Assessing the temporal mixing and stratification in Lake Kivu

Assessing the temporal mixing and stratification in

Lake Kivu

Thesis submitted for the award of the title

“Master of Science”

By

Tuyisenge Janvière MSc Thesis ES.18.10

------------------------

This thesis is submitted in partial fulfilment of the requirements of the Joint academic degree of

Master of Science in Limnology and Wetland Management

Jointly awarded by

The University of Natural Resources and Life Sciences (Boku), Vienna, Austria

UNESCO-IHE Institute for Water Education, Delft, the Netherlands

Egerton University, Njoro, Kenya


March 2018

Updated version, April 2018

Source: Internet

Assessing the temporal mixing and stratification in

Lake Kivu

Master of Science Thesis

by

Tuyisenge Janvière

Supervisor Prof. Ken Irvine, IHE-Delft

Mentors Dr. Anne van Dam, IHE-Delft

Prof. Sally MacIntyre, University of California Santa Barbara, USA

Dr. Gretchen Gettel, IHE-Delft

Examination committee

Prof. Ken Irvine, IHE-Delft

Dr. Anne van Dam, IHE-Delt

Dr. Alicia Cortes, University of California Santa Barbara, USA

This research is done for the partial fulfilment of requirements for the Master of Science degree at the


Delft

March 2018

Although the author and UNESCO-IHE Institute for Water Education have made every effort

to ensure that the information in this thesis was correct at press time, the author and UNESCO-

IHE do not assume and hereby disclaim any liability to any party for any loss, damage, or

disruption caused by errors or omissions, whether such errors or omissions result from

negligence, accident, or any other cause.

© Tuyisenge Janvière 2018.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

http://creativecommons.org/licenses/by-nc/4.0/

http://creativecommons.org/licenses/by-nc/4.0/

i

Abstract

The thermal structure and vertical mixing of lakes change with surface meteorology and control

the vertical distribution of particulate and dissolved substances and organisms in water bodies.

This study assessed the effects of changes in the surface meteorology on vertical mixing in the

deep meromictic Lake Kivu, Africa (485 m) with a major chemocline between 255 and 262 m

and a mixolimnion that undergoes seasonal mixing. Vertical mixing and stratification were

described over diel and seasonal scales in the upper 100 m layer of the lake and the indices of

the water column stability were estimated. Trends in the diel and seasonal variability were

examined and described for meteorological variables: air temperature, wind speed, wind

direction, relative humidity, rain and shortwave and longwave radiation. Conductivity-

Temperature-Depth (CTD) profiles of temperature, conductivity, dissolved oxygen,

chlorophyll fluorescence and pH were used to describe the vertical structure of the water

column and to analyse the time series. Mixing and water stability indices were estimated from

temperature and density data. A seasonal signal in the meteorology observed in the dry season

was characterized by a decrease of the relative humidity to ~ 70 % and that of longwave

radiation to ~ 370 W/m2 and a slight increase of the southeasterly winds to ~ 3.5 m/s that occurs

after the rainy season. Variability in air temperature and shortwave radiation was very limited.

The vertical structure of the mixed layer was nearly isothermal in the morning conditions,

warmed by a maximum of 1oC as a result of the sun heat accumulation and stratified late in the

afternoons. Deep seasonal mixing occurred in the dry season and reached a depth of ~55 m

when the water column was cool and nearly homogeneous with 23.4oC. The lake water started

to stratify towards the end of August. The Chlorophyll fluorescence and the dissolved oxygen

remained enclosed in shallow layers during stratification and deepened with the mixing. The

conductivity was steady above the thermocline. The pH varied significantly at 60 m, chi sq =

34.409, d.f. = 3, p < 0.001 for 2015-2017 as opposed to chi sq. = 2.164, df = 3, p = 0.5391 for

2012. In 2016, the mixolimnion remained warm with less mixing. In 2017, because the seasonal

peak in the meteorology started as early as April, the epilimnetic temperatures were cooler and

the seasonal mixing was deeper and longer than in 2016. The hypolimnetic temperatures,

measured between 70 and 100 m, showed a warming trend of 0.016 oC/year between November

2015 and August 2017. High buoyancy frequency (18 cph) and Schmidt stability (11 kJ/m2)

were observed during the stratification period. The Wedderburn number followed the same

pattern as the thermocline depths and ranged from 1 to 22. The seasonal mixing lasted for three

to four months (May-August period) with the duration and the intensity of the mixing varying

from year to year. The mixolimnion of Lake Kivu is weakly stratified during the dry-mixing

season when the mixed layer dropped to 55 m and moderately stratified during the wet-

stratification period when the mixed layer is limited between the upper 17 to 30 m.

Keywords: vertical mixing, mixed layer, stratification, diel variation, seasonal variation, water

column stability, meteorological forcing.

ii

iii

Acknowledgements

I would like to express my sincere gratitude to Anne van Dam for a continuous support,

constructive advice and guidance provided during my study period at IHE-Delft, especially

during the thesis period. I am very grateful to Gretchen Gettel, Sally MacIntyre for their time,

invaluable comments and scientific contributions for the completion of this thesis. My deep

appreciation goes to Ken Irvine, my supervisor, for the guidance and advice provided during

the thesis.

LKMP is acknowledged for providing the data used in this study and Umutoni Augusta

is warmly thanked for helpful discussions on this study. Appreciation is extended to LKMP

staff to participate in regular collection of the CTD data. Special thanks to Wim Thiery for

providing the meteorological data. I am very thankful to Natacha Pasche for her cooperation

and constructive remarks.

I extend my special gratitude to ADC, through IPGL for funding my MSc studies. LWM

programme coordinators together with lecturers from respective institutions (BOKU, Egerton

University and IHE-Delft) are acknowledged for the courses they taught. Without them, I could

not have done much.

To everyone who contributed to the completion of this work, I greatly appreciated your

valuable motivation. Sincere gratitude is expressed to my mother Mukabagamba Pétronille, my

husband Harerimana Pierre Chrysologue, my son Hirwa Rebero Alain and my daughters Gwiza

Olga Bernice and Sangwa Nickita Benicia for their moral support and encouragement.

iv

v

Table of Contents

Abstract i

Acknowledgements iii

List of Figures vii

List of Tables xi

Abbreviations xiii

List of Symbols xv

Introduction 1

Literature review 4 2.1. Mixing dynamics in lakes 4

2.1.1. Concept and importance of mixing 4

2.1.2. Water density and thermal stratification 4 2.1.3. Meromixis in lakes 5

2.2. Mixing and stratification in Lake Kivu 5

2.2.1. Vertical profile of the water column 5 2.2.2. Homogeneity in Lake Kivu 6

2.3. Carbon dioxide and methane gases in lakes 6 2.4. Methane extraction in Lake Kivu 7

Material and methods 9 3.1. Description of Lake Kivu 9

3.1.1. Location, water budget and morphometry 9 3.1.2. Limnological characteristics 11

3.2. CTD data sources 11 3.3. Determination of conductivity, density and oxygen saturation 13 3.4. Meteorological measurements 14

3.5. Determination of indices of the lake 14 3.5.1. Estimation of the depth-area curve 14 3.5.2. Lake physical indices 14

3.6. Temporal variations in the water column 16

3.7. Data analysis 16

Results 19 4.1. Lake Kivu surface weather 19

4.1.1. Diel meteorological variability 19 4.1.2. Seasonal variation in the meteorology 24

4.2. Diel thermal profile of the water column 27

vi

4.3. Seasonal stratification 31

4.3.1. Vertical profile of the water column 31 4.3.2. Time series of measured parameters 40

4.4. Variability in the thermal structure 2016-2017 42 4.5. Physical indices of mixing and stability 44

4.5.1. Thermocline depths 44

4.5.2. Epilimnetic and hypolimnetic temperatures 45 4.5.3. Schmidt stability and Wedderburn number 46 4.5.4. Buoyancy frequency 47 4.5.5. Correlation analysis of physical indices 49

Discussion 50 5.1. Meteorological forcing on lake water mixing 50

5.2. Diel thermal structure 51 5.3. Seasonal variability in limnological variables 51

5.3.1. Thermal structure 51

5.3.2. Dissolved oxygen and chlorophyll fluorescence 52 5.3.3. Conductivity profile 53 5.3.4. pH variability 53

5.4. Indices of mixing and stability 54

Conclusions and recommendations 56 6.1. Conclusions 56

6.2. Recommendations 57

References 58

Appendix 63 Tables 63 Figures 65 Equations 67

vii

List of Figures

Figure 2-1: Vertical profiles measured in February 2004 for temperature, salinity, CO2 and CH4

concentrations measured in Lake Kivu. The profiles show horizontal gradients in the water column

with a major chemocline at about 250 m below which the gas concentrations increase. Source:

(Schmid et al., 2005). ....................................................................................................................... 8 Figure 3-1: Map of Lake Kivu illustrating (A) the five basins, the location of the meteorological station

and the distribution of 382 profiles grouped into 7 (A,B,C,D,E,F,G) based on the location. (B)

Distribution of 282 selected profiles measured in 2012 and from November 2015 to August 2017.

....................................................................................................................................................... 10 Figure 3-2: A multiparameter instrument, Sea & Sun CTD90M/725, used to obtain vertical profiles of

Lake Kivu water column (Source: LKMP, 2017). ......................................................................... 12 Figure 3-3: Profile141 measured in Lake Kivu in October 2016, showing how 382 profiles were checked

individually for (A) different parameters and (B) density profile with Dens (T): density for

temperature contribution, Dens (TSCC): density for temperature, salinity, CH4 and CO2

contribution and Dens (TS): density for temperature and salinity contribution. Note a difference in

y-axis increments. .......................................................................................................................... 17 Figure 3-4: A summary of the workflow for CTD and surface meteorological data processing and

analysis. ......................................................................................................................................... 18 Figure 4-1: Thirty minutes meteorology data for (a) air temperature, (b) wind speed, (c) wind direction,

(d) relative humidity, (e) shortwave radiation and (f) longwave radiation at the surface of Lake Kivu

in 2015. Selected days correspond to the days on which CTD data were collected in the year 2015.

....................................................................................................................................................... 20 Figure 4-2: Thirty minutes meteorology data for (a) air temperature, (b) wind speed, (c) wind direction,



....................................................................................................................................................... 22 Figure 4-3: Thirty minutes meteorology data for (a) air temperature, (b) wind speed, (c) wind direction,



....................................................................................................................................................... 23 Figure 4-4: A time series of weather variables at the surface of Lake Kivu for (a) wind speed, (b) air

temperature, (c) relative humidity, (d) shortwave radiation and (e) longwave radiation (January

2015 to August 2017). The grey lines show daily averages, the blue lines indicate the moving

averages over 30 days. ................................................................................................................... 24 Figure 4-5: A time series of daily averages at the surface of Lake Kivu for (a) wind speed, (b) air

temperature, (c) relative humidity, (d) shortwave radiation and (e) longwave radiation (October

2012 to December 2014). The grey lines show daily averages, the blue lines indicate the moving

averages over 30 days. ................................................................................................................... 26 Figure 4-6: Diel variations observed in temperature profiles measured in the upper 10 m of Lake Kivu

in seven months between November 2015 and August 2017. The water temperature increases with

time near the surface. ..................................................................................................................... 29 Figure 4-7: Diurnal variability in temperature profiles measured on Lake Kivu in 2017, on (A)

10 January (1) at 0855, (2) at 1632 and on 11 January (3) at 0908, (4) at 1800. (B) on 29 March.

(C) on 01 August 2017. (D) on 31July (1) at 17:09, 01 August (2 and 3) at 08:05 and 17:35,

respectively and on 02 August (4) at 09:25. Note the difference in the axis increments............... 30 Figure 4-8: Vertical temperature profiles measured on Lake Kivu in November and December 2015.

The profiles show thermal gradients an indication of stratification in the water column. ............. 31

viii

Figure 4-9: Vertical and temporal thermal structure in Lake Kivu for 87 profiles measured in 2016. The

lake water mixed layer deepens progressively from January to August then the straticafion starts in

October. ......................................................................................................................................... 32 Figure 4-10: A comparison of temperature profiles measured in 2016. (A) on 14 and 28 January, the

vertical profiles show a noticeable thermal gradient with a difference in temperature and depth of

the mixed layer. (B) on 17 March, 07 May and 23 June, the vertical profiles show a change in

temperature ranges and depths of the mixed layer. ........................................................................ 33 Figure 4-11: Vertical thermal structure in Lake Kivu for 126 profiles measured form January to August

2017. The mixed layer deepens progressively from January to August. In the months of February

and April, no data were collected. ................................................................................................. 34 Figure 4-12: Vertical conductivity corrected for temperature at 25oC for 242 profiles measured in Lake

Kivu on several months from November 2015 to August 2017. The EC remains constant in the

mixed layer and increases below the chemocline. ......................................................................... 35 Figure 4-13: pH profiles in Lake Kivu for 242 measurements from November 2015 to August 2017.

The pH decreases at a depth between 25 to 75 m. ......................................................................... 36 Figure 4-14: pH of Lake Kivu water column for 40 profiles measured in 2012. The pH did not show

fluctuations. ................................................................................................................................... 37 Figure 4-15: Vertical profiles of dissolved oxygen distribution in Lake Kivu water column. A total of

193 oxygen were measure between March 2016 and August 2017. The lake water is permanently

anoxic below ~ 55 m depth. ........................................................................................................... 38 Figure 4-16: Vertical profiles of chlorophyll fluorescence for 193 profiles measured in Lak Kivu

between March 2016 to August 2017. ........................................................................................... 39 Figure 4-17: Time series of (A) temperature (oC), (B) electrical conductivity (μS/cm) and (C) pH for all

profiles measured from Nov-2015 to Aug-2017. Month labels omitted on the horizontal axis

indicate months in which no profiles were measured. Contour intervals are 0.5 oC, 100 μS/cm and

0.5 for the pH. For temperature, data are from 10 m to remove the effects of the diel variations. 41 Figure 4-18: Time series of (A) chlorophyll fluorescence (μg/L) and (B) dissolved oxygen (mg/L)

measured in Lake Kivu from March 2016 to August 2017. Month labels omitted on the horizontal

axis indicate months in which no profiles were measured. Contour intervals for are 0.5 μg/L for

Chl a and 0.5 mg/L for DO. ........................................................................................................... 42 Figure 4-19: Comparison of thermal structure in 2016 (bold line) and 2017 (dash line) in Lake Kivu.

The mixed layer was warmer and stronger gradients formed during the stratification period in 2016

than in 2017. .................................................................................................................................. 43 Figure 4-20: Thermocline depths calculated from 242 CTD temperature profiles measured in Lake Kivu

between November 2015 to August 2017. From May, the thermocline depth moves downward and

reaches its maximum depth in August. The rest of the year the thermocline depths are shallow. 44 Figure 4-21: A time series for (A) Hypolimnetic temperature estimated for the layers between 70 and

100 m in 172 profiles. (B) Epilimnetic temperatures estimated from 186 profiles. Profiles were

measured between November 2015 and August 2017. .................................................................. 46 Figure 4-22: Timeseries for (A) Schmidt stability and (B) Wedderburn number calculated for 172

profiles measured in Lake Kivu between November 2015 to August 2017. The Schmidt stability

decreases with the deepening on of the mixed layer...................................................................... 47 Figure 4-23: Buoyancy frequency profiles calculated for temperature and salinity from CTD data

measured in Lake Kivu (November 2015 to August 2017). The stability increases during the

statification period (referring to the peaks at the thermocline depth). ........................................... 48 Figure 4-24: Pearson correlation analysis among calculated indices: thermocline depths (TD), Schmidt

Stability (SS) and epilimnetic temperatures (Epi). Indices show a correlation. ............................ 49 Figure 5-1: Wind speed variation at different time of the day. From 1000 to 1400 hrs (blue line and

moving averages in solid black line) and the rest of the day (red line and moving averages in dash

lines). Moving averages are calculated over 30 days. ................................................................... 51

ix

Figure 5-2: Variance of the pH among profiles measured in Lake Kivu at 20, 40, 60 and 80 m in 2012

and 2015-2017. Boxes with the same letter were not significantly different (Kruskal Wallis anova,

chi sq. = 2.164, df = 3, p = 0.5391 for 2012; and chi sq = 34.409, d.f. = 3, p < 0.001 for 2015-2017.

....................................................................................................................................................... 53 Figure 6-1: Coefficient of variation (CV) calculated for temperature profiles measured in Lake Kivu in

2012, and from November 2015 to August 2017. On one month, profiles were measured from

different location of the lake but they do not show differences. .................................................... 65 Figure 6-2: Vertical profiles of oxygen saturation measured in Lake Kivu from March 2016 to August

2017. .............................................................................................................................................. 66

x

xi

List of Tables

Table 3-1: Comparison of the Lake Kivu morphometric data with other AGL ...................................... 9 Table 3-2: Description of the two CTD probes used for vertical profiles measurements. .................... 11 Table 4-1: A comparison of 2016 and 2017 for the mean air temperature (AirTC), relative humidity

(RH), wind speed (WS), wind direction (WindDir), shortwave radiation (SW), Longwave radiation

(LW) and the total rain observed in the dry season. ..................................................................... 25 Table 4-2: Diel mean temperature and coefficient of variation (CV) in the upper 10 m layers of Lake

Kivu for profiles measured in 2015. The CV is higher in the afternoon profiles than in the morning

profiles. .......................................................................................................................................... 27 Table 4-3: Diel mean temperature and coefficient of variation (CV) in the upper 10 m layers of Lake


profiles. .......................................................................................................................................... 28 Table 4-4: Diel mean temperature and coefficient of variation (CV) in the upper 10 m layers of Lake


profiles. .......................................................................................................................................... 28 Table 6-1: Depth-area graph estimated for Lake Kivu using rLakeAnalyzer ....................................... 63 Table 6-2: Details on sampling frequency and sampling time of the CTD measurements ................... 64

xii

xiii

Abbreviations

ADC: Austrian Development Cooperation

AGL: African Great Lakes

AirTC: air temperature

BOKU: University of Natural Resources and Life Sciences

IHE: International Institute for Hydraulic and Environmental Engineering

IPGL: International Training Programmes in Limnology

LKMP: Lake Kivu Monitoring Programme

LW: longwave radiation

RH: relative humidity

SW: shortwave radiation

WindDir: wind direction

WS: wind speed

xiv

xv

List of Symbols

CH4: methane gas

CO2: carbon dioxide

cph: cycle per hour

H2S: hydrogen sulfide

N2: buoyancy frequency

pH: potential in hydrogen

PSU: practical salinity unit

rad: radian

W: Wedderburn number

SS: Schmidt stability

xvi

Introduction 1

CHAPTER 1

Introduction

Lakes, enclosed water bodies surrounded by land, are complex and diverse systems. They share

their properties with the surrounding systems and vary in size and shape (Imboden & Wüest,

1995; Gierlowski-Kordesch, 2004). During the 16th and 18th centuries, the African Great Lakes

(AGL): Lakes Malawi, Tanganyika, Victoria, Albert, Edward and Turkana, were explored.

Thereafter, Lake Kivu was the last to be discovered by von Götzen in 1894 (Meybeck, 1995).

The AGL are known to be a habitat for a wide variety of organisms and are essential natural

resources for human benefits. They are used for transportation, water supply, energy generation,

fisheries and recreation (Odada & Olago, 2006).

Lake Kivu, the third deepest lake (485 m) in Africa after Lake Tanganyika and Lake

Malawi, covers a surface area of 2370 m. It shows unique characteristics (Hutchinson, 1957)

due to its volcanic origin, its location at high altitude, specific morphology, chemical

composition and subaquatic groundwater discharge entering the lake at 250 m depth (Haberyan

& Hecky, 1987; Ross et al., 2015). Compared to other AGL, the meromictic deep Lake Kivu is

a fish-poor system with only 29 species while other lakes like Tanganyika and Malawi

accommodate hundreds of endemic species. The total fish stock in this lake is estimated to be

between 5000 and 6000 tons. The Limnothrissa miodon, a Tanganyika sardine, introduced in

Lake Kivu in 1959 has now become the most dominant in commercial catches around the lake

(Guillard et al., 2012). The lake offers a great opportunity for cage culture with a carrying

capacity estimated at 143,030 tons (Mbabazi, 2014).

The groundwater flow allowed the formation of a permanent strong density gradient

below which a large amount of carbon dioxide (CO2) equivalent to 300 km3 and methane (CH4)

equivalent to 60 km3 have accumulated (Tietze et al., 1980; Wüest et al., 2012; Ross et al.,

2015a ). The accumulation of CH4 in Lake Kivu is a benefit as it is seen as a source of electricity

to the region (Descy et al., 2012; Wüest et al., 2012). The CH4 extraction for electricity

production would generate 10 to 40 billion dollars (Jones, 2003). At the same time, the gas is

seen as a hazard as it bears a potential limnic eruption (Wüest et al., 2012). Similar hazardous

events happened in two Cameroonian lakes: Lake Monoun in 1984 and Lake Nyos in 1986,

when an explosion of the gas that had accumulated in their deep layers, killed more than 1700

people (Schmid et al., 2004b; Kling et al., 2006; Hirslund et al., 2012). A catastrophic gas

eruption from Lake Kivu could become a threat to around two million people in the surrounding

area (Jones, 2003; Boyle et al., 2009; Schmid et al., 2010).

To mitigate the risk of gas eruption, the Republic of Rwanda and the Democratic

Republic of Congo, two countries bordering Lake Kivu decided to start CH4 gas extraction. The

gas exploitation would not only avert the risk of gas outburst but also contribute to economic

development and deliver electricity to the population (Descy et al., 2012). For the benefit of the

society and the conservation of Lake Kivu ecosystem, a group of experts prepared a scientific-

technical guidance and recommended rules for a safe and environmentally sound gas

extraction(Boyle et al., 2009; Hirslund, 2012).

Introduction 2

Monitoring and management of Lake Kivu pose challenges as the gas in its deep layers

has to be removed but at the same time the lake structure and other human benefits have to be

preserved (Descy et al., 2012; Wüest et al., 2012). During gas harvest, the lake water is pumped

up from deep layers (260 to 460 m) rich in gases (Wüest et al., 2009). The CH4 is extracted and

collected, whereas the water is pumped back into the lake. This water is known as re-injected

water and is undesirable in the upper layer of the lake as it could induce eutrophication but also

contains a large amount of CO2 and H2S and traces of CH4. At the same time, the re-injected

water is undesired in the deep layers of the lake as it could dilute the gas resource (Descy et al.,

2012; Wüest et al., 2012). A potential concern is that the reinjected water may weaken the

stratified layers and induce mixing which would result in gas release from deep water. Wüest

et al. (2009) said that although it is not possible to predict the impact in details, effects like

occasional fish-kills, toxic algal bloom, change in species composition and degradation of the

already poor fish biodiversity might be expected.

The depth of the mixed layer, in meromictic lakes, is a characteristic of key importance

(Davies‐Colley, 1988). For example, the thermal profile of a water body has important effects

on circulation and long-term changes in the water column temperature might induce mixing

regime shifting and changes in thermocline depth that trigger a change in the vertical

distribution of dissolved particles and gas(Adrian et al., 2009). Although all lakes respond to

changes in environmental forces such as wind, cooling events and shortwaves radiations,

responses are lake-specific. Therefore, an understanding of the temporal evolution of mixing

and stratification, two important processes that drive the magnitude of vertical exchange in a

water body and control the water column characteristics, are needed for Lake Kivu

More particularly, it is crucial to consider the exceptional limnological and geological

features and ensure a sustainable management of the lake resource and structure during gas

harvesting from Lake Kivu (Descy et al., 2012). Hence, it is important to characterise the

physical stratification, the meteorological forcing over the lake and to check stability the water

column in the upper layers of the Lake Kivu as any changes in mixing and stability would lead

to a different mixing regime affecting vertical nutrient and gas fluxes through the water column.

Kling et al. (2006) and Wüest et al. (2009) recommended a regular monitoring of the physical,

chemical and biological conditions of the lake and the evolution of the stratification in the lake.

To act as suggested above, the Ministry of Infrastructure in Rwanda created a monitoring unit,

Lake Kivu Monitoring Programme (LKMP), that started monitoring activities with the first

pilot gas extraction plant in 2008. The monitoring unit collects vertical profiles of temperature,

dissolved oxygen, chlorophyll fluorescence, conductivity, and pH over the Lake Kivu water

column in order to check the lake stratification. However, without a scientific analysis of this

data, the monitoring would not be effective. This study analysed the vertical profiles data

gathered by LKMP in 2012 and on several months between November 2015 and August 2017.

Meteorological data at the surface of Lake Kivu were analysed to explain the mixing cycles in

the water column.

Introduction 3

This work aims to characterise the temporal vertical mixing and stratification dynamics

and to estimate the water column stability in the upper 100 m layer of Lake Kivu.

The specific objectives of this work are:

1. To illustrate the variability of the surface meteorology and gain insight of the

meteorological forcing on the water column

2. To describe a diel thermal pattern in the water column

3. To assess the seasonal vertical mixing in Lake Kivu

4. To quantify the lake physical indices and estimate the water column stability in relation

to meteorological forcing.

It was hypothesised that:

1. The meteorological conditions at the surface of Lake Kivu show a temporal variation

and drive the vertical mixing processes.

2. The response of the mixolimnion to the changes in the weather conditions lead to diel

and seasonal patterns in downwelling and upwelling of the mixed layer.

3. Physical characteristics of the water column stability are derivatives of measured water

parameters, they therefore, show temporal changes.

Literature review 4

CHAPTER 2

Literature review

2.1. Mixing dynamics in lakes 2.1.1. Concept and importance of mixing

Mixing within lakes deserves particular attention as it controls the distribution of oxygen and

nutrients that drive the distribution and abundance of the biota (Bootsma & Hecky, 1993; Spigel

& Coulter, 1996). It has been demonstrated that mixing processes in lakes vary both in temporal

and spatial resolution (Imboden & Wüest, 1995) and that the meteorological control over the

mixing and water temperatures in a lake varies between climate zones (Thiery et al., 2014b).

Based on mixing patterns, lakes have been classified as holomictic, where a complete or partial

mixing occurs at least once a year due to homogeneous temperature conditions (Hakala, 2004)

or meromictic when lake water layers do not mix (Lewis Jr, 1983).

Mixing dynamics in lakes are influenced by factors such as climate, lake morphology

and the nature of inflowing water (Gierlowski-Kordesch, 2004). In tropical climates, the

seasonal cycle of air temperature and radiation are other factors that control mixing regime

(Thiery et al., 2014b). For example, the two meromictic lakes Malawi and Tanganyika have

well-defined seasonal mixing patterns with the seasonal thermocline becoming weaker and

deeper due to the evaporative cooling and wind mixing during the dry windy season (Spigel &

Coulter, 1996).

2.1.2. Water density and thermal stratification

For some periods of time, lakes can develop different properties in their water masses (Boehrer

& Schultze, 2008) leading to the formation of layers of different densities. This creates barriers

to water mixing and results in a situation known as stratification. According to Kling et al.

(2006), the water density is determined by (1) temperature: warmer and light-water float on

cold and denser water, (2) dissolved salts: the density of water increases with increase in

dissolved salts, (3) concentration of dissolved gas: CO2 and H2S increase the density while CH4

lower the water density and (4) depth and pressure: the deeper the lake, the greater the pressure

and the higher the water density. Imboden and Wüest (1995) explained three factors that control

the development of stratification in lakes. First, lakes are relatively standing water bodies,

increased heating in some seasons allows the establishment of a stable gradient in temperature

and dissolved substances. Second, lakes have a long residence time. As a result, cooling, heating

and chemical processes are slow. The stratification leads to a limited interaction between the

surface and the bottom waters.

Heating and cooling events in the near surface water modify the water density which

leads to a thermal stratification. The surface water divides into an upper layer, the epilimnion,

characterised by turbulence which allows the formation of a relatively homogeneous

temperature layer and a lower layer, hypolimnion, characterised by cooler water. Between the

Literature review 5

two regions exists a layer termed the thermocline, which is characterised by a rapid decrease

in temperature (Hutchinson, 1957; Imberger, 1985). The thermal stratification may be persistent

or temporal, changing on a short time scale (hours) to a long time scale (decades) as a response

to external mixing drivers such as wind and cooling. Imberger (1985) defined three different

characteristics of the thermal structure. These include a seasonal thermocline resulting from a

deep seasonal mixing, multiple thermoclines arising as a sum of events on the previous days

and the diurnal thermoclines that result from heating events in a day (Lewis, 1973).

Temporal variations in surface meteorology and stratification and their influence on

vertical mixing have been studied in lakes (Read et al., 2011; MacIntyre et al., 2014; Thiery et

al., 2014a). The magnitude of vertical mixing and stratification and the stability of the water

column are characterised by physical indices such as the Schmidt stability: the amount of work

needed to mix a water column, the buoyancy frequency: the local stability of the density

gradient, the Wedderburn number (the likelihood of the thermocline tilting as a result of wind

stress), the thermocline depths and the epilimnion and hypolimnion temperatures (Boyce, 1974;

MacIntyre et al., 2014)

2.1.3. Meromixis in lakes

In 1935, an Austrian limnologist, Ingo Findeneg, introduced the term meromixis to define a

condition in which the lake water does not mix, interaction and circulation being limited within

restricted layers (Hakala, 2004; Stewart et al., 2010). This condition is observed in a

considerable number of deep lakes such as the Caspian Sea, Lake Baikal and Lake Tanganyika

(Boehrer & Schultze, 2008) in which some water layers remain separated at any time of the

year. Such lakes are permanently stratified and are termed meromictic lakes (Boehrer &

Schultze, 2008; Hutchinson, 1957). In those lakes, the upper layer is called the mixolimnion.

The deep layer known as monimolimnion is denser and does not participate in mixing. The two

layers are separated by a physical barrier of steep density gradient or pycnocline (Gibson, 1999),

a zone of rapid changes in temperature and or salinity with depth. Based on the origin of the

permanent stratification, meromixis can be classified as (1) ectogenic meromixis when caused

by input of salt from an outside source, (2) crenogenic meromixis caused by groundwater

inflows or (3) biogenic meromixis when decomposition of organic matter leads to an elevated

salt concentration (Boehrer & Schultze, 2008; Hakala, 2004; Hutchinson, 1957).

2.2. Mixing and stratification in Lake Kivu 2.2.1. Vertical profile of the water column

The vertical mixing dynamics in Lake Kivu are similar in the mixolimnion when compared

with other large lakes in the world but differ with respect to some of the processes deeper in the

water column (Schmid & Wüest, 2012). In Lake Kivu, the seasonal mixing affects the upper 60

to 65 m depth, with nearly homogenous conductivity and changes in water temperatures. The

mixolimnion of Lake Kivu has also been called the biozone, as it is the only layer that supports

biological activity (except for anaerobic microbial activity which can also occur in the deeper

layers). Below 65 m depth, there are no seasonal changes. The water column is permanently

stratified and anoxic (Schmid & Wüest, 2012; Ross, et al. 2015a). The anoxic monimolimnion

is divided into two layers by a pycnocline between 255 and 262 m depth. This strong gradient

is maintained by subaquatic groundwater discharged at the top of the gradient (Ross et al.,

Literature review 6

2015a). Double diffusive staircases are sometimes found above and below these steps

(Newman, 1976), these features set Lake Kivu apart from the other meromictic East African

Great Lakes. The upper monimolimnion is also called upwelling zone as it is highly influenced

by the subaquatic flow while the lower monimolimnion is called deep zone or resource zone as

it contains an exploitable reservoir of CH4 (Schmid & Wüest, 2012). The last turnover in Lake

Kivu is assumed to have occurred within the last 750-100 years (Schmid et al., 2005; Ross et

al., 2015b).

In water bodies, the temperature typically decreases with depth (Kling et al., 2006).

However, in Lake Kivu the water temperature increases with depth stepwise below the

mixolimnion (Fig. 2-1). For example, the temperature increases from 23oC at the lower

mixolimnion to 26oC at the maximum depth of the water column. The higher temperatures of

deep water and large amount of CH4 have a negative contribution to the vertical density gradient

(Wüest et al., 2009; Schmid & Wüest, 2012). Strong stability of the deep layers is therefore

sustained by salt and CO2 (Schmid et al., 2005).

2.2.2. Homogeneity in Lake Kivu

Temperature and conductivity have been reported to be horizontally homogeneous in the main

basin of Lake Kivu (Schmid & Wüest, 2012; Thiery et al., 2014). However, variations can be

observed at some depths near the northern shore as a result of the water flow from Kabuno Bay

or the subaquatic flow. For example, profiles measured near the northern basin showed a clear

negative peak in temperature and a weak negative peak in conductivity at 250 m depth (Schmid

& Wüest, 2012). Profiles in the basins are similar with two remarkable exceptions observed in

Kabuno bay, where the lake is strongly stratified below 11 m depth, with high conductivity,

CO2, alkalinity and pH (Tessi et al., 2009; Schmid & Wüest, 2012). And in Bukavu bay where

the water column does not show a substantial increase in conductivity below 60 m. This infers

that Bukavu bay is not meromictic and the water column completely mixes during the dry

seasons (Sarmento et al., 2006). The horizontal homogeneity in Lake Kivu can only be affected

by seasonal dynamics in the mixolimnion, supply of the groundwater and CH4 extractions

activities (Schmid & Wüest, 2012; Thiery et al., 2014).

2.3. Carbon dioxide and methane gases in lakes

CH4 and CO2 are known to be the main end products of organic matter decomposition in water

bodies (Pasche et al., 2011). The CH4 accumulation occurs in permanently stratified and anoxic

water bodies (Wüest et al., 2012) and different lacustrine systems are known to be rich in CH4.

For example, the CH4 concentration was found to reach 5 mol/m3 in Lake Ace (Franzmann et

al., 1991) and to approach a maximum of 21.8 mol/m3 in Lake Untersee (Wand et al., 2006).

Moreover, CO2 has been reported to accumulate in the bottom water of lakes such as Lakes

Nyos and Monoun in Cameroon (Sigurdsson et al., 1987; Tuttle et al., 1987; Schmid et al.,

2004b). It has been estimated that 60 km3 of CH4 and 300 km3 of CO2 (gas volumes at 0oC and

1 atm) have accumulated in the hypolimnion of Lake Kivu (Schmid et al., 2005). The CH4 gas

in Lake Kivu increases with depth. For example, the layer between 200 and 260 m deep contains

5.4 mol/m3 while the layer below 260 m depth reaches 17 mol/m3 (Wüest et al., 2012). The CO2

concentration is about five times that of CH4. However due to its higher solubility in water, the

contribution of CO2 to the total gas pressure is much less, it is only ¼ of the contribution of

Literature review 7

CH4 (Schmid et al., 2004a). Such gas accumulation was made possible because the deep water

is separated from the lake surface waters by a strong density gradient between 255 and 262 m

depth (Schmid & Busbridge, 2010; Pasche et al., 2011).

In lacustrine systems, the most important CH4 production ways include methanogenesis

and CO2 reduction (Conrad, 2005) ( Eq. 2-1 and 2-2).

𝐶𝐻₃𝐶𝑂𝑂𝐻 → 𝐶𝑂₂ + 𝐶𝐻₄ ………………………………………………………………..(2-1)

𝐶𝑂₂ + 4𝐻₂ → 𝐶𝐻₄ + 2𝐻₂0 ………………………………………………………………(2-2)

2.4. Methane extraction in Lake Kivu

Following a catastrophe of gas explosion in two Cameroonian lakes Nyos in 1986 and Monoun

in 1984 (Sigurdsson et al., 1987; Tuttle et al., 1987), it was feared that a such limnic eruption

could occur in Lake Kivu. Having a large catchment (5097 km2) and being densely inhabited

(400 inhabitants/km2) is a fact that this would be among the largest natural hazard (Schmid &

Busbridge, 2010). The CH4 gas found in Lake Kivu could be a valuable source of energy and

would generate more than ten times the annual energy utilized in the Democratic Republic of

Congo and Rwanda, the two bordering countries (Jones, 2003).

The two countries decided to remove the CH4 gas from the lake, to promote local energy

supply and at the same time to reduce the eruption risk. A pilot plant started its activities in

2008 (3MW) while in 2015 gas extraction started at a larger scale (25 MW). Gas extraction

technology should avoid any practices that could weaken the current density gradients and

deteriorate lake layers (Boyle et al., 2009). As recommended by Boyle et al. (2009) and Wüest

et al. (2009), the strategy used in gas exploration would meet the following requirements: (i)

ensure the safety of the population by preserving the density stratification and reducing the risk

of gas eruption, (ii) conserve the integrity of the lake ecosystem by controlling the nutrient

loading into the surface layer and (iii) maximize the methane harvest by minimizing the

methane loss to the atmosphere and to the oxic surface water.

The current CH4 extraction principle consists of lifting deep CH4, CO2, H2S gases and

nutrient-rich water between 300 and 400 m. In a separator, the gas-water mixture gets separated

into gases and water. The degassed water is pumped back deep into the lake while gases are

cleaned with gas-free water pumped from upper layers near the surface. As they are highly

soluble, CO2 and H2S are removed by the cleaning process. The CH4 is sent to generators and

converted to electricity while the water used to clean the gas (rich in CO2 and H2S) is reinjected

back into the lake (Boyle et al., 2009; Schmid & Busbridge, 2010; Wüest et al., 2012). It is

expected that large-scale commercial CH4 extraction will develop in the next decade and will

possibly have important impacts on the density stratification in the lake (Descy et al., 2012).

Literature review 8

Figure 2-1: Vertical profiles measured in February 2004 for temperature, salinity, CO2 and CH4

concentrations measured in Lake Kivu. The profiles show horizontal gradients in the water column with

a major chemocline at about 250 m below which the gas concentrations increase. Source: (Schmid et

al., 2005).

Material and methods 9

CHAPTER 3

Material and methods 3.1. Description of Lake Kivu 3.1.1. Location, water budget and morphometry

Lake Kivu, a deep meromictic lake, lies between the Republic of Rwanda and the Democratic

Republic of Congo. It is located in a volcanically active East African rift valley system between

01o34’25’’-02o29’40’’S and 28o51’04’’-29o22’38’’E characterised by a tropical climate

(Thiery et al., 2014b). It is fed by precipitation contributing ∼ 3.3 km3/yr, a total of around 200

small rivers contribute ∼ 2.4 km3/yr and numerous subaquatic sources contributing up to

1.3 km3/yr. It has a single outflow, the River Rusizi that flows to Lake Tanganyika discharging

∼ 3.6 km3/yr while the evaporation losses are estimated to be ∼ 3.4 km3/yr (Muvundja et al.,

2009; Pasche, 2009). Lake Kivu consists of 5 basins: 1 main basin and 4 small basins (from

south to the north): Bukavu, Ishungu, Kalehe and Kabuno (Spigel and Coulter, 1996; Tassi et

al., 2009). This study is carried out in the main basin of Lake Kivu (Fig. 3-1A), which consists

of the deep zone of the lake and where CH4 gas extraction is carried out. Compared to other

AGL, Lake Kivu is small in surface area (2370 km2) and has an intermediate depth (485 m)

(Tab. 3-1).

Table 3-1: Comparison of the Lake Kivu morphometric data with other AGL

Lake Kivu Lake Tanganyika Lake Malawi Lake Victoria

Depth (m) 485 1 470 706 80

Area (km2) 2 370 32 900 29 600 68 800

Volume (km3) 560 18 900 8 400 2 750

Length (km) 89 673 580 337

Catchment (km2) 5 300 231 000 126 500 258 700

Elevation (m) 1463 773 468 1 135

Sources: (Eccles, 1974; Bergonzini, 1998; Naithani et al., 2003; Verburg & Hecky, 2003)


Figure 3-1: Map of Lake Kivu illustrating (A) the five basins, the location of the meteorological station

and the distribution of 382 profiles grouped into 7 (A,B,C,D,E,F,G) based on the location. (B)

Distribution of 282 selected profiles measured in 2012 and from November 2015 to August 2017.

A

B

C

D

E

F

G

A

B


3.1.2. Limnological characteristics

A large amount of CO2 and CH4 has accumulated in the deep waters of Lake Kivu. The CO2 is

confirmed to be of magmatic origin, while the CH4 gas is generated by the reduction of

magmatic carbon (¾) and by the mineralization of carbon (¼) (Tietze et al., 1980). The CH4

concentrations were assumed to be steady in Lake Kivu. However, recent measurements

indicate an increase of methane gas by around 15%. At that production rate, gas concentrations

can approach saturation within a century (Schmid et al., 2005). The H2S gas is absent in the

upper oxic layer with a sharp increase from 50 to 150 m below which it remains constant at

0.27 mmol/L (Pasche et al., 2011). Lake Kivu is an oligotrophic system in which internal

nutrient loading from deep layers is more important than external nutrient loading (Muvundja

et al., 2009). The phytoplankton is mainly composed of cyanobacteria, diatoms and

cryptophytes (Sarmento et al., 2006). The zooplankton is dominated by copepods and

cladoceran (Muvundja et al., 2009).

3.2. CTD data sources

The Conductivity-Temperature-Depth (CTD) data used in this study were provided by LKMP.

In total, 382 vertical profiles (Fig. 3-1A) were obtained from Lake Kivu during fieldwork

before methane gas extraction in 2012 and during methane extraction between November 2015

and August 2017. The CTD data were measured with either CTD60M/257 or CTD90M/725

probes (Sea & Sun Technology, Germany). The two multiparameter probes (Fig. 3-2) allow a

simultaneous profiling of electrical conductivity, temperature, pressure, pH, turbidity and

dissolved oxygen. The Chlorophyll fluorescence was measured with the CTD90M/725 only.

Technical details of the sensors are presented (Tab. 3-2). Salinity is not measured in-situ. LKMP

computes salinity (g/kg) from conductivity at 25 oC (κ₂₅) based on the method established by

Wüest et al. (1996) for Lake Malawi.

Table 3-2: Description of the two CTD probes used for vertical profiles measurements.

Sensor Range Accuracy Precision Response time (63%)

Pressure 0 – 6000 dbar ± 0.1 % 0.002% 150 ms

Temperature -2 – +36oC ± 0.002oC 0.001oC 150 ms

Conductivity 0 – 70 mS cm-1 ± 0.003 mS cm-1 0.001 mS cm-1 150 ms

DO 0 – 20 mg L-1 ± 2% 0.01% > 200 ms

pH 2 – 10 pH ± 0.02 pH 0.0002 pH 1 s

Turbidity 0 – 1000 FTU 0.1 NTU

Chlorophyll a 0 – 500 μg L-1

Source: LKMP, 2017


Figure 3-2: A multiparameter instrument, Sea & Sun CTD90M/725, used to obtain vertical profiles of

Lake Kivu water column (Source: LKMP, 2017).

The rate of lowering the CTD probes was estimated to be about 0.5 m/s resulting in

profiles consisting of approximately 14000 records for a deep profile of ~400 m. The CTD

vertical profiles were measured during the day, mainly on concentric transects (with reference

to the location of the gas extraction floating platform) from different locations of Lake Kivu, at

different time with maximum depths ranging from 60 to 450 m. Only the CTD downcast data

was analysed. All sampling points were geo-referenced using a GPSMAP 78S (GARMIN,

USA).


3.3. Determination of conductivity, density and oxygen saturation

Conductivity κ₂₅

The electrical conductivity (EC) was measured in-situ using the CTD probes at in-situ

temperatures. The water temperatures influence on the viscosity of fluids and this affect the

movement of ions. Therefore the conductivity, a parameter related to the concentration of ions

in water, is highly dependent on water temperatures. To allow a comparison among measured

conductivity profiles, they need to be corrected for temperature effects (Mäntynen, 2001). The

mostly used temperature standards are 18, 20 and 25oC and the conductivity is referred to as

κ₁₈, κ₂₀ and κ₂₅ respectively. All analysed CTD profiles were adjusted to 25oC considering the

effects of the temperature on the viscosity of fluids which affect the movement of ions. The

viscosity of water was computed after Sengers and Watson (1986) (see the appendix). The

conductivity κ₂₅ was computed following Sorensen and Glass (1987).

𝜅₂₅ = 𝜅𝑇 ∗ (𝑣𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦(𝑇)/𝑣𝑖𝑠𝑐𝑜𝑠𝑖𝑡𝑦(25))^0.866 …………………………… (3-1)

where κ₂₅ is the electrical conductivity at 25oC, κT is the EC measured in-situ, viscosity (T) is

the viscosity at in-situ measured temperature and viscosity (25) is the viscosity at 25oC.

Density

The density of water is highly influenced by the temperature and dissolved particulates. For

Lake Kivu, the density of water is calculated considering the effects of temperature, salinity,

CH4 and CO2 (Schmid et al., 2002; Schmid & Wüest, 2012; Thiery et al., 2014b). The

contribution of CO2 and CH4 to the density was checked using the gas concentration measured

in 2002 using the Eq. 3-2. Because of the absence to low concentrations of CO2 and CH4 in the

upper 100 m layers of the lake, and lack of difference between a temperature-salinity density

profile and a temperature-salinity-CO2-CH4 density profile, the effects of the two gases on the

density was not considered. The density was calculated considering the contribution of

temperature and salinity (Eq. 3-3) (Chen & Millero, 1977; Millero & Poisson, 1981; Schmid et

al., 2002; Schmid & Wüest, 2012).

𝜌(𝑇, 𝑆, 𝐶𝑂₂, 𝐶𝐻₄) = 𝜌(𝑇) ∗ (1 + 𝛽𝑠 ∗ 𝑆 + 𝛽CO₂ ∗ 𝐶𝑂₂ + 𝛽CH₄ ∗ 𝐶𝐻₄ ) …(3-2)

𝜌(𝑇, 𝑆) = 𝜌(𝑇) ∗ (1 + 𝛽 ∗ 𝑆 ) ………………………………………………………….. (3-3)

ρ (T) is given by the Eq. 3-4

𝜌(𝑇) = 0.999839 + 6.7914𝑥10‾5 ∗ 𝑇 − 9.0894𝑥10‾6 ∗ 𝑇2 + 1.0171𝑥10‾7 ∗ 𝑇3 − 1.2846𝑥10‾9 ∗

𝑇4 + 1.1592𝑥10−11∗ 𝑇5 − 5.0125𝑥10−14𝑇6 [𝑘𝑔 𝐿 − 1] ………………………………… (3-4)

where ρ is the density (kg/m3), T is the temperature (oC), S is the salinity (kg/g) and 𝛽 is the

haline contraction coefficient = 0.75*10-3 kg/g. This coefficient implies that by adding 0.75 g/L


of salt, the density of water increases by 0.75 g L-1 (Wüest et al., 1996). 𝛽CO₂ = 0.284*10-3 kg/g

is the coefficient of CO2 (Ohsumi et al., 1992) and 𝛽CH₄ = -1.25*10-3 kg/g is the coefficient of

CH4 (Lekvam & Bishnoi, 1997).

Oxygen saturation

Oxygen saturation was calculated from measured dissolved oxygen, temperature and salinity

referring to (Garcia & Gordon, 1992) following the procedure established by Winslow et al.

(2016).

3.4. Meteorological measurements

Local meteorological data were measured by a water-based weather station fixed on a floating

platform anchored on the main basin of Lake Kivu at 29o14’15’’ E and 1o43’30’’ S (Fig. 3-1).

This is approximately at 30 km distance to the north of the study site. Data was available from

October 2012 until August 2017. Variables included wind speed (m/s), wind direction (Degree),

relative humidity (%), air temperature (oC), precipitations (mm), shortwave radiation (W/m2)

and longwave radiation (W/m2). Data was recorded at a frequency of 30 minutes from which

diurnal, daily mean values and moving averages were calculated to characterise the weather

conditions at the surface of Lake Kivu.

3.5. Determination of indices of the lake

3.5.1. Estimation of the depth-area curve

A depth-area curve for Lake Kivu was estimated every 1 m depth for the whole water column

assuming that the shape of the lake is a cone, considering the surface area (2370 km2) and

maximum depth (485 m) of the lake based on the procedure established by Winslow et al.

(2017). A depth-area curve from 0 to 100 m was extracted (see the Appendix) and used to

calculate other indices.

3.5.2. Lake physical indices

Thermocline depths

Thermocline depths (m) were estimated for 242 CTD profiles measured between November

2015 and August 2017, according to the definition by Hutchinson (1957) using the procedure

established by Winslow et al. (2017).

𝟶 =𝑑2 𝜃

𝑑𝑧2 ………………………………………………………………………… (3-5)

where 𝜃 is the temperature and z is the depth.


Epilimnetic and hypolimnetic temperatures

The mean epilimnetic temperatures (oC) were calculated for 186 vertical profiles measured from

morning to 1500 hrs, using the water temperature time series and the depth-area graph. For the

lower layers of the lake (70 to 100 m), the estimation of mean temperatures (oC) was calculated

172 vertical profiles whose maximum depths reached 100 m following (Winslow et al., 2017).

Schmidt stability

The Schmidt stability (kJ/m2) was calculated from the surface 0 to 100 m with 1 m interval for

172 temperature and salinity profiles measured between November 2015 and August 2017

using the estimated depth-area curve (see Appendix) as formalised by Idso (1973).

𝑆 =𝑔

𝛢s∫ (𝑧 − 𝑧ν) 𝜌𝑧 𝐴𝑧

𝑧𝐷

0𝑑𝑧……………………………………………………..(3-6)

where g is the gravitational acceleration, As is the surface area of the lake, z is the depth, zD is

the maximum depth, ρz is the density at depth z, zv is the depth of the volumetric centre and Az

is the area of the lake at depth z.

Wedderburn number

The Wedderburn number, introduced by Thompson and Imberger (1980), was calculated for

172 CTD profiles using the Eq. 3-7 (Davies‐Colley, 1988; Read et al., 2011; MacIntyre et al.,

2014).

𝑊 =𝑔´𝑧2

(0.001∗𝑊𝑆)2𝐿 ……………………………………………………… (3-7)

where g´ = g*Δρ/ρh is the reduced gravitational acceleration due to change in the density across

the thermocline (Δρ), ρh is the density in hypolimnion, g is the gravitational acceleration, z is

the depth of the mixed layer, WS is the wind speed and L is the length of the lake. The

coefficient 0.001 is a simplification based on the assumption that shear stress is equal on both

sides of the air-water interface.

Brunt-Väisälä frequency

The local stability of the water column (rad2/s2) was estimated for 242 CTD profiles measured

between November 2015 and August 2017 as the Brunt-Väisälä frequency or buoyancy

frequency (N2) named after David Brunt and Vilho Väisälä (MacIntyre et al., 2002; Read et al.,

2011; Winslow et al., 2017).

𝑁2 =𝑔

𝜌 𝑑𝜌

𝑑𝑧 ………………………………………………………………………………(3-8)

where g is the gravity and ρ is the density.


The buoyancy frequency is commonly reported as 𝑁 = √(𝑔

𝜌 𝑑𝜌

𝑑𝑧) and expressed in rad/sec or

cph. However calculating the square root becomes a problem when N2 is negative (King et al.,

2012) as it happened in this study. Therefore for resulting vertical profiles, N2 (rad2/sec2) was

considered. To compare Lake Kivu stability with that observed from other lakes, N (cph) was

calculated for discussed positive values, considering that there are 2π radians per cycle and

3600 seconds in 1 hour using the Eq. 3-9.

𝑁 = 3600 ∗ √𝑁2/(2 ∗ 𝜋) ………………………………………………………(3-9)

3.6. Temporal variations in the water column

Individual CTD profiles were analysed. Diel thermal variations were studied in vertical profiles

plots, looking at the temperature variability near the surface based on the time of the day.

Analysed near surface temperatures were obtained by averaging from 0 to 10 m depth. Seasonal

variability was visualised using vertical profiles and time series contour plots of temperature,

electrical conductivity, pH, chlorophyll fluorescence and dissolved oxygen measured from

November 2015 to August 2017 as they seem to have a continuous data. Profiles measured in

2012 were used to describe the pH variations. Given that the upper 10 m undergoes marked diel

variations, the temperature time series were studied in the water layers below 10 m depth. The

years 2016 and 2017 were compared for vertical variability in temperature profiles.

3.7. Data analysis

The profiles were given an identity code from 001 to 382, checked one by one for consistency

and arranged to enable analysis. An example is given in Fig. 3.3 for Profile141. The spatial

distribution analysis allowed to group the profiles into 7 clusters based on their distribution

named A to G (see Fig. 3-1A). Of the 7 groups, one large group “A” made of 282 CTD casts

measured in a ∼ 2.5 km radius circle from the methane gas extraction was selected as the study

site (Fig. 3-1B). As the 282 profiles were measured from different locations, spatial variability

was checked by calculating the relative variability among profiles measured in the same month.

The coefficient of variation (CV), 𝐶𝑉 =𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛

𝑚𝑒𝑎𝑛∗ 100%, showed that the variability

was ≤ 2% (see Fig. 6-1 in appendix). The spatial variability was disregarded, profiles taken on

the same month were assumed not to differ.

For each of the 282 profiles, measured variables were averaged for 1 m intervals. The

top 100 m layer was selected as the seasonal vertical mixing is not expected below this layer

(Thiery et al., 2014b). Selected CTD data were measured as follows: 40 profiles in 2012

(without CTD measurements in January, February, May, October and November), 29 profiles

in 2015 (measured in November and December only), 87 profiles in 2016 (without CTD

measurements in April, September and December) and 126 profiles in 2017 (measured up to

August without CTD profiles in February and April). Tab. 6-2 shows details on analysed

profiles. See Fig. 3-4 for details on workflow data processing.

The pH variation was tested for specific depths: 20, 40, 60 and 80 m at which the pH

showed fluctuations. The variance of the pH among all profiles was calculated to test how

profiles differ within one month in both 2012 and 2015-2017 periods. A non-parametric Kruskal


Wallis ANOVA was used to test this difference. A relationship among computed indices was

evaluated for the thermocline depths, the Schmidt stability and the epilimnetic temperatures. A

correlation analysis after Pearson, at 95 % confidence interval, was used to determine the

variation among calculated indices (thermocline depths, Schmidt stability and epilimnetic

temperatures).

Spatial analysis and drawing of maps were done using QGIS 2.18.14 (QGIS

Development Team, 2017). CTD and meteorological data and statistical analysis were done

using Excel and R 3.3.2 (R Core Team, 2013). Lake Metabolizer, rLakeAnalyzer and Plotly,

specific R packages, were used to calculate oxygen saturation, lake physical indices and to draw

the contour plots, respectively (Winslow et al., 2016; Winslow et al., 2017).

Figure 3-3: Profile141 measured in Lake Kivu in October 2016, showing how 382 profiles were checked

individually for (A) different parameters and (B) density profile with Dens (T): density for temperature

contribution, Dens (TSCC): density for temperature, salinity, CH4 and CO2 contribution and Dens (TS):

density for temperature and salinity contribution. Note a difference in y-axis increments.

Source: LKMP, CTD profiles measured in Lake Kivu in October 2016 and gas measurements

done in 2002.


Figure 3-4: A summary of the workflow for CTD and surface meteorological data processing and analysis.

Results 19

CHAPTER 4

Results

4.1. Lake Kivu surface weather 4.1.1. Diel meteorological variability

Results of the weather data of 05, 13 and 20 November and 03 December 2015 are presented

in Fig. 4-1. Night temperatures were cool and ranged from18.7 oC to 22.3 oC observed at

midnight on 05th and 20th November, respectively. The air temperatures were almost similar at

0600 hrs with 19.9 ± 0.5 oC, then they increased with time of the day and reached a peak of

24.1 oC at 1600 hrs on 03 December. The wind speeds were higher in the afternoons than in the

mornings and reached 7.4 m/s at 1230 hrs on 05 November. Early morning winds were mainly

coming from the north and northeast until 800 hrs when the direction changed to northwest.

The relative humidity varied between 80 and 90 % during nighttime and decreased to between

70 and 80 % at 1000 hrs remained nearly constant and started to increase at 17 hrs. The solar

radiation increased during the day with a peak of 1070 W/m2 at 1230 hrs.

Results 20

Figure 4-1: Thirty minutes meteorology data for (a) air temperature, (b) wind speed, (c) wind direction,

(d) relative humidity, (e) shortwave radiation and (f) longwave radiation at the surface of Lake Kivu in

2015. Selected days correspond to the days on which CTD data were collected in the year 2015.

Results 21

Weather data from 10 days in 2016 are presented in Fig. 4-2. Night air temperatures ranged

between 19 oC (22nd June) and 23 oC (23rd August). The temperature started to increase at

around 700 hrs reaching a maximum of 26.5 oC at 1700 hrs (23rd August). Nighttime and

morning conditions were calm, the wind speeds were low varying between 1 and 5 m/s

dominated by northerly winds in January and northeasterly winds on other days. The wind

velocities started to increase between 1000 and 1100 hrs dominated by south and north-westerly

winds. The relative humidity varied between 70 to 91 % during night and morning time. During

daytime, the relative humidity ranged between 65 and 80 %.

The days of 2017, illustrated in Fig. 4-3, showed a diel signal in all variables. Night air

temperatures ranged between 20 and 22 oC. The weather was warmer in June, July and August

than in January, March and May. At 1630 hours, the air temperature reached a peak of 24.7 oC

on 31 July and 24.99 oC on 27 June at 1700hours. The conditions were cool on 29 March. Night-

time wind speeds ranged from 1 to 6 m/s, mostly coming from the northeast. They started to

increase from around 800 hrs and reached a maximum of 8.23 m/s at 1200 hrs and 8.3 m/s at

1100 hrs observed on 27 June and 31 July, respectively. Increased wind speeds in the daytime

were frequently between south-easterly and west-south-westerly from May to August. In

general, 11 January and 29 March were characterised by low wind speeds with no clear pattern

in wind direction during the day. The relative humidity was high in the night (70 to 90 %) and

decreased in the daytime (800 to 1700 hrs). The relative humidity stayed low on the days of

May, June, July and August and dropped to 60.1 ± 3.3 % at 1000 hrs.

Although the shortwave radiation could become high in March and May (1096.89 W/m2

observed on 9 May at 1230 hrs), some fluctuations could still be observed (Fig. 4-33). However,

daytime solar radiations remained high and regular in June and July. When averaged from 1000

to 1400 hrs, at their highest peak, the shortwave radiation was 834.0 ± 88.9 W/m2 (27 June) and

837.6 ± 69.2 W/m2 (31 July). The lowest longwave radiation was recorded in June and July.

The month of May seemed to be a transitional month between the previous (January and March)

and following (June, July and August) months.

In summary, diel patterns were clearer in 2015 and 2017 than in 2016. The diel

differences depicted between the three years 2015, 2016 and 2017 suggests a different influence

on the lake water column. For instance, low wind speeds coupled with high shortwave radiation

and high relative humidity observed in March can induce the accumulation of heat in the upper

layers of the lake. This prompts a difference in the water density that results in formation of

diel thermoclines. The diel differences observed among the days and the months suggest a

seasonal cycle of the weather at the surface of the lake.

Results 22




Results 23




Results 24

4.1.2. Seasonal variation in the meteorology

Time series of daily averaged meteorological data, from January 2015 to August 2017 are

presented in Fig. 4-4. Moving averages were used to visualise the trend of the studied weather

variables. A strong seasonal signal occurred in the relative humidity, rain and longwave

radiation time series. The variation in the shortwave radiation was very limited. The months of

June, July and August showed a recurring pattern in all variables. During this period, slightly

increased wind velocities were mainly south-easterly (134.1 ± 5.7o in 2016 and 133.8 ± 3.2o in

2017). The intensification of the seasonal signal seemed to have started earlier (in May) and

stayed longer in 2017 than in 2016 (Tab. 4-1).

Figure 4-4: A time series of weather variables at the surface of Lake Kivu for (a) wind speed, (b) air

temperature, (c) relative humidity, (d) shortwave radiation and (e) longwave radiation (January 2015 to

August 2017). The grey lines show daily averages, the blue lines indicate the moving averages over 30

days.

Results 25

Table 4-1: A comparison of 2016 and 2017 for the mean air temperature (AirTC), relative humidity

(RH), wind speed (WS), wind direction (WindDir), shortwave radiation (SW), Longwave radiation

(LW) and the total rain observed in the dry season.

Variable Year 2016 Year 2017

May June July August May June July August

AirTC (oC) 22.1 21.6 21.7 22.3 22.2 22.6 22.1 22.0

RH (%) 81.5 76 73 68 78 73 73 75

WS(m/s) 2.8 3.2 2.8 3.5 3.5 3.4 3.2 3.2

WindDir (o) 148 140 131 130 134 135 136 130

SW (W/m2) 217 204 198 232 216 223 181 174

LW (W/m2) 396 383 380 377 387 382 384 391

Rain (mm) 266 68 6.2 11.6 33 1.4 3.6 14.2

Source: Meteorological station installed at the surface of Lake Kivu.

Following a decrease in the relative humidity at the surface of the lake, the lake water

is exposed to evaporation processes that lead to the cooling of the water column, followed by

the sink of heavier water. To understand the seasonal variations more generally, the seasonal

cycle in the surface meteorology was assessed using data covering the previous years (from

October 2012 to December 2014). The results (Fig. 4-5) depicted similar seasonal patterns in

the relative humidity and in the longwave radiation as observed in the period from 2015 to

2017. However, the 2012-2014 period was dominated by intense winds observed in May, very

limited variations in the solar radiation and low precipitations.

Results 26

Figure 4-5: A time series of daily averages at the surface of Lake Kivu for (a) wind speed, (b) air

temperature, (c) relative humidity, (d) shortwave radiation and (e) longwave radiation (October 2012 to

December 2014). The grey lines show daily averages, the blue lines indicate the moving averages over

30 days.

Results 27

4.2. Diel thermal profile of the water column

Diel thermal structure was assessed by analysing the CTD temperature profiles measured at

different times on the same day. Variability was studied in the uppermost 10 m where the

thermal structure reflected the accumulation of heat during the day. Temperature changes were

observed in the upper layer of the lake (Fig. 4-6). In the morning hours, the water column was

cool and nearly homogeneous. The surface water temperatures increased progressively with the

time of the day as the lake is heated by the sunlight. As a result of the energy accumulation,

profiles measured in the afternoon were warmer. The temperature variation is presented using

the mean and the coefficient of variation for a morning and an afternoon profiles measured on

the same day.

Tab. 4-2, 4-3 and 4-4 summarize the mean values of temperature measured at different

times of the day in 2015, 2016 and 2017, respectively. The water temperature at the surface of

the lake could increase by 1.0 oC as observed on 29 March, from 0916 to 1514 hrs. Although,

the time of measurement was not always the same, the coefficients of variation in temperature

were higher in profiles measured rate in the afternoon than in profiles measured before midday.

A high coefficient of variation illustrates a marked increase in temperature on one day as shown

in the vertical profiles in Fig. 4-6. Temperature variations resulted in the formation of diel

thermoclines: shallow warmer layers with warmer water overlying cooler water. These diel

thermoclines limited the mixing of the water column within shallow layers near the surface.

Table 4-2: Diel mean temperature and coefficient of variation (CV) in the upper 10 m layers of Lake


profiles.

Day of 2015 Time Mean (oC) CV (%)

05 Nov 10:35 24.58 0.366

15:19 24.63 0.447

13 Nov 09:54 24.67 0.365

16:03 24.78 1.05

20 Nov 10:06 24.78 0.404

16:15 25.00 1.12

03 Dec 09:51 24.64 0.203

15:56 24.83 0.926

Source: LKMP, CTD data 2015

Results 28



profiles.


14 Jan 09:21 25.06 0.039

17:10 25.23 0.594

28 Jan 10:47 24.59 0.000

15:31 24.73 0.566

10 Feb 10:20 24.65 0.04

16:39 24.88 0.723

17 Mar 11:27 25.32 0.355

16:03 25.58 1.486

05 May 11:40 25.27 0.277

16:30 25.24 0.674

23 Jun 10:13 24.30 0.165

13:20 24.45 0.818

20 Jul 10:49 23.86 0.168

16:20 24.05 0.707

23 Aug 09:24 23.92 0.293

16:09 24.13 1.077

19 Oct 08:46 24.64 0.122

15:27 24.85 0.926

30 Nov 10:56 24.66 0.040

16:52 24.67 0.446




profiles.


11 Jan 09:08 24.79 0.403

18:00 25.06 1.197

29 Mar 09:08 24.48 0.123

14:41 24.77 1.695

09 May 09:02 24.20 0.000

14:05 24.31 0.165

27 Jun 08:39 24.17 0.000

15:55 24.37 0.903

01 Aug 08:05 23.65 0.000

17:35 23.96 1.669


Results 29

Figure 4-6: Diel variations observed in temperature profiles measured in the upper 10 m of Lake Kivu

in seven months between November 2015 and August 2017. The water temperature increases with time

near the surface.

Analysis of temperature profiles measured at different times on consecutive days also

showed a thermal diurnal variation. The water column was warm on the afternoon of the first

day and cool in the morning of the following day. For instance, in 2017 the temperature of the

upper 10 m was 24.9 ± 0.4 oC on 28 March at 1514 hrs and decreased to 24.48 ± 0.03 oC on the

following morning at 0908 hrs. On 31 July the water column temperature was 23.93 ± 0.27 oC

at 1709 hrs, became cooler and homogeneous on the morning of 01 August with

23.65 ± 0.01 oC at 0805 hrs. On this day the water warmed with the time of the day and reached

23.96 ± 0.40 oC at 1735 hrs and dropped to 23.74 ± 0.03 oC on 02 August at 0925 hrs (Fig. 4-

7D).

On some days, the diel thermocline was not entirely eroded. On the morning of 10

January 2017, the epilimnion was well mixed while a profile in the evening showed the

Results 30

development of a diel thermocline at around 5 m depth. On 11 January, a morning profile

showed that night cooling events deepened the mixed layer to around 10 m but could not

remove the diel thermocline completely. This resulted into the formation of a stronger diel

gradient in temperature latter in the day (Fig. 4-7A). It is also important to realise that the

maximum depth of diel thermoclines can be deeper in some months than in others. An example

is given in Fig. 4-7B and C for profiles measured in March and August 2017 when the diel

thermocline deepened and reached ~15 and ~ 38 m, respectively.

Figure 4-7: Diurnal variability in temperature profiles measured on Lake Kivu in 2017, on (A)

10 January (1) at 0855, (2) at 1632 and on 11 January (3) at 0908, (4) at 1800. (B) on 29 March. (C) on

01 August 2017. (D) on 31July (1) at 17:09, 01 August (2 and 3) at 08:05 and 17:35, respectively and

on 02 August (4) at 09:25. Note the difference in the axis increments.

Results 31

4.3. Seasonal stratification 4.3.1. Vertical profile of the water column

Thermal structure

A total of 29 temperature profiles measured on 05, 13 and 20 November (n = 6, 8 and 8

respectively) and on 03 December 2015 (n =7) are illustrated in Fig. 4-8. Profiles taken on the

3 days of November showed the importance of the sampling day on the water column structure.

Although measured in the same month, the profiles showed a different structure in temperature.

On 05 November, the epilimnion was cooler and nearly homogeneous with a temperature of

24.5 oC. On 13 November, the lake water surface was slightly warmer ~ 24.6 oC. Thermal

gradients formed at around 12 m with a temperature of ~24.5 oC.

Figure 4-8: Vertical temperature profiles measured on Lake Kivu in November and December 2015.

The profiles show thermal gradients an indication of stratification in the water column.

Results 32

The layer below was cooler with temperature decreasing from ~24.5 to 24.0 oC. On

20 November, the mixed layer was warmer and showed a series of thermoclines as compared

to the profiles measured on 13 November. The main thermocline formed at 16 m where the

temperature was 24.6oC. Temperature ranged from 24.6 to 25oC in the layer above the gradient

and decreased from 24.6 to 23.7oC at the lower depth of the mixed layer.

The vertical thermal structure measured in 2016 is illustrated in Fig. 4-9. Temperature

profiles were measured in January (n=11), February (n=14), March (n=7), May (n=5),

June (n=7), July (n=9), August (n=6), October (n=14) and November (n=14). The water

temperature at the surface of the lake ranged from 23.9 oC observed in July and 25.5 oC in

March. In January, the water column was measured on the 14th and the 28th. The mixed layer

was warmer on the 14th with 25.0 oC than on the 28th characterised by a cooler 24.6 oC and

slightly deeper mixed layer. Profiles measured in February showed a mixed layer with ~24.6 oC.

In March, the epilimnion became warm with ~25.5 oC at the surface.

Figure 4-9: Vertical and temporal thermal structure in Lake Kivu for 87 profiles measured in 2016. The

lake water mixed layer deepens progressively from January to August then the straticafion starts in

October.

Results 33

The warming of the surface water induced changes in the water masses and a series of light

thermal gradients started developing above the seasonal thermocline with warmer and lighter

water overlying the cooler and heavier water. Analysis of profiles measured in May shows that

the lake remained warm as in March with a progressive erosion of the thermal gradients. In

June, the lake became cooler with a more homogeneous and deeper mixed layer with 24.3 oC.

In July and August, the lake water column remained cool with a temperature of around 23.8 oC

and 23.6 oC respectively characterised by a deeper isothermal epilimnion. In October and

November, the lake had become warmer with shallow mixed layers.

The difference observed among vertical profiles measured in January 2016 is shown in

Fig 4-10A. The relative humidity and longwave radiation decreased from 76.8 to 71.8% and

401.3 to 397.3 W/m2, on 14 and 28 January, respectively. These weather fluctuations and the

thermal structure of the water column on the two days, suggest that changes in the weather

conditions at the surface of the lake induced a loss of heat gathered in the mixed layer. The lake

became cooler and mixed slightly deeper on 28 January. However, the mixing did not remove

the seasonal thermocline. The lake remained stratified in January and February.

The weather conditions allowed a further increase of the warming of the epilimnion.

Fig. 4.10B shows changes that happened in the water column before a seasonal mixing occurs.

In March and May, the accumulation of heat resulted in the formation steps in the thermal

structure. From May to June, the mean monthly relative humidity and the longwave radiation

dropped from 81.5 to 76.0 % and from 396.4 to 383.4 W/m2 while the mean monthly wind

speed increased from 2.8 to 3.2 m/s. These changes induced a noticeable change in water

temperature. In June, thermal gradients were removed, the mixed layer became cooler,

homogeneous and deeper. The seasonal mixing remained until August (Fig. 4-9).

Figure 4-10: A comparison of temperature profiles measured in 2016. (A) on 14 and 28 January, the

vertical profiles show a noticeable thermal gradient with a difference in temperature and depth of the

mixed layer. (B) on 17 March, 07 May and 23 June, the vertical profiles show a change in temperature

ranges and depths of the mixed layer.

Results 34

Fig. 4-11 presents a total of 126 temperature profiles measured from January to August

2017. Measurements were made in January (n = 41), March (n = 18), May (n = 19),

June (n = 24), July (n = 6) and in August (n = 24). Apparently, the lake water temperature did

not vary a lot from January to March. The water column was homogeneous in the mixed layer

with a temperature of ~24.5 oC in January and 24.4 oC in March with a week gradient at 16 m.

In May, the epilimnion was much deeper and cooler with 24.2 oC. Temperature profiles

measured in June, July and August showed a progressive deepening and cooling of the mixed

layer with a temperature of 24.2 oC, 23.6 oC and 23.6 oC respectively.

Figure 4-11: Vertical thermal structure in Lake Kivu for 126 profiles measured form January to August

2017. The mixed layer deepens progressively from January to August. In the months of February and

April, no data were collected.

Results 35

Electrical conductivity at 25oC

The mixed layer did not show any variability in the conductivity profiles (Fig. 4-12).

Conductivity varied only slightly among profiles. The conductivity at the surface of lake ranged

from 1109.4 to 1148.2 μS/cm, observed in June and October 2016, respectively. In 2017 the

conductivity at the surface ranged from 1135.8 to 1169.2 μS/cm observed in March and August,

respectively. The lowest conductivity values (in the mixolimnion) were measured in June 2016.

This resulted in the formation of a strong chemocline gradient in that month. Vertical variations

were much more important than temporal variations. A sharp increase was observed below the

chemocline depth to a maximum of ~2290 μS/cm at 100 m.

Figure 4-12: Vertical conductivity corrected for temperature at 25oC for 242 profiles measured in Lake

Kivu on several months from November 2015 to August 2017. The EC remains constant in the mixed

layer and increases below the chemocline.

Results 36

pH

In 2016, the surface pH ranged from 8.64 to 9.27 (observed in March and February 2016,

respectively), and in 2017 from 8.97 to 9.79 (January and July) 2017 (Fig. 4-13). Although, the

pH was more homogeneous near the surface, unexpected sharp negative peaks occurred at a

depth between 25 to 75 m. It is unlikely that this discrepancy was brought about by the

instrument as it was observed in some profiles and not in others. The variations were much

higher from November 2015 to May 2016 and could reach values of <7.00 at 60 m. After this

period, the fluctuations became less important but remained common in most profiles at around

60 m. In the lower layers, the pH became nearly uniform with a similar trend in all profiles. The

mean pH values at 100 m were 6.85 ± 0.01 in 2015, 6.86 ± 0.09 in 2016 and 7.07 ± 0.29 in

2017.

Figure 4-13: pH profiles in Lake Kivu for 242 measurements from November 2015 to August 2017.

The pH decreases at a depth between 25 to 75 m.

Results 37

To understand the presence of the peaks pH profiles measured in 2012 were checked

(Figure 4-14). The surface pH ranged from 8.44 (August) to 9.61 (April). The pH averaged

6.63 ± 0.29 at 100 m. These pH profiles did not show peaks similar to those observed from

2015 to 2017.

Figure 4-14: pH of Lake Kivu water column for 40 profiles measured in 2012. The pH did not show

fluctuations.

Dissolved oxygen

The dissolved oxygen at the surface of the lake ranged from 4.63 mg/L (August 2017) to

5.93 mg/L (March 2017) (Fig. 4-15). The vertical profiles followed the same pattern as

temperature, and the oxygen gradients (oxycline) depths matched the thermocline depths.

During the stratification period, oxygen remained in shallow layers. During the mixing seasons,

the lake was oxygenated to deep zones at a depth of ~ 55 m. Below this depth, the water column

Results 38

was devoid of oxygen throughout the year. The oxygen saturation did not reach 100 %. At the

lower boundary of the oxycline and in the layers below, the saturation was 0 % (see the

appendix).

Figure 4-15: Vertical profiles of dissolved oxygen distribution in Lake Kivu water column. A total of

193 oxygen were measure between March 2016 and August 2017. The lake water is permanently anoxic

below ~ 55 m depth.

Chlorophyll fluorescence

Fig. 4-16 presents the chlorophyll fluorescence profiles measured in 2016 and 2017. The depth

of vertical distribution of chlorophyll mimicked the dissolved oxygen profile. In March, May,

October and November 2016, the distribution was limited to the upper 40 m. Peaks were

observed between 20 and 40 m, reaching 2.9 μg/L at 23 m in October. Although, the vertical

Results 39

distribution was much deeper during the mixing season (June, July and August 2016), the

concentrations remained low with a maximum of 2.2 μg/L observed at 23 m in August. In

January and March 2017 the water column exhibited a similar pattern as observed during the

stratification period of 2016.

As observed in the temperature profiles, in 2017 the mixing started earlier and was much

deeper than in 2016. This has affected the distribution of the chlorophyll fluorescence. In May

the fluorescence seemed to be uniformly distributed in the mixed layer, this indicating the

starting of the mixing in the water column. In June, July and August 2017, when the mixing

became important the chlorophyll fluorescence was both deeper and higher compared to 2016.

Figure 4-16: Vertical profiles of chlorophyll fluorescence for 193 profiles measured in Lak Kivu

between March 2016 to August 2017.

Results 40

4.3.2. Time series of measured parameters

The time series for temperature, conductivity, pH, chlorophyll fluorescence and dissolved

oxygen illustrate stratification and vertical mixing cycles observed in the mixolimnion of Lake

Kivu between November 2015 to August 2017 (Fig. 4-17&18). Multiple shallow stratified

layers were observed during the wet period, that is from October to May. During this period,

the mixolimnion was thermally stratified with mixing limited between 15 and 35 m (Fig. 4-

17A). As a response to changes observed in the surface meteorology, i.e. lower relative

humidity, lower longwave radiation and intensification of southeasterly winds, the water

column became cool in June, July and August. This resulted in the sinking of heavier water

masses to the lower layers, leading to a isothermal structure and deeper mixing in the upper

layer at ~48 m in 2016 and ~55 m in 2017.

The electrical conductivity did not show variations among months, a sharp increase was

only observed in the layers below the chemocline (Fig. 4-17B). pH contours showed a rapid pH

decrease in February, March and May 2016 when the surface pH decreased from 9 (observed

in other months) to 8.5 in March (Fig. 4-17C). This pH value was normally observed from

around 40 to 55 m depth in other months. Assuming the reduction of the pH is due to the two

gases as discussed above, they create an acidic environment which does not favour living

conditions. This may have affected chlorophll fluorescence (Fig. 4-18A) and the dissolved

oxygen (Fig. 4-18B) distribution.

The countour plot showed lower concentrations in the chlorophyll fluorescence in 2016.

Sometimes, the phytoplanton could be assembled in layers over the water column as observed

in dissolved oxygen in May and June. Besides, dissolved oxygen was found to be sequestered

in the the upper layer near the surface during stratification periods. The contours showed much

lower concentration of oxygen in the mixing season of 2017.

Results 41

Figure 4-17: Time series of (A) temperature (oC), (B) electrical conductivity (μS/cm) and (C) pH for all

profiles measured from Nov-2015 to Aug-2017. Month labels omitted on the horizontal axis indicate

months in which no profiles were measured. Contour intervals are 0.5 oC, 100 μS/cm and 0.5 for the pH.

For temperature, data are from 10 m to remove the effects of the diel variations.

Results 42

Figure 4-18: Time series of (A) chlorophyll fluorescence (μg/L) and (B) dissolved oxygen (mg/L)

measured in Lake Kivu from March 2016 to August 2017. Month labels omitted on the horizontal axis

indicate months in which no profiles were measured. Contour intervals for are 0.5 μg/L for Chl a and

0.5 mg/L for DO.

4.4. Variability in the thermal structure 2016-2017

A comparison was made for the thermal structure in 2016 and 2017 (Fig. 4-19). To minimize

the effects of diel variability, one profile measured in the morning hours was used, except for

the month of July 2017 for which measurements were made in the afternoon. In 2016, the water

column was warmer in 2016 and showed a stronger density gradient in the epilimnion than in

2017. The formation of a gradient in the temperature profiles suggests stratification conditions

in the lake. In 2016, the stratification period was longer (from January to May) and was marked

by stronger gradients than in 2017 (January to March). The water temperatures measured in

2017 were cool with a nearly isothermal structure.

For both 2016 and 2017, the months of June, July and August were important for vertical

circulation events in the water column. During this period, the weather data showed a signal of

seasonal changes. Therefore, changes observed in the water column are associated with changes

Results 43

in the meteorological data at the surface of the lake. Because the changes in the meteorological

conditions seemed to have started early in 2017 (observed in April), the seasonal mixing was

much deeper than in the months of 2016. A thermal profile measured on 09 May showed that

the lake was already cool with a homogeneous mixed layer. This infers that the mixing had

already started. A thermal gradient developing in the upper depths of the mixed layer in August

2016 suggested the starting of stratification.

Figure 4-19: Comparison of thermal structure in 2016 (bold line) and 2017 (dash line) in Lake Kivu.

The mixed layer was warmer and stronger gradients formed during the stratification period in 2016 than

in 2017.

Although, the mixed layer deepened during the mixing period, a strong thermal gradient could

be still be noticed in June 2016 (Fig. 4-19). The thermal structure in 2017 showed a weaker

stratification compared to 2016. On both years, the temperature in the hypolimnion, i.e. from

the seasonal thermocline to a depth of 100 m, did not show any fluctuations.

Results 44

4.5. Physical indices of mixing and stability 4.5.1. Thermocline depths

The trend of the thermocline depth was estimated from 242 temperature profiles in different

months between November 2015 and August 2017 (Fig. 4-20). Results (mean ± standard

deviation) indicate a progress upwelling of the thermal stratification from November 2015 to

January 2016 (28.4 ± 2.3, 25.2 ± 5.4 and 19.9 ± 4.7 m, respectively). As observed in February,

March and May 2016, temperature profiles showed persistent gradients. As a consequence the

lake could not mix and remained strongly stratified with thermocline depths observed at

30.5 ± 1.5, 30.1 ± 2.6 and 30.0 ± 1.6 m, respectively. During the dry season, the lake water

column was subjected to cooling events followed by a deeper mixing and the dropping of the

seasonal thermocline depth. The mixed layer extended down to 38.4 ± 0.4 m, 45.4 ± 2.2 m and

48.7 ± 0.8 m on the three consecutive months.

After the seasonal mixing, the warming of the surface water begins. This resulted in the

upwelling of thermocline depth to 17.9 ± 3.5 m in October and 30.7 ± 1.4 m in November.

Temperature profiles measured in 2017 indicated that the lake was stratified at 31.5 ± 0.9 m in

January and 31.9 ± 1.4 m in March. In May, the thermocline was observed at 35.3 ± 2.5 m

followed by a dry season marked by a pronounced downward extension to 45.2 ± 0.8 m in June,

53.7 ± 0.3 m in July and 52.2 ± 0.7 m in August. A comparison of the thermocline depth among

respective months showed that the seasonal thermocline was deeper in 2017.

Figure 4-20: Thermocline depths calculated from 242 CTD temperature profiles measured in Lake Kivu

between November 2015 to August 2017. From May, the thermocline depth moves downward and

reaches its maximum depth in August. The rest of the year the thermocline depths are shallow.

Results 45

4.5.2. Epilimnetic and hypolimnetic temperatures

To understand the differences in vertical mixing and stratification when surface waters are

warm or cool, the epilimnetic temperatures were computed for 186 profiles measured from

November 2015 and August 2017 (Fig. 4-21B). To reduce on the effect of diel temperature

variability, the epilimnetic temperatures were computed for temperature profiles measured

before 1500hrs. Although the number of profiles used to calculate the temperatures is different

from one month to another, resulting mean showed a trend similar to the vertical temperature

profiles.

Referring to measured vertical temperature profiles and observed seasonal mixing,

warm epilimnion layers limit mixing and enhance stratification. When the epilimnion water

temperature is above 25 oC, strong gradients form and limit vertical circulation. With seasonal

changes in the weather conditions at the surface, the epilimnion loses stored heat and becomes

cool progressively. The decrease of the epilimnetic temperatures in 2016 corresponds to a

gradual deepening of the mixed layer. When the mixing season ends, the temperatures increase

as observed in October and November. The epilimnetic temperatures were lower in 2017 than

in 2016. The epilimnion temperature in the months of March 2017 (~25.20 oC) was almost as

warm as May 2016 and May 2017 (~24.20 oC) was as cool as July 2016. The warmest

epilimnion was observed in March with ~25.4 oC in 2016 and ~25.2 oC in 2017.

Below the thermocline depth, the temperature decreased with depth. Minimum water

column temperatures varied from 23.15 to 23.17 oC observed between 80 and 90 m. The mean

hypolimnetic temperatures were estimated from 172 temperature profiles for the depth between

70 to 100 m (Fig. 4-21A). They showed a warming trend ~ 0.016 oC/year as the temperatures

increased from ~23.17 oC in November 2015 to 23.20 oC in August 2017.

Results 46

Figure 4-21: A time series for (A) Hypolimnetic temperature estimated for the layers between 70 and

100 m in 172 profiles. (B) Epilimnetic temperatures estimated from 186 profiles. Profiles were measured

between November 2015 and August 2017.

4.5.3. Schmidt stability and Wedderburn number

The Schmidt stability was calculated for 172 profiles for the water column between 0 and

100 m. The results show that the Schmidt stability was higher during the period of stratification

and lower during the mixing seasons (Fig. 4-22A). Higher energy of ~11 kJ/m2 was observed

in March and May 2016. The same months showed noticeable strong thermal gradients in their

vertical profiles and warm epilimnetic temperatures. The epilimnetic cooling events and deeper

thermocline depths decreased the Schmidt stability to around 7.8 kJ/m2 in August 2016. After

the season mixing, when the lake starts to stratify, the amount of energy increased to 9.5 kJ/m2.

Compared to 2016, the year 2017 showed lower amount of energy required to mix the water

column (0 to 100 m). Higher values of the Schmidt stability were observed in January and

March with around 9.5 kJ/m2. From March the energy dropped progressively to around 6.8 J/m2

in August 2017.

A

B

Results 47

The Wedderburn number (W) was high during the mixing seasons and low during the

stratification periods, ranging from 0.98 (observed in October 2016) to 22.96 (observed in

June 2016) (Fig. 4- 22B).

Figure 4-22: Timeseries for (A) Schmidt stability and (B) Wedderburn number calculated for 172

profiles measured in Lake Kivu between November 2015 to August 2017. The Schmidt stability

decreases with the deepening on of the mixed layer.

4.5.4. Buoyancy frequency

The buoyancy frequency was estimated for the temperature profiles. Results for the water

column stability (N2) are presented in Fig. 4-23. The vertical profiles follow the same pattern

as observed in the temperature profiles and the thermocline depths. Higher buoyancy frequency

observed near the surface are a result of the diel warming that creates shallow gradients and

oscillations occur in the water column. Between the diel thermoclines and the thermocline depth

the stability was neutral. The N2 increased at the thermocline depths. High local water column

stability, observed in March and May 2016, reached 10-3 rad2/s2 (18 cph). A strong gradient in

B

A

Results 48

June 2016 created a buoyancy frequency of around 9x10-4 rad2/s2 (~17cph) at the thermocline

depth. During the seasonal mixing, the buoyancy frequency was reduced. Below 60 m, the

water stability was reduced to around 0 rad2/s2.

Figure 4-23: Buoyancy frequency profiles calculated for temperature and salinity from CTD data

measured in Lake Kivu (November 2015 to August 2017). The stability increases during the statification

period (referring to the peaks at the thermocline depth).

Results 49

4.5.5. Correlation analysis of physical indices

The variations among estimated indices of mixing and stability (Fig. 4-24) shows that the

thermocline depths (TD) are significantly correlated with the epilimnetic temperatures (Epi)

(r = -0.65, n = 172, p < 0.05) and with the Schmidt stability (SS) (r = -0.79, n = 172, p < 0.05).

The SS was significantly correlated with the Epi (r = 0.74, n = 172, p < 0.05). The analysis

showed that for the 172 CTD profiles considered for this comparison, the TD were mostly

located at depths between 30 and 35 m and only few were located at depths of less than 20 m.

The Epi was dominated by temperatures between 24 and 24.5 oC and only few were above

26.5 oC. The SS was mainly distributed between 9.5 and 10 kJ/m2.

Figure 4-24: Pearson correlation analysis among calculated indices: thermocline depths (TD), Schmidt

Stability (SS) and epilimnetic temperatures (Epi). Indices show a correlation.

Discussion 50

CHAPTER 5

Discussion

5.1. Meteorological forcing on lake water mixing

The role of the meteorological conditions on mixing dynamics has been widely studied in AGL

(MacIntyre, 2012; Thiery et al., 2014a; Thiery et al., 2014b). Lake Kivu region, characterised

by a tropical climate, is dominated by two seasons: a dry season from June to September and a

wet season from October to May (Thiery et al., 2014b). This study showed a limited seasonal

variability in the air temperature and the incoming shortwave radiation, as observed elsewhere

in the tropical region. (MacIntyre & Melack, 2009) argued that mixing and stratification cycles,

in the tropical region, are caused by the changes in the wind speed and cloud cover that

decreases the amount of radiation that reaches the lake surface.

Major variations in surface water heat fluxes occurred toward the end of long rain

periods as it has been observed on Lake Victoria (MacIntyre et al., 2014). The seasonal peak in

the meteorological cycle at the surface of Lake Kivu was marked by a decrease in the relative

humidity, longwave radiation and precipitation during the dry seasons and a slight increase in

the wind velocities. The same period was marked by deeper mixed depths in the lake. Despite

the variations in the movement of the Intertropical Convergence Zone (ITCZ), their effects on

the duration of seasonal wind-driven mixing (Wolff et al., 2011) and the strong events of wind

during the monsoon over Lake Kivu (Kling et al., 2006), the changes in wind velocities are

smaller than at some of the other East African Great lakes during the dry season. However,

there are some months in some years in which increases occur. These are often in May or July.

There are also higher maxima in the dry seasons. Therefore, the contribution of wind to deep

mixing and upwelling is less than at some of the other East African Great Lakes. However, as

increased wind causes an increase in evaporation, and as temperatures cool and the mixed layer

deepens during the dry season, the changes in stratification due to the wind are important in

Lake Kivu.

The relative humidity and the longwave radiation show noticeable seasonality and

moderate the amount of heat that contributes to the stratification. During the dry seasons,

cloudiness reduces the radiation and a low relative humidity supports high evaporation from

surface lake water. As evaporation also depends on wind speed, the combined increased winds

and decreased relative humidity cause evaporation to increase. These scenarios result in the loss

of the near-surface energy. The seasonal deep mixing depicted in the dry season is, therefore, a

result of cooling and heat loss processes near the surface. Cooler and heavier water masses sink

to the lower layers leading to a convective deep mixing (Schmid & Wüest, 2012; Thiery et al.,

2014b). High primary production are common to the southern basin in this period, attesting to

upwelling (Darchambeau et al., 2014). After the monsoon, a weak stratification starts to develop

in the water column. The increased wind speeds observed during the dry season (Fig. 5-1),

might have only a slight contribution to the direct mixing of the water column.

Discussion 51

In 2016, the lake water was warmer than in 2017. The warm air temperatures (Fig. 4-4),

the delay in the increased winds (Fig. 5-1) and the fact that they were more intermittent in 2016

allowed the accumulation of heat in the lake water near the surface and the lake became

stratified as it has been observed in Lake Toolik (MacIntyre & Melack, 2009). Additionally,

NASA (2016) reported that strong El Niño events occurred in 2016, and may have affected the

physical processes in the lake and caused an unusual change of the surface water color in April

2016.

Figure 5-1: Wind speed variation at different time of the day. From 1000 to 1400 hrs (blue line and

moving averages in solid black line) and the rest of the day (red line and moving averages in dash lines).

Moving averages are calculated over 30 days.

5.2. Diel thermal structure

Lake Kivu shows diel stratifications as observed in other tropical lakes (MacIntyre et al., 2002).

The mixed layers of water bodies can be turbulent. However, in early morning hours, conditions

are calm, the evaporation reduces and the solar energy accumulates in the surface layers of the

lake resulting in the heating of the lake water (Imberger, 1985). The diel thermal variations,

observed in the top water, affect the density structure. This results in the formation of multiple

thermal stratifications near the surface as reported in other AGL by MacIntyre (2012). As the

wind stress increases during the day, they remove the thermal gradients and lower the depth of

the mixed layer. As the sun sets in the evening, the heat loss starts to increase and night cool

conditions remove most of the accumulated heat. The density gradients are removed causing

nearly isothermal profiles on the next morning (Imberger, 1985). During the deep mixing

periods, high evaporation and cooling processes remove the diel surface gradients and deepen

the isotherms to near the seasonal thermocline depths, while during stratification periods diel

density gradients are not eroded completely.

5.3. Seasonal variability in limnological variables 5.3.1. Thermal structure

During the wet season (September to May), the Lake Kivu water column undergoes shallow

stratification near the surface. In this period, the evaporation events are reduced and heat

accumulates in the water column. The mixed layers become warm. Heated surface layers

enhance the formation of a warmer and lighter water layer floating on heavier and cooler layer

(Wüest & Lorke, 2009). This thermal stratification reduces the vertical mixing as the wind is

Discussion 52

not able to overturn the established gradients. During the dry season (June to August), low

humidity together with the low incoming longwave radiation plus increased winds in the day

intensify the evaporative cooling process (Thiery et al., 2014a; Thiery et al., 2014b) that

removes the energy accumulated in the water column and induce mixing by gravity, the mixed

layer deepened to ~ 55 m a observed by Katsev et al. (2014).

Although both 2016 and 2017 showed seasonal deep mixing, the epilimnion was warmer

and more stratified during the rainy season prior to the southwest monsoon in 2016 than in

2017. Cool water temperatures in 2017 suggest higher evaporation and cooling events in the

water column. Although the epilimnetic temperatures were lower during the dry season,

seasonal mixing did not reach the minimum water temperature layer observed between 85 and

90 m. A warming trend (0.016 oC/year) was detected in the hypolimnion (70 to 100 m). The

increasing rate was consistent with an average rate of ~0.02 to 0.04 oC per year in Lake Kivu

between 2011 and 2012 (Katsev et al., 2014). Similar warming trends have been reported at a

rate of 0.10 oC and 0.15 oC per decade for Lake Victoria and Lake Albert, respectively (Hecky

et al., 1994; Lehman, 1998).

5.3.2. Dissolved oxygen and chlorophyll fluorescence

Temporal stratification highly influences the vertical mixing, gases and nutrient fluxes, the

phytoplankton as well as higher trophic levels (MacIntyre et al., 2014). The increase of the

density gradient slows down the vertical mixing and controls the depth of the mixed layer as

well as the distribution of dissolved oxygen and nutrients (Verburg et al., 2003). This explains

the patterns observed in the vertical distribution of the chlorophyll fluorescence and dissolved

oxygen. The lake was oxygenated in the mixed layer and anoxic in deep isolated zones. A

permanent absence of dissolved oxygen in the layers below 55 m of the lake is an indication

that the water remained below the mixed layers for long periods.

During the dry season, a uniformly mixed mixolimnion enhanced the deep distribution

of the dissolved oxygen. At the same time, it makes available the nutrients already concentrated

in the deeper layers of the lake. This highlights the effects of the intensity of the mixing on

processes happening in the lake. For Lake Kivu, nutrient loading is dominated by internal

upwelling (Pasche, 2009). Therefore nutrients accumulate at the lower interface of the

mixolimnion. When deep mixing events take place and reach this zone, they prompt the

recycling of nutrients by vertical mixing. Nutrients are then distributed in the mixed water

column (Schmid & Wüest, 2012). As a response to deep mixing and nutrients, the

phytoplankton becomes more productive and spreads to deeper layers.

When insufficient deep mixing takes place, the lower mixolimnion boundary becomes

weak due to turbulent movements (Schmid & Wüest, 2012). This creates a pathway for the

nutrients that remained sequestrated below the mixolimnion, they are then distributed in high

amounts on the next season. This is what may have happened for the years 2016 and 2017. The

epilimnion layer was warm with shallow stratification in 2016, seasonal deep mixing was short

and not as deep as in 2017. As a consequence, the phytoplankton did not show a noticeable

response to the mixing. In the following year (2017), the mixing started earlier and was deeper.

The abrupt changes in chl fluorescence follow those in DO, specific conductance, and

temperature. Nutrients were sufficiently distributed and enhanced the phytoplankton. Similar

scenarios have been discussed for Lake Tahoe by Goldman et al. (1989). It is unfortunate that

this study did not look at the nutrient loading during these years to see if any changes have

occurred.

Discussion 53

5.3.3. Conductivity profile

Conductivity profiles did not show any clear temporal trend. Conductivity was near constant in

the upper 50 m layer of the lake with only slight increase during the deep mixing period. A

similar conductivity profile steadiness was observed by Schmid and Wüest (2012). Vertical

variations are important compared to temporal variations. A chemocline develops below the

mixed layer and is marked by a rapid increase on a vertical scale.

5.3.4. pH variability

The negative peaks observed in the pH profiles did not show a clear pattern. This means they

were neither due to seasonal nor to vertical variability. Also, they were not observed in all

profiles, suggesting they did not come from the instrument error. Therefore, there should be

other drivers controlling pH changes that occur at particular depths. Considering that the year

2015 corresponds to the starting of methane extraction from Lake Kivu, the deviations observed

in the pH could be attributed to the reinjection of washwater with low pH and containing

hydrogen sulfide. The pH fluctuations infer a signature of the water re-injected back into the

lake at about 60 to 80 m during the gas extraction processes. This has caused a significant pH

variation at 60 m (Fig. 5-2).

Figure 5-2: Variance of the pH among profiles measured in Lake Kivu at 20, 40, 60 and 80 m in 2012

and 2015-2017. Boxes with the same letter were not significantly different (Kruskal Wallis anova, chi

sq. = 2.164, df = 3, p = 0.5391 for 2012; and chi sq = 34.409, d.f. = 3, p < 0.001 for 2015-2017.

The deep waters of Lake Kivu are rich in CO2 and H2S gases (Pasche, 2009). It could

be inferred that when this CO2 and H2S-rich water is re-injected back into shallow layers of the

lake, they contribute a high input of CO2 and H2S lifted up from deep water. The H2S is oxidised

which lowers the amount of dissolved oxygen (Eccles, 1974; Pasche, 2009). Therefore, increase

Discussion 54

of the H2S flux into the biozone would have effects on oxygen availability to organisms (Pasche

et al., 2009). At the same time the two acidic gases lower the pH. The changes are locally

observed in vertical profiles and then stratifies to local layers’ pH values. Apparently, the

fluctuations are reducing, as observed in last months measured in 2017 (Fig. 4-13). However,

the profiles show a progressive increase of a negative gradient at 60 m. At this depths, the pH

measurements show a significant difference in the period 2015-2017 (Fig. 5-2).

Another important pH variability was observed in the whole mixed layer column in

2017. When the phytoplankton is at high peak, during the seasonal mixing, they may consume

the CO2 more rapidly than the production rate by bacterial respiration. Insufficient CO2, is

compensated for by the dissociation of HCO3-. The latter decreases the concentration of H+ and

the pH of the water column increases.

5.4. Indices of mixing and stability

Thermocline depths

The thickness of the density gradient layer indicates the seasonal dynamics as a response to

mixing drivers. During the stratification periods, series of gradients form near the surface and

mixing is limited in shallow layers. In the dry season, changes in the meteorological conditions

induce changes in temperature. The water column becomes cool and isothermal progressively,

induce a deep mixing and cause the deepening of the thermocline to about 55 m. This

observation is similar to that demonstrated in Lake Malawi where mixing of the cool epilimnion

drops the thermocline by about 60 m (Eccles, 1974) and in Lake Tanganyika where the

thermocline lowers for 60 to 70 m (Plisnier et al., 1999). When the higher south-easterly wind

stops, oscillation movements start to take place. These oscillations produce internal waves in

the water column (Plisnier et al., 1999) and the thermocline upwells in shallow layers.

Schmidt stability

For a vertical mixing to occur in the water column, there is a need for mechanical forces that

remove the gradients created by a difference in the water density. Changes in a temperature

profile affect the vertical fluxes and the stability of the water column. During the monsoon

periods, when the thermocline is much deeper and an indication of much more isothermal

conditions, the Schmidt stability reflected lower values. The same difference was observed also

between a warmer 2016 and cooler 2017. Higher variations in the thermal structure result in the

formation of larger vertical density gradients. This suggested that a lower amount of energy is

required to mix a cool homogeneous water and higher energy is required to mix a warmer

stratified water column. Similar trends have been report on Lake Annie and Lake Rotorua by

Read et al. (2011).

Wedderburn number

The Wedderburn number, W, has been used to determine the stability of lakes based on the

movement of the thermocline as a response to the surface wind forces. For lakes with W > 15,

the thermocline will not tilt as a consequence of wind energy. When the values of 1 < W < 15

occur, slow deepening may occur. When W ~1, horizontal mixing is important. For W << 1,

Discussion 55

the depth of the mixed layer will increase (Kling et al., 2006; Read et al., 2011). The ideal trend

is for the W to decrease during the mixing seasons when the wind speed intensifies. However,

results from this study showed a different pattern. The W was low when the lake was stratified

and high when the lake was mixing. This suggests that the buoyancy force is greater than the

wind stress. Because the mixed layer depth is squared in the calculation of the Wedderburn

number, the Wedderburn numbers followed the same pattern as the mixed layer depths. The

deeper the mixed layer, the higher were the Wedderburn numbers.

For Lake Kivu, the winds are mostly from south. The lake water is therefore likely to

experience high wind stresses as they are mainly across the long axis of the lake. The wind

velocities at the surface of the lake increased to between 5 and 10 m/s during the dry season (i.e

mixing season) from 1000 to 1400 hrs (Fig. 5-2) and remained low during the rest of the day.

At the same time, the wind velocities at the Lake Kivu were considered to be low and their

contribution towards evaporation may be more important than for seasonal upwelling (Thiery

et al., 2014a). The tilting of the thermocline in Lake Kivu, as a result of wind impulse, requires

further investigation.

Buoyancy frequency

During the periods of stable stratification, the thermal gradient becomes stronger than in the

mixing season. The boundary between water parcels with different water masses is marked by

the presence of gradients and peaks in the water column (King et al., 2012). These conditions

cause the local water column stability to change. Any mixing will lead to a fluctuating vertical

heat flux and results in the formation of a weaker and steeper gradient (Wüest & Lorke, 2009).

A water column stratification is categorised as weak when N < 5 cph, moderate when

5 < N < 22 cph and strong when N > 22 cph (Kling et al., 2006). The values for N2 at the

thermocline depths indicated that the mixolimnion of Lake Kivu is moderately stratified during

the wet period and weakly stratified during in the dry season. The buoyancy frequency of the

Lake Kivu water column seems to be steady. The magnitude of stability at the thermocline

depth in February 2016 was similar to the one estimated in February 2002 by Kling et al. (2006).

The buoyancy frequency varies from one lake to another. It reaches a maximum value of

~15 cph in Lake Victoria and ~ 60 cph in dimictic lakes during summer time.

Conclusions and recommendations 56

CHAPTER 6

Conclusions and recommendations

6.1. Conclusions

Analysis of the CTD casts measured in the upper 100 m (in 2012 and from 2015 to 2017)

together with the surface meteorological data allowed the characterisation of vertical and

temporal changes in Lake Kivu and an assessment of the influence of the weather conditions

on the vertical mixing of the water column. A seasonal signal was observed during the dry

season (June to August in 2016 and April-August 2017) marked by a decrease in the relative

humidity, longwave radiation that occurred at the end of the rainy season. During the same

period, a slight intensification of the southeasterly winds was recorded between 1000 and

1400 hrs. The air temperature and the solar radiation showed limited variations.

Vertical mixing and fluxes in the mixolimnion of a deep Lake Kivu responded to

changes observed in the weather conditions at the surface of the lake. The mixed layer of the

lake exhibited diel thermal variability that highly depends on the solar energy. In the morning

hours, the thermal structure of the mixed layer was nearly homogeneous. As a result of the solar

energy absorption during the day, the lake water near the surface accumulated heat and

developed thermal gradients which are removed by cooling events during nighttime.

A seasonal vertical mixing pattern was observed in the water column. A deep seasonal mixing

occurred during the cool dry months (May-August) followed by the development of density

gradients toward the end of August leading to shallow thermal stratification during the rest of

the year. The hypolimnion temperatures, was isolated from the seasonal mixing in the

mixolimnion and showed a progressive warming trend in the water column from November

2015 to August 2017. Chlorophyll fluorescence and dissolved oxygen distribution was limited

in the mixed layer and mimicked the temperature profile. Conductivity showed temporal

constancy in the mixed layer. The pH of the lake at 60 m has significantly changed from 2015

to 2017 compared to 2012. A comparison made for 2016 and 2017 shows the interannual

variability both in the water column and in the meteorology at the surface of the Lake. The year

2017 was characterised by an early seasonal signal, cooler water column temperatures, deeper

mixing and longer seasonal mixing than 2016.

Temporal variations were confirmed by the physical indices of mixing and stability of

Lake Kivu. Cooler and therefore deeper mixing periods are marked by slight and deep gradients

whereas the gradients become shallow and large during the stratification periods. For a water

column with density gradients to mix, higher energy was required than for a well-mixed or

nearly homogeneous water column. The Schmidt stability was higher during the stratification

periods, when the epilimnion was warm, than in the mixing season when the mixed layer was

cool. The buoyancy frequency showed that the mixolimnion of Lake Kivu is moderately

stratified during the wet season and weakly stratified during the dry season.

Conclusions and recommendations 57

6.2. Recommendations

The analysed CTD data allowed visualisation and analysis of the mixing and stratification

periods and the patterns in other lake indices. However, because the data was intermittent, with

observations in some months missing, the exact time at which some changes in mixing and

stratification started and ended could not be determined. Besides, when water parameters are

measured at different locations and at different times, a rigorous analysis of seasonal changes

is confounded by differences in location. These two points can lead to a bias when analysing

the data.

With respect to these matters, the following is recommended to LKMP:

To establish a clear strategy for identification of fixed locations where profiles are

measured on a regular monthly basis.

To put in place a continuous monitoring of the thermal structure to detect changes that

happen on a short (diel) time scale.

To consider weekly to biweekly measurements of the water quality in selected locations

as monthly measurements leave a gap in capturing the changes in the water column.

To particularly investigate on the causes of the pH variability in the water column.

Future studies should consider the following:

To study the energy fluxes in the water column

To investigate on nutrients and gases (H2S, CO2 and CH4) fluxes in the water column

References 58

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Appendix

Tables

Table 6-1: Depth-area graph estimated for Lake Kivu using rLakeAnalyzer

Depth (m) Area (km2) Depth (m) Area (km2) Depth (m) Area (km2)

0 2370.0 36 2194.1 72 2018.2

1 2365.1 37 2189.2 73 2013.3

2 2360.2 38 2184.3 74 2008.4

3 2355.3 39 2179.4 75 2003.5

4 2350.5 40 2174.5 76 1998.6

5 2345.6 41 2169.6 77 1993.7

6 2340.7 42 2164.8 78 1988.8

7 2335.8 43 2159.9 79 1984.0

8 2330.9 44 2155.0 80 1979.1

9 2326.0 45 2150.1 81 1974.2

10 2321.1 46 2145.2 82 1969.3

11 2316.2 47 2140.3 83 1964.4

12 2311.4 48 2135.4 84 1959.5

13 2306.5 49 2130.6 85 1954.6

14 2301.6 50 2125.7 86 1949.8

15 2296.7 51 2120.8 87 1944.9

16 2291.8 52 2115.9 88 1940.0

17 2286.9 53 2111.0 89 1935.1

18 2282.0 54 2106.1 90 1930.2

19 2277.2 55 2101.2 91 1925.3

20 2272.3 56 2096.4 92 1920.4

21 2267.4 57 2091.5 93 1915.5

22 2262.5 58 2086.6 94 1910.7

23 2257.6 59 2081.7 95 1905.8

24 2252.7 60 2076.8 96 1900.9

25 2247.8 61 2071.9 97 1896.0

26 2242.9 62 2067.0 98 1891.1

27 2238.1 63 2062.1 99 1886.2

28 2233.2 64 2057.3 100 1881.3

29 2228.3 65 2052.4

30 2223.4 66 2047.5

31 2218.5 67 2042.6

32 2213.6 68 2037.7

33 2208.7 69 2032.8

34 2203.9 70 2027.9 35 2199.0 71 2023.1

References 64

Table 6-2: Details on sampling frequency and sampling time of the CTD measurements

Year Day Time of sampling Profiles’ ID Nbr of profiles 2012 20 Mar 12:36 to 14:48 297 to 299 3

21 Mar 09:04 to 10:55 300 to 302 3

25 Apr 08:23 to 12:30 306 to 310 5

26 Apr 08:59 315 1

06 Jun 07:50 to 11:17 316 to 320 5

07 Jun 09:00 322 1

10 Jul 08:27 to 11:58 323 to 327 5

11 Jul 08:36 331 1

22 Aug 08:49, 09:51, 15:39 to 16:59 332, 333, 335 to 337 5

26 Sep 10:17 to 16:16 338 to 343 6

03 Dec 08:37 to 12:41 344 to 348 5

S/total 40

2015 05 Nov 10:35 to 15:19 001 to 006 6

13 Nov 09:54 to 16:03 007 to 014 8

20 Nov 10:06 to 15:52 015 to 022 8

03 Dec 09:51 to 15:56 023 to 029 7

S/total 29

2016 14 Jan 09:21 to 12:25, 14:50 to17:10 030 to 033, 036 to 039 8

28 Jan 10:47 to 15:31 40 to 42 3

10 Feb 10:20 to 16:39 043 to 046 4

11 Feb 09:36 to 16:17 047 to 056 10

17 Mar 11:27 to 16:03 057 to 060 4

18 Mar 11:32 to 13:37 061 to 063 3

04 May 11:21, 14:02 064, 065 2

05 May 11:40, 14:29, 16:30 070, 073, 076 3

22 Jun 13:21, 14:28, 15:12, 15:47 078, 082, 084, 086 4

23 Jun 10:13 to 13:20 090 to 092 3

20 Jul 10:49 to 16:20 115 to 121 7

21 Jul 14:53, 16:09 127, 128 2

23 Aug 09:24 to 12:14, 16:09 130 to 134, 138 6

18 Oct 10:27 to 15:21 139 to 143 5

19 Oct 08:46 to 15:27 144 to 152 9

29 Nov 12:15 to 14:20 155 to 160 6

30 Nov 10:56 to 16:52 162 to 169 8

S/total 87

2017 10 Jan 08:55 to 16:32 170 to 188 19

11 Jan 09:08 to 18:00 190 to 211 22

28 Mar 09:16 to 15:14 212 to 218 7

29 Mar 09:08 to 14:41 219 to 229 11

09 May 09:02 to 14:05 230 to 235 6

10 May 08:09 to 12:19 236 to 248 13

27 Jun 08:39 to 15:55 249 to 266 18

31 Jul 14:45 to 17:09 267 to 272 6

01 Aug 08:03 to 17:35 273 to 292 20

02 Aug 09:25 to 10:38 293 to 296 4

S/total 126

TOTAL 282

References 65

Figures

Figure 6-1: Coefficient of variation (CV) calculated for temperature profiles measured in Lake Kivu in

2012, and from November 2015 to August 2017. On one month, profiles were measured from different

location of the lake but they do not show differences.

References 66

Figure 6-2: Vertical profiles of oxygen saturation measured in Lake Kivu from March 2016 to August

2017.

References 67

Equations

1. Equation used to calculate the Viscosity of water

Tstar = 647.27 (k)

Vstar = 55.071e-6 (Pa s)

Rhostar = 317.763 (kg/m3)

Rho = 1000

rho0 = rho/rhostar

h0 = 1

h1 = 0.978197

h2 = 0.579829

h3 = -0.202354

T0 = (T+273.15)/Tstar

v0 = sqrt(T0)/(h0+h1/T0+h2/T0^2+h3/T0^3)

v1 = exp(rho0*(0.5132407

+0.3205656*(1/T0-1)^1*(rho0-1)^0

-0.7782567*(1/T0-1)^4*(rho0-1)^0

+0.1885447*(1/T0-1)^5*(rho0-1)^0

+0.2151778*(1/T0-1)^0*(rho0-1)^1

+0.7317883*(1/T0-1)^1*(rho0-1)^1

+1.241044*(1/T0-1)^2*(rho0-1)^1

+1.476783*(1/T0-1)^3*(rho0-1)^1

-0.2818107*(1/T0-1)^0*(rho0-1)^2

-1.070786*(1/T0-1)^1*(rho0-1)^2

-1.263184*(1/T0-1)^2*(rho0-1)^2

+0.1778064*(1/T0-1)^0*(rho0-1)^3

+0.4605040*(1/T0-1)^1*(rho0-1)^3

+0.2340379*(1/T0-1)^2*(rho0-1)^3

-0.4924179*(1/T0-1)^3*(rho0-1)^3

-0.04176610*(1/T0-1)^0*(rho0-1)^4

+0.1600435*(1/T0-1)^3*(rho0-1)^4

-0.01578386*(1/T0-1)^1*(rho0-1)^5

-0.003629481*(1/T0-1)^3*(rho0-1)^6 ))

Viscosity = v0*v1*vstar

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Assessing the temporal mixing and stratification in Lake Kivu