Predictive modelling of eutrophication and algal bloom formation in tropical lakes

253 Pages·2012·3.11 MB·English

Predictive modelling of
eutrophication and algal bloom
formation in tropical lakes

MOHD YUSOFF ISHAK

A thesis submitted for the award of Doctor of Philosophy

2012

Ecoinformatics and Watershed Ecology Laboratory
School of Earth and Environmental Sciences
Environmental Biology Discipline
The University of Adelaide, Australia
CONTENTS

THESIS CONTENTS ..................................................................................................... i
Abstract .......................................................................................................................... v
Declaration ................................................................................................................... vii
Acknowledgements .................................................................................................... viii
List of Tables ................................................................................................................ ix
List of Figures .............................................................................................................. xii

CHAPTER 1 ......................................................................................................... 1
1.0 INTRODUCTION ................................................................................................... 1

1.1 General Introduction ............................................................................................ 1

1.2 The Computational Modelling Approach of This Study ..................................... 2

1.3 Objectives and Achievements of This Study ....................................................... 3

1.4 Thesis Overview .................................................................................................. 5

CHAPTER 2 ......................................................................................................... 6
2.0 LITERATURE REVIEW ........................................................................................ 6

2.1 Lake Ecosystem and Lake Models ...................................................................... 7
2.1.1 Lake Ecosystem ............................................................................................ 7
2.1.2 Ecological Lake Models ............................................................................... 8

2.2 Data-driven Lake Models .................................................................................. 13
2.2.1 Artificial Neural Networks (ANN) ............................................................. 14
2.2.2 Evolutionary Algorithms (EA) ................................................................... 15
2.2.3 HEA: Hybrid Evolutionary Algorithms ...................................................... 16

2.3. Process-based Lake Models .............................................................................. 17
2.3.1 The Differential Equation Approach .......................................................... 17
2.3.2 The Model SALMO-OO ............................................................................. 18
2.3.3 Structure and Parameter Optimisation of SALMO-PLUS.......................... 22
2.3.4 Particle Swarm Optimisation (PSO) ........................................................... 23

2.4 DYRESM Model for Lake Putrajaya ................................................................. 25

2.5 Validation Methods ............................................................................................ 26
2.5.1 Leave-one-out Cross Validation ................................................................. 26
2.5.2 Bootstrap Procedure .................................................................................... 27

2.6 Models Generic for Lake Ecosystem Categories ............................................... 28

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2.7 Tropical Lake Ecosystem ................................................................................... 28

2.8 Limnological Research in Malaysia................................................................... 32

2.9 Past Modelling Effort on Lakes in Malaysia ..................................................... 33
2.9.1 Lake Putrajaya ............................................................................................ 34
2.9.2 Lake Kenyir ................................................................................................ 34
2.9.3 Lake Penang ................................................................................................ 35

2.10 The Gaps on Tropical Lakes Modelling .......................................................... 36
2.10.1 Data Availability and Quality ................................................................... 37
2.10.2 Research Efforts ........................................................................................ 38
2.10.3 Malaysian Scenario ................................................................................... 38

2.11 Recent Trends in Ecosystem Modelling .......................................................... 39

2.12 Summary .......................................................................................................... 40

CHAPTER 3 ....................................................................................................... 42
3.0 MATERIALS AND METHODS ........................................................................... 42

3.1. Study Sites and Data Acquisition ..................................................................... 42
3.1.1 Limnological Time Series Data of Lake Putrajaya ..................................... 43
3.1.2 Lake Kenyir ................................................................................................ 47
3.1.3 Lake Penang ................................................................................................ 50

3.2 SALMO-OO (Simulation by means of an Analytical Lake MOdel) ................. 53
3.2.1 The Overview of SALMO-OO ................................................................... 54
3.2.2 Data Requirements for the Model SALMO-OO ......................................... 58

3.3 SALMO-PLUS .................................................................................................. 59
3.3.1 Particle Swarm Optimisation Method for Model Structure and Parameter
Optimisation in SALMO-PLUS .......................................................................... 59
3.3.2 Running SALMO-PLUS ............................................................................. 63

3.4 Running SALMO-OO ........................................................................................ 64
3.4.1 Addition of Process Models from Law et al. (2009) to the Library of
SALMO-OO ........................................................................................................ 66
3.4.2 Evaluation Criteria for Potential Models .................................................... 67

3.5 Description of New Process Models of Phytoplankton Growth and Grazing and
Zooplankton Growth and Mortality for SALMO-OO Library ................................ 68
3.5.1 Phytoplankton Process Model in Law et al. (2009) .................................... 69
3.5.2 Zooplankton Process Model in Law et al. (2009)....................................... 72
3.5.3 Differences Between Process Model in Law et al. (2009) and SALMO-OO
Simulation Library ............................................................................................... 73

3.6 HEA (Hybrid Evolutionary Algorithms) ........................................................... 75
3.6.1 Data Requirements for the HEA ................................................................. 77
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3.6.2 Data Treatment for the HEA ....................................................................... 78
3.6.3 Sensitivity Analysis .................................................................................... 79
3.6.4. Generic Predictive Rules for Particular Lake Category ............................. 80

3.7. DYRESM Model .............................................................................................. 80

3.8. Fitness evaluation.............................................................................................. 81

CHAPTER 4 ....................................................................................................... 82
4.0 RESULTS .............................................................................................................. 82

4.1 Experimental Results from HEA and the Generic Rules for Tropical Lakes .... 82
4.1.1 Predictive Chl-a Model for Lake Putrajaya ................................................ 84
4.1.2 Predictive Algal Biovolume Model for Lake Kenyir ................................. 91
4.1.3 Predictive Chl-a Model for Lake Penang.................................................... 96
4.1.4 Predictive Algal Biovolume Model for Lake Penang ............................... 101

4.2 Towards a Generic Forecasting Model for Algal Biovolume in Mesotrophic
Tropical-stratified Lakes ........................................................................................ 104
4.2.1 Experiment 1 ............................................................................................. 105
4.2.2 Experiment 2 ............................................................................................. 109

4.3 Generic Forecasting Model for Algal Biovolume in Mesotrophic Tropical-
stratified Lakes using Electronically Measured Data ............................................ 116
4.3.1 Experiment with Merged Electronically Measured Data for Lakes Kenyir
and Penang ......................................................................................................... 116
4.3.2 Experiment with Merged Electronically Measured Data for Lake Putrajaya
............................................................................................................................ 121

4.4 Model Identification for Different Lake Categories by SALMO PLUS ......... 125

4.5 Mesotrophic tropical-stratified Lakes Kenyir and Penang .............................. 127
4.5.1 Simulation Results for Lake Kenyir by Means of the Simulation System
SALMO-PLUS .................................................................................................. 129
4.5.2 Simulation Results for Lake Penang by Means of the Simulation System
SALMO-PLUS .................................................................................................. 135

4.6 Dimictic Mesotrophic Saidenbach Reservoir .................................................. 141
4.6.1 Simulation Results by Means of the Simulation System SALMO-PLUS for
Saidenbach Reservoir, Germany ........................................................................ 141

4.7 Warm-monomictic Hypertrophic Lake ............................................................ 147
4.7.1 Simulation Results by Means of the Simulation SALMO-PLUS for
Roodeplaat Dam, South Africa .......................................................................... 147

4.8 Warm Monomictic and Mesotrophic (South Para Reservoir, South Australia)
................................................................................................................................ 153
4.8.1 Simulation results by Means of the Simulation SALMO-PLUS for South
Para Reservoir, South Australia ......................................................................... 153
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4.9 Extension of the SALMO-OO Simulation Library by Additional Process Model
................................................................................................................................ 159
4.9.1 Mesotrophic Tropical-stratified (Lake Kenyir) ........................................ 165
4.9.2 Mesotrophic Tropical-stratified (Lake Penang) ........................................ 168
4.9.3 Dimictic and Mesotrophic (Saidenbach Reservoir, Germany) ................. 171
4.9.4 Warm Monomictic and Hypertrophic (Roodeplaat Dam, South Africa) .. 174

CHAPTER 5 ..................................................................................................... 178
5.0 DISCUSSION AND CONCLUSIONS ............................................................... 178

5.1 General Discussion .......................................................................................... 178

5.2 The SALMO-PLUS Model as a Tool for Tropical Lake Ecosystem Analysis 179
5.2.1 Parameter Optimisation by SALMO-PLUS ............................................. 180
5.2.2 Initial Value Identification ........................................................................ 183

5.3 Addition of the New Process Model from Law et al. (2009) into SALMO-OO
Simulation Library ................................................................................................. 185
5.3.1 Evaluation of SALMO-OO Results .......................................................... 186

5.4 Data-driven Forecasting Models Developed by HEA ..................................... 190
5.4.1 Input Sensitivity Analyses ........................................................................ 192
5.4.2 Generic Forecasting Model of Algal Biovolumes for the Lakes Penang and
Kenyir ................................................................................................................ 197
5.4.3 Generic Forecasting Model of Algal Biovolumes for the Lakes Penang and
Kenyir using Electronically Measured Data ...................................................... 197

5.5 Combination of Process-based and Data–driven Modelling Techniques ........ 198

5.6 The DYRESM Model for Tropical Lakes ....................................................... 199

5.7 The Limitation of Input Data Set from Tropical Lakes in this Research ......... 202

5.8 Closing Gaps on the Tropical Lake Ecosystem Analysis: Role of SALMO-
PLUS and HEA Models ......................................................................................... 204

5.9 Algal Functional Groups Dynamics in Tropical Lake Ecosystem ................... 205

5.10 Conclusions .................................................................................................... 206

5.11 Future Works ................................................................................................. 208

APPENDIX A: Manual for preparing input and measured data, uploading data and
operating the SALMO-PLUS

APPENDIX B: Input files for DYRESM model with a brief description for each file.
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Abstract

My original contribution to knowledge is the successful application of two
modelling paradigms 1) SALMO-PLUS process based model and 2) HEA data driven
model to tropical lakes of different morphometry and trophic status. The application
of SALMO-PLUS to tropical lakes involves utilising the SALMO-OO model
structure for optimisation. This was followed by multi objective parameter
optimisation on selected parameters to seek the optimum parameter values that can
model the algal dynamics and state variables fluctuations in the tropical lakes to an
acceptable magnitude and peaks.

SALMO-PLUS is another version SALMO-OO with capability to run
optimisation by means of particle swarm optimisation (PSO) method. SALMO-OO
has been used as a research tool over a number of lakes with different trophic states
and mixing conditions to simulate algal succession and respond to ecosystem
dynamic. SALMO-OO is driven by process-based differential equations and works
by utilizing a library of three phytoplankton growth and three grazing process models.

Evolutionary algorithms (EA) are bio-inspired adaptive methods which mimic
processes of biological evolution, natural selection and genetic variation such as
cross-over and mutation to develop solutions to complex computational problems
(Recknagel et al, 2006). HEA is designed for rule discovery in water quality time-
series (Cao et al., 2006b) and is capable of forecasting potential algal population
dynamics and outbreaks in water bodies.

The SALMO-PLUS model was applied for simulating the state variables of
selected lakes (Lake Kenyir, Lake Penang, Saidenbach Reservoir, Roodeplaat Dam
and South Para Reservoir). Measured data from the year 2005 and 1992 were used
for Lake Penang and Lake Kenyir respectively. The HEA was applied for predicting
the Chl-a and algal biovolume abundance on tropical lakes (Lake Putrajaya, Lake
Penang and Lake Kenyir) in Malaysia. This study discusses the application of
SALMO-PLUS and HEA towards tropical lakes eutrophication management. The
results of application of SALMO-PLUS on tropical lakes are presented, simulating
response of the phytoplankton community to fluctuation in nutrient loading, light
availability and hydrological aspect in the water bodies. Results of applying HEA on
tropical lakes are also interpreted in the context of empirical and causal knowledge on
Chl-a and algal biovolumes dynamics under tropical lake water quality conditions by
means of rule-based model.

Results for both Lake Kenyir and Penang showed that SALMO-PLUS were
able to predict annual average trends not only for chlorophyll-a but also other state
variables and algal functional groups. Simulated state variables namely Chl-a, N and
P showed good agreement with field observations data for both lakes. Despite the
fact that this is the first time SALMO-PLUS been used for tropical lakes and the
limited data availability from this region, the simulated values of biological and
nutrient state variables match reasonably with measured data. Outcomes from
SALMO-PLUS simulation show consistent compliance with algal community
assembly obtained from other researchers.

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The HEA achieved reasonable accuracy in predicting timing and magnitudes
of algal blooms up to 7-days-ahead. The HEA proved to be most efficient in
modelling and predicting seasonal dynamics of chlorophyll-a and algal biovolumes.
A sensitivity analysis conducted for Lake Penang revealed that algal abundance is not
only driven by physical and chemicals characteristics of the water body but also by
impact of inorganic substances in the water that contributes to high level of chemical
oxygen demand in the water as well.

In addition, this study has successfully implemented a new process model
from Law et al. (2009) consisting algal growth, algal grazing, zooplankton growth
and zooplankton mortality functions into the SALMO-OO simulation library.
Combination of this new process models were tested on dataset from Lake Kenyir,
Lake Penang, Saidenbach Reservoir and Roodeplaat Dam within the simulation
library to discover the optimal model structures for respective water bodies. Even
though the new process model was not selected in complete totality as the optimal
model structure for any of the test lakes, the addition has added another alternative for
water body simulation in SALMO-OO process library.

Based on these forecasting results, both SALMO-PLUS and HEA have
showed potential for utilisation in early warning and strategic control of algal blooms
in tropical freshwater lakes. The generic nature of HEA forecast model was also
observed when tested for forecasting algal biovolume for merged data of similar lake
ecosystem category. Results from merged Lake Kenyir and Lake Penang data
showed reasonable accuracy in predicting the timing and magnitudes of algal blooms
up to 7-days-ahead. Addition of the new process model from Law et al. (2009) into
the SALMO-PLUS simulation library has also expanded the alternative for lake
category simulation to give a more comprehensive decision support tool for lake and
reservoir management. This study has also affirmed the generality and flexibility of
SALMO-PLUS for usage in tropical lakes modelling. SALMO-PLUS was observed
to be capable of simulating simultaneous seasonal fluctuations in algal growth and
nutrients (phosphate and nitrate) making it valuable for forecasting the impacts of
various simulated scenarios for various lake management regimes.

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Declaration

I declare that this work contains no material which has been accepted for the award of
any other degree or diploma in any university or other tertiary institution and to the
best of my knowledge and belief, contains no material previously published or written
by another person, except where due reference has been made in the text.

I give consent to this copy of my thesis, when deposited in the University Libraries,
being made available in all forms of media, now or hereafter known.

Mohd Yusoff Ishak

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Acknowledgements

In presenting this thesis, I have received generous help from numerous individuals
whom I should like to thank.

I would like to express my gratitude to my principal supervisor, A/P Friedrich
Recknagel for his patience with me and mentoring throughout my years as a PhD
student and for giving me the opportunity to carry out the research. Thanks also to
both my second supervisor (Dr. David Lewis) and external supervisor (Prof. Fatimah
Md Yusoff) for their support and helpful tips.

I would also like to thank the following people for their guidance and contribution to
my PhD project:

To Dr Hongqing Cao, Byron Zhang, Jeffrey Newman and Samuel Martin for their
valuable inputs during SALMO-OO and HEA experiments.

To the Malaysian government for their funding of my tuition fee, the Universiti Putra
Malaysia and the local authorities for respective water bodies (Putrajaya Corporation,
Perbadanan Bekalan Air Pulau Pinang and Tenaga Nasional Berhad) studied in this
project as well as Meteorological Department, Institute of Environment and Water
Resource Management (IPASA) and Department of Environment Malaysia. I would
like to extend thanks to the many people in Malaysia and elsewhere, who so
generously contributed to the work presented in this thesis. The extensive results
presented herein are due in part to their contributions.

To my fellow postgraduate colleagues; Young-Kil Kim, Grace Wai, Nor Azman
Kasan, Pranay Sharma, Philip Northeast, Andrew Barker, Peter Ward and Rebecca
Duffield.

Finally, and by no means least, I would like to express my heartfelt thanks to my
wonderful wife, Siti Shafikah Md Daud, who has stood by me throughout the stresses
and difficulties that being a graduate student brings. Thanks also to my parent and
siblings.

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List of Tables

Table 1 General characteristics of study lakes ............................................................. 43

Table 2 List of parameters measured in situ and ex situ in Lake Putrajaya ................. 44

Table 3 List of physico-chemical and biological parameters measured in situ and ex
situ in Lake Kenyir and used for this study ................................................................. 48

Table 4 List of physico-chemical and biological parameters measured in situ and ex
situ in Lake Penang and used for this study ................................................................. 51

Table 5 State variables and processes represented by SALMO-OO (from Recknagel et
al. (2008a)) ................................................................................................................... 56

Table 6 Process library for algal growth and grazing, zooplankton growth and
mortality with AGRO[i] , AGRA[i] , ZGRO[i] , ZMO[i] , adopted from Benndorf
A A A A
and Recknagel (1982) AGRO[i] , AGRA[i] , ZGRO[i] , ZMO[i] adopted from
B B B B
Park et. al. (1974) AGRO[i] , AGRA[i] , ZGRO[i] , ZMO[i] adopted from
C C C C
Hongping and Jianyi (2002) and AGRO[i] , AGRA[i] , ZGRO[i] , ZMO[i] ,
D D C D
adopted from Arhonditsis and Brett (2005) (from Recknagel et al. (2008a)) ............. 57

Table 7 Three lake categories and their best performing structure of the algal mass
balance equations of SALMO-OO (from Recknagel et al. (2008a)) ........................... 57

Table 8 Input data required for running the SALMO-OO model ................................ 58

Table 9 Daily input data used for HEA in this study ................................................... 77

Table 10 Parameter settings of HEA for rule model discovery (Cao et al., 2006b) .... 79

Table 11 List of input data required for running the DYRESM model ....................... 81

Table 12 Spectrum of environmental conditions of the lakes chosen for model
identification by SALMO-PLUS ............................................................................... 125

Table 13 Sources of process models included in the library of SALMO-PLUS ....... 126

Table 14 List of parameters used for multi objective parameter optimisation in
SALMO-PLUS .......................................................................................................... 128

Table 15 Expert-based pre-set and interactively fine-tuned initial values for state
variables achieved by SALMO-PLUS for Lake Kenyir. ........................................... 129

Table 16 Expert-based pre-set, optimised and interactively fine-tuned parameter
values achieved by SALMO-PLUS for Lake Kenyir; X1=diatoms, X2=green algae
and X3=blue-green algae ........................................................................................... 130

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