Abstract

ABSTRACTReservoirs are major sources of water supply in many densely populated areas. Eutrophic state plays an important role in reflecting the health status of reservoir ecosystem and it is a key field of water environment study. In this paper the method of grading the eutrophic state of reservoir based on the results of Environmental Fluid Dynamics Code (EFDC) model was proposed in order to obtain the spatial and temporal evaluation of eutrophic state in reservoir. Taking Tianjin Erwangzhuang reservoir as an example, the temporal and spatial trends of hydrodynamic and water quality of the reservoir were studied. The eutrophic state and the limited factor of eutrophication were predicted for Tianjin Erwangzhuang reservoir. This study provides a useful tool for understanding of the eutrophic state grade in a reservoir and surface water environment.

Highlights

  • Reservoir as an important water resource is of increasing importance for worldwide economic and social sustainable development

  • In this paper the method of grading the eutrophic state of reservoir based on the results of Environmental Fluid Dynamics Code (EFDC) model was proposed in order to obtain the spatial and temporal evaluation of eutrophic state in reservoir

  • This study provides a useful tool for understanding of the eutrophic state grade in a reservoir and surface water environment

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Summary

Introduction

Reservoir as an important water resource is of increasing importance for worldwide economic and social sustainable development. The evaluation methods of eutrophication are mainly eutrophic state index (Elzbieta et al, 2014), grey clustering (Heung & Hu, 2013, 2014), fuzzy mathematics (Ju & Yoo, 2014; Liou & Lo, 2005; Peche & Esther, 2012) and matter element analysis (Chen, Wu, Blanckaert, Ma, & Huang, 2012) These methods are based on the field data monitored and collected traditionally at limited locations. Numerical models play an important role in decision support for the management of surface water systems They can simulate and predict the overall water quality of water body. They are superior to the traditional field monitoring methods

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