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2017 IWA Symposium of Lake and Reservoir Management
Shanghai, China, 22-26 May, 2017
Understanding of Harmful Algal Bloom Dynamics with an Integrated
Modeling Approach in Tropical Water Bodies
Jingjie Zhang1*, Gerard Pijcke1, Wang Xuan1 , Vladan Babovic1, Karina Yew-Hoong Gin1, Hans Los2, Tineke
Troost2 , Wai Sum Chan3, Yanna Zhou3, Manh-Tuan Nguyen3
1Department
2Deltares,
3PUB,
of Civil and Environmental Engineering, National University of Singapore, 117577, Singapore
P.O. Box 177,2600 MH Delft,The Netherlands
Singapore’s National Water Agency, 40 Scotts Road #22-01, Environment Building Singapore 228231
Presenting Author: Karina Yew-Hong Gin
Keywords:
Harmful Algal Bloom; Integrated Modeling; Species composition and adaptation
Introduction
Harmful algal blooms may be driven by chemical, biological and physical factors or their
combined influence. To address the dynamics and mechanisms of the algal blooms, ecological
and environmental models are considered very useful in the assessment of changes in water
quality influenced by external environmental conditions and effective restoration of aquatic
ecosystems (Gurkan et al., 2006). However, traditional eutrophication models may only focus
on one or another aspect without considering species competition and adaptation to the
prevailing environmental conditions and catchment-scale assessment, which are critical to
better understanding the phenomenon of phytoplankton dynamics. In this paper, we present an
integrated modelling approach based on Delft3D Water Quality modelling suite incorporating
with BLOOM modeling (Los 2009) and applied it to study harmful algal bloom dynamics in
tropical water bodies in Singapore. The BLOOM model is based on optimizing phytoplankton
biomass under consideration of environmental constraints where species most adaptable to
environmental change will have higher biomass and finally become dominant species. The
BLOOM model was first calibrated and validated against observations, and then employed to
explore possible mechanisms of algal blooms and changes in phytoplankton species
composition driven by changes in land use and other environmental forcing functions. Our
results show that the integrated modeling approach can describe species dynamics and show
changes in species adaptation under different environmental conditions in tropical urban water
bodies.
Materials and Methods
The modelling framework consists of integrating six models and combined with BLOOM
modeling, which was characterized by experiments in consideration of the tropical
environmental condition as indicated in Figure 1. The schematic shows the integration of
models that provide input to other models and respective input data needed for each model.
Figure 1 Integrated modelling framework for studying harmful algal bloom dynamics
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2017 IWA Symposium of Lake and Reservoir Management
Shanghai, China, 22-26 May, 2017
8 phytoplankton species (groups) including Freshwater Diatoms (including Synedra), Green
algae (including Ankistrodesmus and Cosmarium), and 6 representative groups of
Cyanobacteria (Microcystis, Pseudanabaena, Cylindrospermopsis, Arthrospira, Planktothrix
and Planktolyngbya) were selected based on the observed abundance and estimated biomass
in the reservoirs. In order to characterize algal species in the tropical area, eco-physiological
parameters (e.g. maximum growth rates, mortality and respiration rates) of representative
cyanobacteria from both literature and experiments were used and included in the BLOOM
model.
Results and Discussion
Figure 2 demonstrates how phytoplankton species composition was influenced by changes in
environmental conditions and species characteristics. Water Body 1 was mainly limited by
phosphorus, nitrogen and followed by light and growth whereas Water Body 2 was less
influenced by nitrogen as relatively high concentrations of nitrogen were observed in this
system. This difference resulted in Cylindrospermopsis as the dominant species followed by
Pseudanabaena and Diatoms in Water Body 1 while Water Body 2 was mainly dominated by
Pseudanabaena, followed by Cylindrospermopsis and green algae. The difference in
dominant species in the two water bodies may be attributed to Cylindrospermopsis being a
better competitor with increasing nitrogen limitation as it can fix nitrogen from the
atmosphere compared to Pseudanabaena.
Figure 2 Influence of environmental condition on phytoplankton composition and competition
Conclusions
Changes in environmental conditions can result in changes in phytoplankton species
community structure and composition. A catchment-scale modeling approach can predict
dominant species and describe temporal-spatial dynamics of species in the aquatic
ecosystems.
References
Gurkan, Z., Zhang, J.J., Jorgensen, S.E., 2006. Development of a structurally dynamic model for forecasting the
effects of restoration of Lake Fure, Denmark. Ecological Modelling 197, 89-102.
Los, H. (2009). Eco-hydrodynamic modelling of primary production in coastal waters and lakes using BLOOM. PhD
thesis Wageningen University ISBN 978-90-8585-329-9.
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