1st Edition
Spatial Pattern Dynamics in Aquatic Ecosystem Modelling UNESCO-IHE PhD Thesis
In this work, several modelling approaches are explored to represent spatial pattern dynamics of aquatic populations in aquatic ecosystems by the combination of models, knowledge and data in different scales.
It is shown that including spatially distributed inputs retrieved from Remote Sensing images, a conventional physically-based Harmful Algal Bloom model can be enhanced. Also, Cellular Automata based models using high resolution photographs prove to be good in representing aquatic plant growth. Multi-Agent Systems can capture well the spatial patterns exhibited in GIS density maps. A synthesis modelling framework was developed to include biological/ecological growth and diffusive processes, and local effects in conventional modelling framework. The results of the complementary modelling paradigms investigated in this research can be of help in achieving a sustainable environmental management strategy.
Abstract
Samenvatting
Acknowledgement
Chapter 1 Introduction
1.1 Research scope
1.1.1 Background
1.1.2 Available approaches
1.2 Objectives and research questions
1.3 Thesis outline
Chapter 2 Theoretical background
2.1 Introduction
2.2 General description
2.3 Population life-system
2.4 Processes and factors influencing aquatic population dynamics
2.4.1 Impact of abiotic factors
2.4.2 Mortality, intraspecific interaction and interspecific competition
2.4.3 Biological growth and feedback to the external environment
2.4.4 Spatial patterns and local interactions
2.5 Summary
Chapter 3 Developments in modelling paradigms
3.1 Introduction
3.2 Overview
3.3 Physically- based modelling
3.4 Data-driven modelling
3.5 Discrete cellular automata
3.6 Individual / agent based and multi-agent systems
3.7 Emerging research needs
3.8 Summary
Chapter 4 Harmful algal bloom prediction using data-driven techniques
4.1 Introduction
4.2 Background
4.2.1 Harmful algal bloom events
4.2.2 Case study area
4.3 Methodology
4.4 Results
4.4.1 Correlation analysis and principle component analysis
4.4.2 Dominant factor analysis using ANN
4.4.3 FL model based on the best scenario of ANN
4.5 Summary
Chapter 5 Enhancing physically-based algal dynamics modelling using remote sensing images
5.1 Introduction
5.2 Background
5.3 BLOOM/GEM modelling instrument
5.3.1 Overview
5.3.2 General description of processes in BLOOM/GEM
5.4 Model application to the southern North Sea
5.4.1 Schematization
5.4.2 Hydrodynamics and the BLOOM/GEM model
5.5 Enhanced algal spatial pattern prediction in the Delft3DBloom/Gem model using RS images
5.5.1 Filling missing data in TSM maps retrieved from remote sensing images
5.5.2 Enhancing HAB modelling with TSM maps retrieved from RS images
5.5.3 Discussions and remarks
5.6 Summary
Chapter 6 Spatial pattern dynamics in cellular automata based aquatic ecosystem modelling
6.1 Introduction
6.2 Cellular automata
6.2.1 Background
6.2.2 Types of CA models
6.3 Photography-based cellular automata in aquatic plant dynamics modelling
6.3.1 Introduction
6.3.2 Influencing factors for water lily growth
6.3.3 Model setup
6.3.4 Analysis of results
6.3.5 Conclusions and discussion
6.4 Summary
Chapter 7 Revealing spatial pattern dynamics in aquatic ecosystem modelling with multi-agent systems
7.1 Introduction
7.2 Multi-agent systems
7.2.1 Concept
7.2.2 Components in MAS modelling
7.2.3 Advantages and possible problems in MAS modelling
7.2.4 MAS model construction
7.2.5 MAS in aquatic ecological modelling
7.3 Multi-agent system for Lake Veluwe, Netherlands
7.3.1 Introduction
7.3.2 Conceptual model and operational rules
7.3.3 Sensitivity analysis
7.3.4 Analysis of model results
7.3.4 Discussion
7.4 Summary
Chapter 8 A synthesis of physically-based water quality modelling and multi-agent-based population dynamics modelling
8.1 Introduction
8.2 Delft3D-WAQ open process library
8.2.1 Background
8.2.2 Adding modules into the DELWAQ open process library
8.3 Synthesis of multi-agent based aquatic plant dynamics model into open process library
8.3.1 Purpose
8.3.2 State variables and scales
8.3.3 Processes
8.3.4 Sub modules and modelling procedure
8.4 Simulation experiments
8.4.1 Sensitivity analysis
8.4.2 Application in Lake Veluwe
8.5 Analysis of results
8.5.1 Sensitivity analysis results
8.5.2 Modelling results for Lake Veluwe
8.6 Discussion and summary
Chapter 9 Conclusions and recommendations
9.1 Conclusions
9.1.1 Differential equation-based approaches
9.1.2 Discrete cellular automata models
9.1.3 Multi-agent systems
9.1.4 A synthesis of a multi-process ecohydraulics system
9.2 Recommendations
References
Biography
Hong Li carried out her PhD research in collaboration with Deltares | Delft Hydraulics, UNESCO-IHE and Delft University of Technology. Her current interests are in spatial temporal dynamics in aquatic ecosystems. She has experience in several areas including, hydroinformatics and ecohydraulics modelling, aquatic population dynamics, and modelling spatial patterns of aquatic populations with various data sources and advanced computational technologies. She holds a M.Sc. degree in Hydrology from Hohai University, China and one in Hydroinformatics from UNESCO-IHE Institute for Water Education, the Netherlands.
