Decision Support for Flood Event Prediction and Monitoring Darka Mioc, Gengsheng Liang Department of Geodesy and Geomatics Engineering University of New Brunswick Fredericton, Canada dmioc@unb.ca , c1g68@unb.ca François Anton Department of Informatics and Mathematical Modelling Technical University of Denmark Lyngby, Denmark fa@imm.dtu.dk Bradford George Nickerson Faculty of Computer Science University of New Brunswick Fredericton, Canada bgn@unb.ca Abstract—In this paper the development of Web GIS based decision support system for flood events is presented. To improve flood prediction we developed the decision support system for flood prediction and monitoring that integrates hydrological modelling and CARIS GIS. We present the methodology for data integration, floodplain delineation, and online map interfaces. Our Web-based GIS model can dynamically display observed and predicted flood extents for decision makers and the general public. The users can access Web-based GIS that models current flood events and displays satellite imagery and digital elevation model integrated with flood plain area. The system can show how the flooding prediction based on the output from hydrological modeling for the next 48 hours along the lower Saint John River Valley. Keywords-flood modelling; Web GIS; floodplain delineation; I. INTRODUCTION Floods are common natural disasters in the world. Each year they result in much damage to people’s life and properties. In spring 1973, the lower Saint John River in the Fredericton area (New Brunswick, Canada) experienced its worst ever recorded flooding, resulting in economic losses of $31.9 million, and leaving one person dead [1]. At the peak of the flood, private houses and public churches were flooded, and roads and bridges were damaged (see Fig. 1). Figure 1. The impact of flooding in Fredericton, New Brunswick in 1973. Since 1973, other floods have left another three people dead and caused more than $68.9 million in damage. The Saint John River Forecast System operated by the Department of Environment Hydrology Centre is monitoring and predicting flood events along the Saint John River. The Hydrology Centre team uses hydrologic modeling software to predict water levels for the next 48 hours along the lower Saint John River Valley by inputting climate data, weather forecast data, snow data, and flow data. However, the predicted water levels provided by this system cannot satisfy the requirements of the decision support system for flood events. They neither directly display the areas affected by flooding, nor show the difference between two flood events. Based on the water levels, it is hard for users to directly determine which houses, roads, and structures will be affected by the predicted flooding. To deal with this problem, it is necessary to interface the output from hydrological modeling to a Geographic Information System (GIS). GIS has powerful tools that will allow the predicted flood elevations to be displayed as a map showing the extent of the flood inundation. After the interface for the visualization of the impact of flood events is designed, a computerised system is developed that predicts the extent of floods and dynamically display near-real-time flood information for decision makers and the general public. To improve flood prediction for Saint John River, we developed the Web GIS based decision support system for flood prediction and monitoring. In this paper we present the methods for data integration, floodplain delineation, and online map interface. Our Web-based GIS model can dynamically display observed and predicted flood extent for decision makers and the general public. II. STUDY AREA The Saint John River lies in a broad arc across south eastern Quebec, northern Maine and western New Brunswick. It extends from a point on the international boundary to the Bay of Fundy. It drains a total watershed area of 54 600 km 2 . The river is about 700 km long, and the total fall from its headwaters to the city of Saint John is about 482 m. The slope of river gradually decreases from about 1.5 metres per