Title

River Environmental Decision Support System Development For Suzhou Creek In Shanghai

Keywords

Data mart, hydrodynamic and water quality models; Geographic Information Systems (GIS); River Environmental Decision Support System (REDSS); Suzhou Creek

Abstract

The Suzhou Creek Rehabilitation Project (SCRP) is one of the largest water-related environmental rehabilitation schemes ever undertaken in the vicinity of Shanghai, China. This paper details the development and application of a River Environmental Decision Support System (REDSS) for scientific planning and decision-making on the Suzhou Creek project, and illustrates the flexibility of the REDSS framework. We developed the following components: (1) a GIS-based analysis employing Component technology; (2) a " data mart" for multi-dimensional, multi-level, integrated, dynamic, and flexible data querying; and (3) a set of hydrodynamic and water quality models which can simulate complex tidal river networks. In addition, we detail how a water quality assessment model is embedded into the REDSS by employing an Identification Index Method. With the REDSS, all GIS and non-GIS components are integrated seamlessly and data from different sources can be queried simultaneously. This allows for various scenarios to be simulated and analyzed in advance to predict and assess the effects of proposed engineering and management measures. Generated information can thus support effective decisions. All operations of the REDSS can be implemented conveniently through user-friendly interfaces. The function of the REDSS framework is demonstrated through an application to Suzhou Creek. Because the REDSS characteristics are quite general, it may be applied in different geographic regions. © 2011 Elsevier Ltd.

Publication Date

9-1-2011

Publication Title

Journal of Environmental Management

Volume

92

Issue

9

Number of Pages

2211-2221

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.jenvman.2011.04.006

Socpus ID

79957941947 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79957941947

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