1st Edition

Eco-hydraulic Modelling of Eutrophication for Reservoir Management





ISBN 9780415573825
Published May 11, 2010 by CRC Press
264 Pages - 50 Color & 50 B/W Illustrations

USD $130.00

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Book Description

This study presents an systematic approach to water quality assessment, hybrid modelling and decision support for eutrophication management in deep reservoirs. It is found that during the summer monsoon the catchment runoff into the Yongdam reservoir induces a transfer of pollutants from a middle stratified layer to the surface layer. Although the transport  mechanism limits nutrient accumulation on the bottom of the reservoir, it also offers an opportunity for on-going algae production in the surface water. Physically based modelling is used to understand the process of micro-scale turbulent mixing and its impact on the nutrient uptake by algae. Further, a data-driven model using clustering and partial least squares regression which uses results from a physically based model of the reservoir successfully predicts Chlorophyll-a concentrations.

Table of Contents

INTRODUCTION
Man-Made Reservoir
Characteristics of Reservoir
Management Issues
Framework of Reservoir Management
Challenges
Study Site
Study Aim, Objective and Hypothesis
Layout of the Thesis

RESERVOIR ECOSYSTEMS
Physical Structure
Abiotic and Biotic Structure
Eutrophication

PHYSICAL PROCESSES OF RESERVOIRS
Mixing Processes in a Reservoir
Scales of Motions
Navier-Stokes Equation
Reynolds Time-Averaged Mean Flow and Transport Equations
Turbulent Viscosity

PHYSICALLY BASED NUMERICAL MODELLING
Equations for Physical Process
Solution of Equations
Methods of Time Variable Solutions
Lake/Reservoir Modelling
Instantiating Models

DATA-DRIVEN MODELLING
Introduction
Machine Learning Methods
Application of DDM to Eco-Environment

CASE STUDY: DATA ACQUISITION AND ASSESSMENT
Research Site
Material and Methods
Water Quality Assessment
Algal Growth Potential Test
Conclusion

CASE STUDY: CONSTRUCTION OF MODELS
Outline of Case Study
Eco-Hydrodynamic Modelling
Data-Driven Modelling
Hybrid Model
Decision Support

CONCLUSIONS AND RECOMMENDATIONS
Conclusions
Recommendations for Future Research

Appendices
References

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