, with a mean

situated around 13 µm. Zone IV (9.5 – 12 m depth) shows some variability in the grain-size

distributions, with a mean grain size fluctuating between 7 and 12 µm. Between ~12 – 17.5 m (zone

V), the mean grain size remains rather constant, situated around 8 µm. There is only a slight increase

between 16 and 16.5 m depth towards a mean grain size of 10 µm. Zone VI confines the bottom 4 m

in the core and shows a variable grain size with a mean grain size fluctuating between 10 – 14 µm.

The grain-size distributions of 9 samples throughout the composite core are given on the right in

figure 5.1. They support the general observations which are described above. Samples 878, 998, 1754

and 2006 have a mode which is situated between 10 and 15 µm, while the other samples have their

mode situated at finer grain sizes, between 5 and 10 µm. Sample 518 is clearly polymodal, with its

second mode situated at a much coarser grain size around 250 µm. This sample originates from the

base of a turbidite, which might explain the coarser nature. A small peak is observed in all samples

around 0.8 µm, except for sample 878 in which this peak is absent.

Chapter V - Results

20

Figure 5.1: Variations in grain size (in volume %) together with the mean grain size (in µm) for the CHALLACEA core as a function of composite depth. The blue curve represents the age-depth model for the

CHALLACEA core (Blaauw et al., 2011). Grey areas represent the depths of the turbidite levels. The grain-size distributions for 9 samples are shown on the right with indications of their composite depths in cm.

Chapter V - Results

21

5.2. End-member modelling

5.2.1. End-member Analysis (EMA)

In order to unmix the grain-size distributions of the CHALLACEA core, a new end-member model

AnalySize (Paterson and Heslop, 2015) was used. The overlay of all grain-size distributions of the core

(n=178), shown in Fig. 5.2, reveal a polymodal distribution, with 5 recurring modes at ~0.6, 5, 12, 150

and 550 µm respectively.

Figure 5.2.: Multi-specimen plot of all obtained grain-size distributions. Numbers 1 – 5 identify five recurring peaks that are observed in the

entire data set.

In a first step, a non-parametric end-member model was established. The nonparametric EMA

calculates the maximum amount of end members (EM) based on the initial raw data. However, a

non-parametric model can result in negative or polymodal end members and are therefore only used

as a first estimation of the needed end members. Figure 5.3 A shows the goodness-of-fit statistics of

the non-parametric end-member model. Figure 5.3 B shows the end members calculated by the non-

parametric end-member model, in which EM 4 and EM 5 clearly show a polymodal distribution.

Chapter V - Results

22

Figure 5.3.: A) Coefficient of determination (R²) in function of the number of end members for a non-parametric end-member model. R2 is

increasing with increasing amount of end member. B) Non-parametric end-member model for 5 end members.

The coefficient of determination (R²) is calculated to identify the minimal numbers of end members

necessary for a good statistical explanation of the data. The correlation illustrates a better statistical

fit (higher R2) with increasing number of end members (see fig 5.3 A). The R2 for the 4 end-member

model is 0.994, which means that 99.4 % of the variance in each grain-size class can be reproduced.

For models with a higher number of end member, there is only a small increase in R², indicating that

the 4 end-member model provides a realistic resolution and meets the requirement of a minimum

number of end members and maximum reproducibility. The R² for the 5 end-member model is 0.997,

hence 99.7 % of the grain-size distribution can be explained. The 5 end-member model calculated by

the non-parametric EMA is shown in figure 5.3 B. However, since polymodal end members are

obtained by the non-parametric approach (see fig 5.3 B), further analysis was necessary.

In order to verify the initial amount of end members, calculated by the non-parametric EMA, a

parametric end-member model, with a given amount of end members, was performed in a second

step. Parametric end-member modelling takes a longer computation time but generally results in

more reliable results. Fig 5.4 shows the goodness-of-fit statistics for the parametric EMA. Similar to

the results of the non-parametric model R2 is rising with increasing number of end members. The 4

end-member model using a parametric EMA explains 99.0 % of the grain-size distributions, while the

5 end-member model explains 99.3 %.

Chapter V - Results

23

Figure 5.4.: Coefficient of determination (R²) as a function of the number of end members for a parametric EMA. R2 is increasing with

increasing amount of end members.

The overlay of the modeled end members and the measured grain-size data (Fig. 5.5) show that the

end members do not match the entire grain-size distribution. The 4 end-member model (fig 5.5 A)

does not show a good overlap between its third end member (orange curve) and the fourth peak

which was observed in the grain-size distributions as shown in figure 5.2. The overlap in the 5 end-

member model (fig 5.5 B) shows a better overlap, indicating a better model. However, none of the

generated models show any overlap with the first peak in the grain-size distributions which is

situated around a mode of 0.6 µm.

Figure 5.5.: A) Overlay of 4 end members onto the measured grain-size data (grey curves). As obvious from the plot the 4 end members do

not match the full grain-size spectrum. B) Overlay of 5 end members onto the measured grain-size data. Beside of the finest peak the 5 end

members do cover the whole range observed in the grain-size distributions.

Chapter V - Results

24

As a result of these observations, the model resulting in five parametric end members is used for

further interpretation (fig 5.6). The modes of the distinct end members are situated at 4, 10, 20, 60

and 400 µm respectively. The end members are perfectly unimodal and very-well sorted, which is an

indication for a representative model.

Figure 5.6.: Abundance of the five parametric end members used in this study.

5.2.2. End-member variations

Figure 5.7 shows the five obtained end members, modeled onto the grain-size distributions of six

distinct samples. The abundance of each end member varies depending on the shape of the sample

distribution. The samples are situated at 2, 530, 854, 1118, 1718 and 2162 cm composite depth

respectively, and each sample in figure 5.7 represents one of the identified zones I to VI. By

comparing the variations in the end-member abundances of these samples, a general idea is

established on how the downcore variations in end-member abundances are evolving.

Generally, EM’s 1 and 2 are the most important end members, showing high abundances although

some considerable variations are present throughout the core. EM 1 is the most important end

member in zones II, IV, V and VI, while EM 2 has a larger importance in zones I and III. EM 3 is also

prominently present in all samples, and its abundance seems rather stable in each zone. EM’s 4 and 5

represent the coarsest fraction of the distributions, with a generally higher abundance of EM 5. EM 4

is almost absent in zones III and V, and has a rather low abundance in zones IV and VI. However, its

abundance is higher than the abundance of EM 5 in zones I and II. The low abundance of EM 4 in

zones III - VI can probably be explained by the coarser nature of these samples with respect to zones

I and II (upper 7.5 m of the core). This coarser sediments result in an increase in the abundance of

EM 5.

Chapter V - Results

25

Sample CHALLA05-530 (zone II) is situated at the base of the uppermost turbidite in the core, and

therefore also shows increased abundance of coarse material, which is expressed as an increase in

EM 4. Further, sample CHALLA05-854, situated in the clearly coarser zone III shows a much less

pronounced abundance of EM 1, while EM 2 has its highest abundance in this sample.

Figure 5.7.: Modelling of the five obtained end members onto the grain-size distributions of six samples throughout the core. The core

samples are respectively 2, 530, 854, 1118, 1718 and 2162 cm composite depth. Each sample originates from one of the six different zones

which were identified in the core (Zones I – VI).

Chapter V - Results

26

The variations of the end member abundances through time are shown in figures 5.8 and 5.9. Figure

5.8 shows the cumulative proportions of the different EM’s through time, while figure 5.9 shows the

abundances per end member. The curves in figure 5.8 and the bold curve in figure 5.9 represent the

weighted averages for the different EM’s in order to better visualize the temporal variations. EM’s 1

and 2 are most dominating and together represent 70-80 % of the grain-size distribution. However,

their abundances varies significantly through time. The occurrence of EM’s 3 and 5 show less

variation and remain rather constant through time. EM 4 generally has a larger contribution to the

grain-size distributions during the last 11 ka. Its abundance during the period 11 – 25 ka is generally

very low and sometimes almost absent. Similar to the results of the mean grain size (fig. 5.1) the six

zones can be identified in the end-member abundances as well.

Figure 5.8.: Proportions of the different end members as a function of time. The curves represent the weighted averages of the

abundances. The different zones which were identified in the sediment core are indicated with numbers I – VI.

Zone I represents the period from 0-2.7 ka. In this period, EM 2 is most abundant while the frequency

of EM 1 increases from ~28 % to ~45 % (fig 5.8). The abundance of EM 2 shows two episodes of

reduction during this time interval, the first around 0.5 ka and a second between roughly 2 and 3 ka.

The second reduction episode is also illustrated in EM 1. EM 4 had two periods of increased

abundance, the first between 0 and 0.5 ka and the second from 2 to 2.7 ka (fig 5.9). The abundance

of EM 5 gradually increases from ~2 % to ~8 %.

Chapter V - Results

27

The period between 2.7 and 9.5 ka (zone II) features an gradual increase in abundance of EM 1 from

~45 % to 60 %, followed by a decrease towards ~45%. The maximal abundance of ~60 % is reached at

5.5 ka. The decrease in the abundance of EM 1 starts abruptly after its maximum and rose again at

6.5 ka to ~55 % (fig 5.8). Between 6.5 ka and 9 ka, its abundance generally decreased, although not

gradually but with some fluctuations. The abundances of EM’s 2, 4 and 5 remained rather constant in

this zone, although EM’s 4 and 5 reach higher abundances during the interval 5.5-6.5 ka, coincident

with a decrease in the abundances of EM 1 and 3 (fig 5.9).

Between 9.5 and 11.5 ka, EM 1 reaches an absolute minimum of ~15 %. This interval corresponds to

zone III in the sediment core, which is characterized by coarser sediments (fig 5.1). However, this

coarser interval does not show an increase in the coarser end members (EM 4 and 5), but rather

shows an prominent increase in EM’s 2 and 3 (fig 5.9). EM 2 increases from 18 % at the end of zone II

(9 ka) towards 60 % at 10.5 ka. EM 3 shows a similar increase, from 6 % at 9 ka towards 20 % at 10.5

ka. A slight increase is observed in the abundances of EM 4 and 5 during the period 11-11.5 ka.

Zone IV runs from 11.5 to ~15 ka. Similar to zone II, EM 1 represents around 50 % of the grain-size

distribution. However, from 13 to 14.5 ka, a significant decrease is present in the abundance of EM 1

(fig 5.8), which results in an increase in EM’s 2 and 3. End member 4 almost completely disappears

during this period, with an abundance of ~2 %. EM 5 remains constant around ~5 %.

The subsequent interval, between ~15 and ~20.7 ka (zone V), is featured by the highest abundances

of EM 1, up to 70 %, although values are decreasing around 19 ka. EM 2 reaches a minimum during

this period, EM 3 increases to ~12 %, while the other EM’s show little variation. Around 20.5 ka, a

severe decrease of EM 1 from ~65 % towards ~35 % is observed. The abundance of EM 2 increases

significantly from ~20 % towards ~45 % at this time. EM 4 remains low at ~2 %.

In the period from ~20.7 ka until 25 ka, which represents zone VI, the abundances of EM’s 1, 4 and 5

remain rather constant, with several short-term variations. EM 1 does show some fluctuations, with

an increase and decrease in its abundance between 21-22 ka, followed by a gradual increase from

~30% at 22 ka to ~40% at 25 ka. EM 4 becomes slightly more important between 21.5 and 22.5 ka,

but further remains its relatively low abundance. EM 2 varies between 40 % and 55 %, with a

decreasing trend from 23 to 25 ka. EM 3 gradually lowers towards 2 % at 22 ka, and increases again

afterwards to 12 %.

Chapter V - Results

28

Figure 5.9.: Abundances of the five end members as a function of time. The bold curve represents the weighted average. The identified

zones are indicated with numbers I – VI.

Chapter VI - Discussion

29

CHAPTER VI. Discussion

6.1. Identification of the end members

Based on their distinct characteristics, the correlations between the end-member abundances and

different proxies, an explanation what the different end members represent was established. Figure

6.1 compares the abundances of end members 1, 4 and 5 with the BIT-index from Verschuren et al.

(2009), the lake level fluctuations in Lake Challa from Moernaut et al. (2010) and insolation values at

the equator during March and September (Berger and Loutre, 1991). The BIT-index is used as a proxy

for local monsoonal rainfall, with higher values representing periods of increased humidity and

increased surface run-off. Identification of each end member is explained below and subsequently

used for a paleo-environmental interpretation.

Figure 6.1.: Comparison of the end-member abundances (weighted average) with insolation values at the equator during March and

September, the BIT-index and lake level fluctuations in Lake Challa. Numbers I-VI represent the different zones which were identified in the

grain-size variations. The limits of these zones are indicated with dotted lines. Gray bars indicate drought events in East Africa: EAD: East

African droughts, YD: Younger Dryas, LGM: Last Glacial Maximum. The green lines indicate the ages of the turbidite levels in the

CHALLACEA core.


30

6.1.1. End member 1

End member 1 corresponds with the finest fraction which is present in the sediments, with a mode

situated at 4 µm. With a percentage of 15-80%, it is the most abundant end member in the record.

Comparison of EM1 and the grain-size distribution of shortcores from Lake Challa (Eloy, unpublished

MSc thesis) shows a good overlap (fig 6.2). EM 1 shows an inverse correlation with the BIT-index and

the lake levels stands, with an increased amount of EM 1 during periods of low lake levels and vice

versa (fig 6.1). Based on these observations, EM 1 is concluded to represent the fine-grained

background sedimentation of Lake Challa. A drop of the lake level during more arid times results in a

larger catchment area and more erosion as well as more available sediments, leading to an increase

of clastic material entering the lake. The fine clayey material stays in suspension and only deposits

slowly, allowing this sediment fraction to reach the center of the lake.

Figure 6.2.: Identification of end member 1 (blue curve) by comparison with grain-size distributions from shortcores (grey curves) in Lake

Challa (after Eloy, unpublished MSc thesis).

6.1.2. End member 2

The second end member has a mode of 10 µm. As evident from Figure 6.3, there is a good

correlation between end member 2, the BIT-index and the lake-level variations. A higher abundance

of EM 2 is present during periods of increased humidity and vice versa. Microscope pictures of

distinct samples revealed the presence of diatoms in the samples next to clastic particles (see fig 6.5),

which indicates an insufficient working of the heavy liquid separation method.


31

Figure 6.3.: Comparison of the abundances of EM 2 and EM 3 with the BIT-index and lake-level fluctuations in Lake Challa. The bold line in

the end-member curves represents the weighted average. The identified zones are indicated with numbers I – VI.

Two dominant diatom species are present in the Lake Challa sediments, Nitzschia sp. 1 and

Gomphocymbella sp. 1 (Milne, 2007). The size of these species varies between 10 µm and 30 µm.

Due to their slim and elongated shape the grain-size measurements will give instable results.

Depending on their position in the laser beam the result will over- or underestimate the real particle

size, leading to intermediate grain size (fig 6.4). Based on the good correlation between the

hydrological fluctuations and the abundance of EM 2, this end member is concluded to represent the

diatom signal from the species with a size of 10 µm (Nitzschia sp. 1). Periods of higher precipitation

increased the nutrient concentrations in the lake, and therefore resulted in an increased diatom

productivity (Milne, 2007). Since the focus in this dissertation is on the siliciclastic fraction in the

sediments and not on the biogenic fraction, EM 2 was excluded from further interpretation.


32

Figure 6.4.: Sketch illustrating the variable particle size of diatoms in the Malvern Mastersizer, depending on their position in the laser

beam. A: small particle size measured, B: intermediate particle size measured, C: larger particle size measured.

6.1.3. End member 3

End member 3 has a mode situated at 20 µm. The abundance of EM 3 show variations which are very

similar to the variations of EM 2, with higher abundances of EM 3 observed during wetter periods

and vice versa (see fig 6.3). Based on the large similarities with EM 2, EM 3 is suggested to represent

a second diatom signal. Periods of low lake level stands most likely enhanced mixing of the water

layers in the lake, resulting in enhanced nutrient supply in the deeper parts of the water column. An

increased mixing during these periods results in the dominance of Gomphocymbella sp. 1 diatoms

(Barker et al., 2013), which are larger in size than the Nitzschia sp. 1 diatoms. This explains the higher

abundance of EM 3 in zones II and V. The mode of 20 µm matches with the size of the diatoms as

described above, and therefore EM 3 is concluded to represent the diatom signal from the larger-

sized diatoms (Gomphocymbella sp. 1). The high abundance of diatoms in the samples, as evident

from the microscope pictures (fig 6.5), support this conclusion. EM 3 is also not used for further

interpretation.


33

Figure 6.5.: Microscope images for six distinct samples in the CHALLACEA core, which revealed the presence of diatoms (D) next to clastic

sediment particles (C). Each sample is coming from a different zone (Z I – VI) in the core.

6.1.4. End member 4

The mode of end member 4 is situated at 60 µm. The signal of this EM is rather complex. During the

Pleistocene, the abundance is moderately low (1-6 %), while in the Holocene the abundance is more

pronounced including several distinct maxima (up to 18 %). Correlation with BIT and lake-level

fluctuations is generally good, except in zone III. Transitions from low to high lake levels are indicated

by a low abundance of EM 4. Increasing humidity, evident as a rise in BIT-values or lake level, is

followed by an increasing abundance of EM 4. Correlation with BIT-results revealed that peak


34

abundances are reached during periods of maximal humidity. However, the maximal abundance of

EM 4 (8-9 ka) is not reached during a period of maximal humidity, but rather in a period towards

more arid conditions. An abrupt rise in the abundance of EM 4 is observed after drought periods in

equatorial Africa (fig 6.1). A maximum is immediately present after the Younger Dryas and the East

African droughts, while a post-LGM maximum was only established after ~0.5 ka. A comparison of

EM 4 with the grain-size distribution of onshore samples from Lake Challa (Eloy, unpublished MSc

thesis) show a good overlap with a distinct peak around 60 µm (fig 6.6). This indicates that this end

member is most likely originating from this source area. Based on the described observations, EM 4 is

concluded to represent proximal aeolian dust. However, since there is no excessive amount of data

available from possible dust source areas near Lake Challa, future sampling in possible source areas

should strengthen this interpretation. Furthermore, the deposition of wind-blown sediments in the

lake shows a monsoonal signal, with stronger monsoons resulting in a higher sedimentation rate of

aeolian dust. This can be explained by the dry monsoonal winds in East Africa. As described in

chapter II, passage of these winds above the eastern Sahara and friction with the East African

shoreline results in a decreasing humidity of the monsoonal winds (Nicholson, 1996). However, still

some moisture remains present in the winds, so that stronger monsoons also result in increased

humidity. This explains why an increase in wind-blown sediments is observed during moist times. The

link between aeolian dust in Lake Challa and the monsoons will be described more into detail below.

Figure 6.6.: Identification of EM 4 (orange curve) by comparison with grain-size distributions of onshore samples from Lake Challa (grey

curves). The inset shows the locations of the onshore samples (yellow dots) with respect to the lake (grey area) and the lake catchment

area (dotted line).


35

6.1.5. End member 5

End member 5 corresponds with the coarsest fraction in the sediments, with a mode situated at 400

µm. The abundance of this end member shows some variation through time, however a correlation

with the BIT-index and lake-level fluctuations is not evident (fig 6.1). Higher abundances are generally

observed during transitions towards lower lake levels and during periods of increased rainfall.

However, the high abundances are generally only lasting for a short amount of time (~1000 yr).

Correlation with onshore samples from the crater rim of Lake Challa (Eloy, unpublished MSc thesis)

show a strong overlap between EM 5 and the grain-size distributions of the onshore samples coming

from the crater rim (fig 6.7). EM 5 is therefore concluded to represent erosional material from the

crater rim of the lake. The coarser nature of this material is explained by the close proximity of the

source area. The sediment is only transported to the center of the lake under special conditions, e.g.

during transitions of lake level stands or by collapsing of the crater rim when the lake level was high.

These periods are characterized by an increased transportation energy, and therefore allow the

coarse particles to reach the center of the lake.

Figure 6.7.: Identification of EM 5 (blue curve) by comparison with grain-size distributions of onshore samples (grey curves) from the crater

rim of Lake Challa (after Eloy, unpublished MSc thesis). The inset shows the locations of the onshore samples with respect to Lake Challa.


36

Correlation between EM 5 and the turbidite intervals gives an insight in the erosional dynamics that

occurred during the last 25 ka. The turbidites in the core were not sampled for grain-size analysis, so

no information is presented about them in the figures. However, an increasing abundance of EM 5 is

observed in the periods leading to the age when a turbidite was deposited (5.9, 8, 22.2 and 23.4 ka

respectively). Correlation with the lake-level fluctuations indicate that turbidites were triggered

during transitions from a high to a low lake level (see fig 6.1). A drop in lake level exposed the

sedimentary material on the edges of the lake. As evident from the bathymetry and seismic profiles

of Lake Challa (Moernaut et al., 2010), the upper slopes are dipping between 30° and 90° (see fig

2.2), resulting in an unstable ground when exposed. Abundance increase of EM 5 in the period

leading to a turbidite is interpreted as build-up of slope instabilities in the crater rim, which

ultimately led to triggering of the turbidites. After deposition of the turbidite, the abundance

abruptly decreased.

6.2. Paleo-environmental implications of the dust fraction

The low abundance of the aeolian fraction (EM 4) during the Pleistocene, followed by an increased

abundance in the Holocene, is interpreted as reflecting the monsoonal signal in equatorial Africa,

rather than reflecting lake level changes or increasing run-off. Aeolian sediments are deposited

during the dry season of a monsoonal cycle, with an increased dust input during periods of a

strengthened monsoon.

In the period corresponding with zone VI (20.7-25 ka), insolation on the equator was maximal during

the summer months (see fig 6.1). During this period the ITCZ was located north of Lake Challa,

resulting in an enhanced SE monsoon. An increased abundance of EM 4 is observed between 21.7

and 22.7 ka. Barker et al. (2011) described a period of reduced monsoonal rainfall in Lake Challa

between 22 and 25 ka, based on oxygen isotopes from biogenic silica in the sediments. However, the

observations from Barker et al. (2011) are contradictory with the BIT and lake level fluctuations,

which indicate a rather humid period. A prolonged dry season is assumed during this interval, due to

a reduction of the long rains linked to a weak SE monsoon during this interval. The longer-lasting dry

season of the monsoon resulted in an increasing dust deposition in Lake Challa.

During the late Pleistocene (zones IV and V), maximal insolation at the equator occurred during the

winter months. The ITCZ moved southward, resulting in a dominance of the NE monsoonal system.

As described in chapter III, the NE monsoonal system was weakened during the LGM and YD, most

likely because Northern Hemisphere high-latitudinal ice sheets and the Indian Ocean SST had a

dominant influence over the African monsoonal system (Barker and Gasse, 2003; Tierney and Russell,

2007). This is reflected in the low abundance of EM 4, which shows little input of aeolian sediment in

the lake during these periods due to the reduced monsoonal activity.

At the beginning of the Holocene (zone III), the NE monsoonal system regained strength. An abrupt

resumption of the monsoon activity is described by Garcin et al. (2007) during the transition from the

YD to the Holocene. The abundance of EM 4 increases after the YD, although not very significant.

These observations are supported by the study from Weldeab et al. (2014), who reconstructed the

evolution of the East African monsoonal strength during the Holocene. They describe a gradually

increasing monsoonal strength during the early Holocene, which is in agreement with the increasing

input of aeolian sediments during the period from ~11.5 ka to ~9.5 ka (fig 6.1). Weldeab et al. (2014)

and Jung et al. (2004) further described that the monsoonal strength gradually decreased from 8.7 ka


37

onwards. The maximal abundance of EM 4 is reached at ~9 ka, which indicates a good correlation

with the described monsoonal evolution.

The mid and late Holocene (zone I and II) are characterized by a maximal insolation during the

summer months. Hence, the ITCZ was situated north of Lake Challa. The SE monsoonal system was

dominant during this interval. Consequently, the aeolian input in Lake Challa was decreasing due to

the weakening of the SE East African monsoon. During the last 5 ka, similar conditions were present

as during zone VI, with a reduced long rain season resulting in a longer dry season (Barker et al.,

2011). This is reflected in the abundance of EM 4, with a higher aeolian input during the last 5 ka (fig

6.1).

Since the aeolian sediment in Lake Challa has a rather coarse grain size of about 60 µm, the dust

source must be situated close to the lake. The most likely dust sources are situated to the

northeastern and northwestern side of the lake. The onshore samples in which overlap is observed

with EM 4 are situated to the south-southeastern side of the lake and in the northwestern side (see

fig 6.6). The wind in this area is dominantly

coming from the southeast and the

northeast. Consequently, dust is more

likely to come from these directions. The

aeolian dust fraction in the onshore

samples is blown in by the southeastern

monsoonal winds, onto the southwestern

crater rim, in the lake and over the lake

onto the northwestern side. Figure 6.8

compares a vegetation map from the area

(Sinninghe Damsté et al., 2011) with

satellite imagery (Google Earth). At the

western side of the lake, the montane

forests of the Mt Kilimanjaro and other

vegetation types cover main parts of the

surface, reducing possible dust source

areas. At the eastern side of the lake the

vegetation consists of an savanna

landscape with open bush- and grassland.

This type of patchy vegetation allows

particles to be lifted up and transported by

wind. Moreover, as visible in figure 6.8,

rivers are present in this area. Since it is

known that dried out- lake or river beds

are an important source for aeolian

material (Washington et al., 2003), these

rivers and their catchments would provide

a good source of dust, during periods with

low precipitation. Figure 6.8.: Comparison of a vegetation map from Lake Challa (Sinninghe

Damsté et al., 2011) with a satellite image (Google Earth) from the lake in

order to pinpoint the dust source areas.

Chapter VII - Conclusions

38

CHAPTER VII. Conclusions

This study regarded the terrigenous fraction from the sedimentary infill of Lake Challa

(Kenya/Tanzania). A high-resolution grain-size analysis on the CHALLACEA composite core was

carried out using laser diffractometry. Due to very large abundance of diatom frustules in the

sediment, pre-treatment of the samples was done using a heavy liquid separation technique

(Madella et al., 1998). Downcore grain-size variations allowed to distinguish six different zones which

share the same characteristics. These zones show good correlation with the already available proxies,

like the lake-level fluctuations and the BIT-index (Moernaut et al., 2010; Verschuren et al., 2009). The

obtained grain-size distributions were unmixed into geologically representative end members in

order to gain insights about the mechanisms that transported siliciclastic particles to the lake and

how their variation through time was influenced by prevailing climate conditions.

Five end members were identified after end-member modeling. The first end member represents

clayey background sedimentation and shows good correlation with BIT and lake-level fluctuations.

Higher abundances are observed during arid periods with low lake levels, and an enlarged catchment

area, resulting in increased input of clastic material. Sedimentation of clayey material is the most

important sedimentation mechanism, except during the interval 11.5 – 9.5 ka.

Two diatom end members are identified, implying an unsatisfactory working of the heavy liquid

separation method. The finer-sized end member is interpreted to represent the smaller diatom

fraction (Nitzschia sp. 1), while the slightly coarser-sized end member most likely represents larger

diatom species (Gomphocymbella sp. 1). Increased precipitation (high lake levels) enhances nutrient

input in the lake, resulting in diatom blooms.

The fourth end member represents the aeolian fraction in the sediments. A monsoonal signal is

recorded in this sediment fraction. Deposition of wind-blown sediments occurs during the dry season

of a monsoon, with increased aeolian input during periods of stronger monsoons. In general, dust

input was more important during the Holocene than during the Pleistocene, indicating that the

monsoonal system in equatorial East Africa increased in strength following the Younger Dryas.

Periods of weakened NE monsoons are observed during the LGM and YD, as evident from a low dust

input. A transitional interval is present from 11.5 to 9.5 ka, during which the monsoonal system

gradually gained strength. During the middle and late Holocene, the SE monsoonal strength initially

decreased, but an increased dust input is observed during the last 5 ka when the dry season of the

monsoon was prolonged. The dust fraction in Lake Challa sediments is rather coarse (~60 µm) and

therefore the provenance of this fraction must be situated close by the lake. Due to the prevailing

wind direction and the availability of the dust sources, aeolian transport is suggested to come

predominantly from the eastern-southeastern side of the lake.

End member 5 is representing run-off of coarse erosional material, originating from the crater rim.

Increasing abundances are observed during transitions from high to low lake levels, since lake level

lowering exposes the steep crater rim and causes instabilities. Moreover, increasing abundances are

observed in periods leading to the deposition of turbidites, which points to build-up of slope-

instabilities and ultimately failure of the rim. This depositional mechanism remains equally important

through time and is the second most important mechanism for transport of clastic material to the

lake.

Chapter VII - Conclusions

39

Future research should include improving the heavy liquid separation technique in order to

completely remove the diatoms which are present in the sediment. The diatoms are probably

obscuring the signal from the terrigenous fraction, therefore making the identification and

interpretation of the end members more difficult. Furthermore, identification of the sediment

provenance could be performed by combining XRD-analysis with grain-size analysis from onshore

samples of potential dust source areas. Isotope records and geochemical proxies could be combined

with the already available proxies in order to create an even broader view on the environmental

evolution of Lake Challa.

40

REFERENCE LIST

Barker, P., Gasse, F., 2003. New evidence for a reduced water balance in East Africa during the Last Glacial Maximum: Implication for model-data comparison. Quat. Sci. Rev. 22, 823–837.

Barker, P.A., Hurrell, E.R., Leng, M.J., Plessen, B., Wolff, C., Conley, D.J., Keppens, E., Milne, I., Cumming, B.F., Laird, K.R., Kendrick, C.P., Wynn, P.M., Verschuren, D., 2013. Carbon cycling within an East African lake revealed by the carbon isotope composition of diatom silica: A 25-ka record from Lake Challa, Mt. Kilimanjaro. Quat. Sci. Rev. 66, 55–63.

Barker, P.A., Hurrell, E.R., Leng, M.J., Wolff, C., Cocquyt, C., Sloane, H.J., Verschuren, D., 2011. Seasonality in equatorial climate over the past 25 k.y. Revealed by oxygen isotope records from Kilimanjaro. Geology 39, 1111–1114.

Barker, P.A., Leng, M.J., Greenwood, P.B., Swain, D.L., Perrott, R.A., Telford, R.J., 2001. A 14 , 000-Year Oxygen Isotope Record from Diatom Silica in Two Alpine Lakes on Mt . Kenya 292, 2307–2310.

Berger, A., 1978. Long-Term Variations of Daily Insolation and Quaternary Climatic Changes. J. Atmos. Sci. 35, 2362–2367.

Berger, A., Loutre, M.F., 1991. Insolation values for the climate of the last 10 million years. Quat. Sci. Rev. 10, 297–317.

Beuning, K.R.M., Talbot, M.R., Kelts, K., 1997. A revised 30,000-year paleoclimatic and paleohydrologic history of Lake Albert, East Africa. Palaeogeogr. Palaeoclimatol. Palaeoecol. 136, 259–279.

Blaauw, M., van Geel, B., Kristen, I., Plessen, B., Lyaruu, A., Engstrom, D.R., van der Plicht, J., Verschuren, D., 2011. High-resolution 14C dating of a 25,000-year lake-sediment record from equatorial East Africa. Quat. Sci. Rev. 30, 3043–3059.

Black, E., Slingo, J.M., Sperber, K.R., 2003. An observational study of the relationship between excessively strong short rains in coastal East Africa and Indian Ocean SST. Mon. Weather Rev. 74–94.

Blott, S.J., Pye, K., 2001. Gradistat : a Grain Size Distribution and Statistics Package for the Analysis of Unconsolidated Sediments 1248, 1237–1248.

Buckles, L.K., Weijers, J.W.H., Verschuren, D., Sinninghe Damsté, J.S., 2014. Sources of core and intact branched tetraether membrane lipids in the lacustrine environment: Anatomy of Lake Challa and its catchment, equatorial East Africa. Geochim. Cosmochim. Acta 140, 106–126.

Camberlin, P., Okoola, R.E., 2003. The onset and cessation of the “‘ long rains ’” in eastern Africa and their interannual variability. Theor. Appl. Climatol. 54, 43–54.

Camberlin, P., Philippon, N., 2002. The East African March – May Rainy Season : Associated Atmospheric Dynamics and Predictability over the 1968 – 97 Period. J. Clim. 15, 1002–1019.

DeMenocal, P., Ortiz, J., Guilderson, T., Adkins, J., Sarnthein, M., Baker, L., Yarusinsky, M., 2000. Abrupt onset and termination of the African Humid Period: Rapid climate responses to gradual insolation forcing. Quat. Sci. Rev. 19, 347–361.

Downie, C., Wilkinson, P., 1972. The Geology of Kilimanjaro. Dep. Geol. Univ. Sheffield, Shefield, pp. 253.

Eshel, G., Levy, G.J., Mingelgrin, U., Singer, M.J., 2004. Critical Evaluation of the Use of Laser Diffraction for Particle-Size Distribution Analysis. Soil Sci. Soc. Am. J. 68, 736–743.

Garcin, Y., Vincens, A., Williamson, D., Buchet, G., Guiot, J., 2007. Abrupt resumption of the African Monsoon at the Younger Dryas-Holocene climatic transition. Quat. Sci. Rev. 26, 690–704.

Gasse, F., 2000. Hydrological changes in the African tropics since the Last Glacial Maximum. Quat. Sci. Rev. 19, 189–211.

41

Hamann, Y., Ehrmann, W., Schmiedl, G., Krüger, S., Stuut, J.B., Kuhnt, T., 2008. Sedimentation processes in the Eastern Mediterranean Sea during the Late Glacial and Holocene revealed by end-member modelling of the terrigenous fraction in marine sediments. Mar. Geol. 248, 97–114.

Holz, C., Stuut, J.B.W., Henrich, R., 2004. Terrigenous sedimentation processes along the continental margin off NW Africa: Implications from grain-size analysis of seabed sediments. Sedimentology 51, 1145–1154.

Holz, C., Stuut, J.B.W., Henrich, R., Meggers, H., 2007. Variability in terrigenous sedimentation processes off northwest Africa and its relation to climate changes: Inferences from grain-size distributions of a Holocene marine sediment record. Sediment. Geol. 202, 499–508.

Jones, P.D., Osborn, T.J., Briffa, K.R., 2001. The evolution of climate over the last millennium. Science (80-. ). 292, 662–7.

Jung, S.J.A., Davies, G.R., Ganssen, G.M., Kroon, D., 2004. Stepwise Holocene aridification in NE Africa deduced from dust-borne radiogenic isotope records. Earth Planet. Sci. Lett. 221, 27–37.

Kiage, L.M., Liu, K. -b., 2006. Late Quaternary paleoenvironmental changes in East Africa: a review of multiproxy evidence from palynology, lake sediments, and associated records. Prog. Phys. Geogr. 30, 633–658.

Kristen, I., 2010. Investigations on rainfall variability during the late Quaternary based on geochemical analyses of lake sediments from tropical and subtropical southern Africa. Dtsch. GeoForschungZentrum Sci. Tech. Rep. STR10/01.

Krumbein, W.C., 1941. Measurement and Geological Significance of Shape and Roundness of Sedimentary Particles. J. Sediment. Petrol. 11, 64–72.

Madella, M., Powers-Jones, a. H.A., Jones, M.K.M., 1998. A Simple Method of Extraction of Opal Phytoliths from Sediments Using a Non-Toxic Heavy Liquid. J. Archaeol. Sci. 25, 801–803.

Meyer, I., Davies, G.R., Vogt, C., Kuhlmann, H., Stuut, J.B.W., 2013. Changing rainfall patterns in NW Africa since the Younger Dryas. Aeolian Res. 10, 111–123.

Milliman, J.D., Syvitski, J.P.M., 1992. Geomorphic/Tectonic Control of Sediment Discharge to the Ocean: The Importance of Small Mountainous Rivers. J. Geol. 100, 525–544.

Milne, I., 2007. Climate and Environmental change inferred from Diatom Communities in Lake Challa (Kenya). MSc Thesis.

Moernaut, J., Verschuren, D., Charlet, F., Kristen, I., Fagot, M., De Batist, M., 2010. The seismic-stratigraphic record of lake-level fluctuations in Lake Challa: Hydrological stability and change in equatorial East Africa over the last 140 kyr. Earth Planet. Sci. Lett. 290, 214–223.

Mutai, C.C., Ward, M.N., 2000. East African rainfall and the tropical circulation/convection on intraseasonal to interannual timescales. J. Clim. 13, 3915–3939.

Nicholson, S.E., 1996. A review of climate dynamics and climate variability in Eastern Africa. Limnol. Climatol. Paleoclimatology East African Lakes.

Nicholson, S.E., Nash, D.J., Chase, B.M., Grab, S.W., Shanahan, T.M., Verschuren, D., Asrat, A., Lezine, A.M., Umer, M., 2013. Temperature variability over Africa during the last 2000 years. The Holocene 23, 1085–1094.

Ott, S.T., Ott, A., Martin, D.W., Young, J.A., 1991. Analysis of Trans-Atlantic Saharan Dust outbreak based on satellite and GATE data. Mon. Weather Rev. 119, 1832–1850.

Passega, R., 1957. Texture as Characteristic of Clastic Deposition. Am. Assoc. Pet. Geol. Bull. 41, 1952–1984.

Paterson, G.A., Heslop, D., 2015. New methods for unmixing sediment grain size data. Geochemistry, Geophys.

42

Geosystems 16, 4494–4506.

Payne, B.R., 1970. Water balance of lake Chala and its relation to groundwater from Tritium and stable isotope data. J. Hydrol. 11, 47–58.

Petters, S.W., 1991. Regional Geology of Africa. Springer-Verlag, Berlin – Heidelb. 722.

Prospero, J.M., Glaccum, R.A., Nees, R.T., 1981. Atmospheric transport of soil dust from Africa to South America. Nature 289, 570–572.

Roberts, N., Taieb, M., Barker, P., Damnati, B., Icole, M., Williamson, D., 1993. Timing of the Younger Dryas event in East Africa from lake level changes. Nature 366, 146–148.

Ryner, M., Gasse, F., Rumes, B., Verschuren, D., 2007. Climatic and hydrological instability in semi-arid equatorial East Africa during the late Glacial to Holocene transition: A multi-proxy reconstruction of aquatic ecosystem response in northern Tanzania. Palaeogeogr. Palaeoclimatol. Palaeoecol. 248, 440–458.

Schefuß, E., Kuhlmann, H., Mollenhauer, G., Prange, M., Pätzold, J., 2011. Forcing of wet phases in southeast Africa over the past 17,000 years. Nature 480, 509–12.

Schefuß, E., Schouten, S., Schneider, R.R., 2005. Climatic controls on central African hydrology during the past 20,000 years. Nature 437, 1003–6.

Schüler, L., Hemp, A., Zech, W., Behling, H., 2012. Vegetation, climate and fire-dynamics in East Africa inferred from the Maundi crater pollen record from Mt Kilimanjaro during the last glacial-interglacial cycle. Quat. Sci. Rev. 39, 1–13.

Sinninghe Damsté, J.S., Verschuren, D., Ossebaar, J., Blokker, J., van Houten, R., van der Meer, M.T.J., Plessen, B., Schouten, S., 2011. A 25,000-year record of climate-induced changes in lowland vegetation of eastern equatorial Africa revealed by the stable carbon-isotopic composition of fossil plant leaf waxes. Earth Planet. Sci. Lett. 302, 236–246.

Stuut, J.B.W., Prins, M.A., Schneider, R.R., Weltje, G.J., Fred Jansen, J.H., Postma, G., 2002. A 300-kyr record of aridity and wind strength in southwestern Africa: Inferences from grain-size distributions of sediments on Walvis Ridge, SE Atlantic. Mar. Geol. 180, 221–233.

Talbot, M.R., Filippi, M.L., Jensen, N.B., Tiercelin, J.J., 2007. An abrupt change in the African monsoon at the end of the Younger Dryas. Geochemistry, Geophys. Geosystems 8.

Thompson, L.G., Mosley-Thompson, E., Davis, M.E., Henderson, K.A., Brecher, H.H., Zagorodnov, V.S., Mashiotta, T.A., Lin, P., Mikhalenko, V.N., Hardy, D.R., Beer, J., 2002. Kilimanjaro ice core records: evidence of holocene climate change in tropical Africa. Science (80-. ). 298, 589–593.

Tierney, J.E., Lewis, S.C., Cook, B.I., LeGrande, A.N., Schmidt, G.A., 2011a. Model, proxy and isotopic perspectives on the East African Humid Period. Earth Planet. Sci. Lett. 307, 103–112.

Tierney, J.E., Russell, J.M., 2007. Abrupt climate change in southeast tropical Africa influenced by Indian monsoon variability and ITCZ migration. Geophys. Res. Lett. 34, 1–6.

Tierney, J.E., Russell, J.M., Huang, Y., 2010. A molecular perspective on Late Quaternary climate and vegetation change in the Lake Tanganyika basin, East Africa. Quat. Sci. Rev. 29, 787–800.

Tierney, J.E., Russell, J.M., Huang, Y., Damsté, J.S.S., Hopmans, E.C., Cohen, A.S., 2008. Northern Hemisphere Controls on Tropical Southeast African Climate During the Past 60,000 Years. Science (80-. ). 322, 252–255.

Tierney, J.E., Russell, J.M., Sinninghe Damsté, J.S., Huang, Y., Verschuren, D., 2011b. Late Quaternary behavior of the East African monsoon and the importance of the Congo Air Boundary. Quat. Sci. Rev. 30, 798–807.

43

Verschuren, D., Laird, K.R., Cumming, B.F., 2000. Rainfall and drought in equatorial east Africa during the past 1,100 years. Nature 403, 410–414.

Verschuren, D., Sinninghe Damsté, J.S., Moernaut, J., Kristen, I., Blaauw, M., Fagot, M., Haug, G.H., 2009. Half-precessional dynamics of monsoon rainfall near the East African Equator. Nature 462, 637–641.

Visher, G.S., 1969. Grain Size Distributions and Depositionalprocesses. J. Sediment. Petrol. 39, 1074–1106.

Washington, R., Todd, M., Middleton, N.J., Goudie, A.S., 2003. Dust-storm source areas determined by the total ozone monitoring spectrometer and surface observations. Ann. Assoc. Am. Geogr. 93, 297–313.

Weldeab, S., Menke, V., Schmiedl, G., 2014. The pace of East African monsson evolution during the Holocene. Geophys. Res. Lett. 41, 1724–1731.

Weltje, G.J., 1997. End-Member Modeling of Compositional Data: Numerical-Statistical Algorithms for Solving the Explicit Mixing Problem 1: The mixing problem. Math. Geol. 29.

Weltje, G.J., Prins, M.A., 2007. Genetically meaningful decomposition of grain-size distributions. Sediment. Geol. 202, 409–424.

Weltje, G.J., Prins, M.A., 2003. Muddled or mixed? Inferring palaeoclimate from size distributions of deep-sea clastics. Sediment. Geol. 162, 39–62.

Weltje, G.J., von Eynatten, H., 2004. Quantitative provenance analysis of sediments: Review and outlook. Sediment. Geol. 171, 1–11.

Wolff, C., Haug, G.H., Timmermann, A., Damsté, J.S.S., Brauer, A., Sigman, D.M., Cane, M.A., Verschuren, D., 2011. Reduced Interannual Rainfall Variability in East Africa During the Last Ice Age. Science (80-. ). 333, 743–747.

Wolff, C., Kristen-Jenny, I., Schettler, G., Plessen, B., Meyer, H., Dulski, P., Naumann, R., Brauer, A., Verschuren, D., Haug, G.H., 2014. Modern seasonality in Lake Challa (Kenya/Tanzania) and its sedimentary documentation in recent lake sediments. Limnol. Oceanogr. 59, 1621–1636.

44

APPENDIX

Appendix A: List of the grain-size samples, their composite core section and the depth of the

sampled core increment. Black lines indicate the levels of the turbidites between two samples.

Sample name core section

Depth interval (cm comp. depth)

CHALLA05- 002 1G 0-4 CHALLA05- 014 1G 12-16 CHALLA05- 026 1G 24-28 CHALLA05- 038 1G 36-40 CHALLA05- 050 2Ka 48-52 CHALLA05- 062 2Ka 60-64 CHALLA05- 074 2Ka 72-76 CHALLA05- 086 2Ka 84-88 CHALLA05- 098 3PIb 96-100 CHALLA05- 110 2Kb 108-112 CHALLA05- 118 2Kb 116-120 CHALLA05- 134 2Kb 132-136 CHALLA05- 146 2Kb 144-148 CHALLA05- 158 2Kb 156-160 CHALLA05- 170 2Kb 168-172 CHALLA05- 182 2Kb 180-184 CHALLA05- 194 2Kc 192-196 CHALLA05- 206 2Kc 204-208 CHALLA05- 218 2Kc 216-220 CHALLA05- 230 2Kc 228-232 CHALLA05- 242 2Kc 240-244 CHALLA05- 254 2Kc 252-256 CHALLA05- 266 2Kc 264-268 CHALLA05- 278 4PIb 276-280 CHALLA05- 290 4PIc 288-292 CHALLA05- 302 4PIc 300-304 CHALLA05- 314 4PIc 312-316 CHALLA05- 326 4PIc 324-328 CHALLA05- 338 4PIc 336-340 CHALLA05- 350 4PIc 348-352 CHALLA05- 362 2PIIa 360-364 CHALLA05- 374 2PIIa 372-376 CHALLA05- 386 2PIIa 384-388 CHALLA05- 398 2PIIa 396-400 CHALLA05- 410 4PIIa 408-412 CHALLA05- 422 4PIIa 420-424 CHALLA05- 434 4PIIa 432-436 CHALLA05- 446 4PIIa 444-448 CHALLA05- 458 4PIIa 456-460 CHALLA05- 470 4PIIa 468-472 CHALLA05- 482 4PIIa 480-484

CHALLA05- 518 4PIIb 516-520 CHALLA05- 530 4PIIb 528-532 CHALLA05- 542 4PIIb 540-544 CHALLA05- 554 4PIIb 552-556 CHALLA05- 566 4PIIb 564-568 CHALLA05- 578 4PIIb 576-580 CHALLA05- 590 4PIIc 588-592 CHALLA05- 602 4PIIc 600-604 CHALLA05- 614 4PIIc 612-616 CHALLA05- 638 4PIIc 636-640 CHALLA05- 650 4PIIc 648-652 CHALLA05- 662 4PIIc 660-664 CHALLA05- 674 4PIIc 672-676

CHALLA05- 722 3PIIIb 720-724 CHALLA05- 734 2PIIIa 732-736 CHALLA05- 746 4PIIIa 744-748 CHALLA05- 758 4PIIIa 756-760 CHALLA05- 770 4PIIIa 768-772 CHALLA05- 774 4PIIIa 772-776 CHALLA05- 782 4PIIIa 780-784 CHALLA05- 794 4PIIIa 792-796

CHALLA05- 806 2PIIIb 804-808 CHALLA05- 818 2PIIIb 816-820 CHALLA05- 830 2PIIIb 828-832 CHALLA05- 842 2PIIIb 840-844 CHALLA05- 854 4PIIIb 852-856 CHALLA05- 866 4PIIIb 864-868 CHALLA05- 878 4PIIIb 876-880 CHALLA05- 890 4PIIIb 888-892 CHALLA05- 902 4PIIIc 900-904 CHALLA05- 914 4PIIIc 912-916 CHALLA05- 926 4PIIIc 924-928 CHALLA05- 938 3PIVa 936-940 CHALLA05- 950 3PIVa 948-952 CHALLA05- 958 3PIVa 956-960 CHALLA05- 962 3PIVa 960-964 CHALLA05- 966 3PIVa 964-968 CHALLA05- 970 3PIVa 968-972 CHALLA05- 974 3PIVa 972-976 CHALLA05- 986 3PIVa 984-988 CHALLA05- 998 3PIVa 996-1000 CHALLA05- 1010 3PIVb 1008-1012 CHALLA05- 1022 3PIVb 1020-1024 CHALLA05- 1034 3PIVb 1032-1036 CHALLA05- 1046 3PIVb 1044-1048 CHALLA05- 1050 3PIVb 1048-1052 CHALLA05- 1058 3PIVb 1056-1060 CHALLA05- 1070 3PIVb 1068-1072 CHALLA05- 1074 3PIVb 1072-1076 CHALLA05- 1082 3PIVb 1080-1084 CHALLA05- 1094 3PIVb 1092-1096 CHALLA05- 1106 3PIVc 1104-1108 CHALLA05- 1118 4PIVa 1116-1120 CHALLA05- 1130 4PIVb 1128-1132 CHALLA05- 1142 4PIVb 1140-1144 CHALLA05- 1154 4PIVb 1152-1156 CHALLA05- 1166 4PIVb 1164-1168 CHALLA05- 1178 4PIVb 1176-1180 CHALLA05- 1190 4PIVb 1188-1192 CHALLA05- 1202 4PIVb 1200-1204 CHALLA05- 1214 4PIVb 1212-1216 CHALLA05- 1226 4PIVc 1224-1228 CHALLA05- 1238 4PIVc 1236-1240 CHALLA05- 1250 3PVa 1248-1252 CHALLA05- 1262 3PVa 1260-1264 CHALLA05- 1274 3PVa 1272-1276 CHALLA05- 1286 3PVa 1284-1288 CHALLA05- 1298 3PVa 1296-1300 CHALLA05- 1310 3PVa 1308-1312 CHALLA05- 1322 3PVa 1320-1324 CHALLA05- 1334 4PVa 1332-1336 CHALLA05- 1346 4PVa 1344-1348 CHALLA05- 1358 3PVb 1356-1360 CHALLA05- 1370 3PVb 1368-1372 CHALLA05- 1382 3PVb 1380-1384 CHALLA05- 1394 3PVb 1392-1396 CHALLA05- 1406 3PVb 1404-1408 CHALLA05- 1418 3PVb 1416-1420 CHALLA05- 1430 4PVb 1428-1432 CHALLA05- 1442 4PVb 1440-1444 CHALLA05- 1454 4PVb 1452-1456 CHALLA05- 1466 4PVb 1464-1468 CHALLA05- 1478 4PVb 1476-1480 CHALLA05- 1490 4PVb 1488-1492 CHALLA05- 1502 2PVIc 1500-1504

45

CHALLA05- 1514 2PVIc 1512-1516 CHALLA05- 1526 2PVIc 1524-1528 CHALLA05- 1538 2PVIc 1536-1540 CHALLA05- 1550 2PVIc 1548-1552 CHALLA05- 1562 3PVIa 1560-1564 CHALLA05- 1586 2PVIIa 1584-1588 CHALLA05- 1598 2PVIIa 1596-1600 CHALLA05- 1610 2PVIIa 1608-1612 CHALLA05- 1622 2PVIIa 1620-1624 CHALLA05- 1634 2PVIIa 1632-1636 CHALLA05- 1646 2PVIIa 1642-1646 CHALLA05- 1658 2PVIIa 1656-1660 CHALLA05- 1670 3PVIb 1668-1672 CHALLA05- 1682 2PVIIb 1680-1684 CHALLA05- 1694 2PVIIb 1692-1696 CHALLA05- 1706 2PVIIb 1704-1708 CHALLA05- 1718 3PVIc 1716-1720 CHALLA05- 1730 3PVIc 1728-1732 CHALLA05- 1742 4PVIb 1740-1744 CHALLA05- 1754 4PVIb 1752-1756 CHALLA05- 1766 4PVIb 1764-1768 CHALLA05- 1778 4PVIb 1776-1780 CHALLA05- 1790 3PVIIa 1788-1792 CHALLA05- 1802 3PVIIa 1800-1804 CHALLA05- 1814 3PVIIa 1812-1816 CHALLA05- 1826 3PVIIa 1824-1828 CHALLA05- 1838 3PVIIa 1836-1840 CHALLA05- 1850 3PVIIa 1848-1852 CHALLA05- 1862 3PVIIa 1860-1864 CHALLA05- 1874 3PVIIa 1872-1876 CHALLA05- 1886 3PVIIa 1884-1888 CHALLA05- 1898 4PVIIa 1896-1900

CHALLA05- 1910 4PVIIa 1908-1912

CHALLA05- 1934 4PVIIa 1932-1936 CHALLA05- 1946 4PVIIa 1944-1948 CHALLA05- 1958 4PVIIa 1956-1960 CHALLA05- 1970 4PVIIa 1968-1972 CHALLA05- 1982 4PVIIa 1980-1984 CHALLA05- 1994 4PVIIb 1992-1996 CHALLA05- 2006 4PVIIb 2004-2008 CHALLA05- 2014 4PVIIb 2012-2016 CHALLA05- 2018 4PVIIb 2016-2020

CHALLA05- 2054 4PVIIb 2052-2056 CHALLA05- 2066 2PVIIIc 2064-2068 CHALLA05- 2078 2PVIIIc 2076-2080 CHALLA05- 2090 2PVIIIc 2088-2092 CHALLA05- 2102 2PVIIIc 2100-2104 CHALLA05- 2114 4PVIIc 2112-2116 CHALLA05- 2126 4PVIIc 2124-2128 CHALLA05- 2138 4PVIIc 2136-2140 CHALLA05- 2150 4PVIIc 2148-2152 CHALLA05- 2162 4PVIIc 2160-2164

The palaeo-environmental history of equatorial East Africa ... · East Africa: Implications from...

Documents

Transcript of The palaeo-environmental history of equatorial East Africa ... · East Africa: Implications from...