Development of a GIS-based integrated framework for coastal seiches monitoring and forecasting: A North Jiangsu shoal case study. (June 2017)

• Record Type: Journal Article
• Authors: Qin, Rufu; Lin, Liangzhao
• Is Part Of: Computers & geosciences. Volume 103(2017)
• Page Start: 70
• Page End: 79
• Publication Date: 2017-06
• Subjects: Coastal seiches -- GIS -- Visualization -- Web-based platform -- In situ observations -- Model predictions; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2017.03.010
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Coastal seiches have become an increasingly important issue in coastal science and present many challenges, particularly when attempting to provide warning services. This paper presents the methodologies, techniques and integrated services adopted for the design and implementation of a Seiches Monitoring and Forecasting Integration Framework (SMAF-IF). The SMAF-IF is an integrated system with different types of sensors and numerical models and incorporates the Geographic Information System (GIS) and web techniques, which focuses on coastal seiche events detection and early warning in the North Jiangsu shoal, China. The in situ sensors perform automatic and continuous monitoring of the marine environment status and the numerical models provide the meteorological and physical oceanographic parameter estimates. A model outputs processing software was developed in C# language using ArcGIS Engine functions, which provides the capabilities of automatically generating visualization maps and warning information. Leveraging the ArcGIS Flex API and ASP.NET web services, a web based GIS framework was designed to facilitate quasi real-time data access, interactive visualization and analysis, and provision of early warning services for end users. The integrated framework proposed in this study enables decision-makers and the publics to quickly response to emergency coastal seiche events and allows an easy adaptation to other regional and scientific domains related to real-time monitoring and forecasting. Highlights: An integrated framework for coastal seiches monitoring and forecasting was presented. A GIS-based tool for automatically processing model outputs was developed. A WebGIS application for data access, visualization and analysis was developed. The integrated framework allows an easy adaptation to other domains.