A flood risk assessment based on an OpenStreetMap application: a case study in Manmunai North Divisional Secretariat of Batticaloa, Sri Lanka

Abstract

In the recent past, Sri Lanka has been experiencing an increase of intensity and frequency of natural disasters. Therefore, the study was carried out to introduce an Open Source application to collect the field level information and to identify the flood inundation areas through a 3D model. The case study area included 48 Grama Niladhari Divisions in Manmunai North Divisional Secretariat (DS), Batticaloa District, Sri Lanka. The study helped to analyze the role of OpenStreetMap (OSM) to support the mapping of the flood risk level of the study area. This objective was achieved by collecting flood exposure data through community participatory method using OSM, which was integrated into a Digital Elevation Model (DEM). Elevation points were collected using Google Earth and TCX Converter. The flood hazard maps were created using inputs such as water depth and flood extent of the DEM and verified through a local community participatory mapping exercise. Next, vulnerability maps were generated based on factors such as building characteristics of houses, population of the areas and the availability of assistance during the flood scenarios in 2010 and 2011. Finally, the flood risk map of the study area was prepared in combination with hazard and vulnerability maps. The study produced a user-friendly application of open source and GIS to develop a 3D flood risk model for the identification of flood risk levels. Exposure data have been uploaded into the OSM, therefore, it can be accessed anytime, anywhere and by anyone. The extent of study area is 2593 ha; where about 25,000 families live and there are more than 32,000 buildings. The building footprint database was established using JavaOSM and Bing satellite imagery. It was updated with the building attributes produced by the data collection exercise. This study showed that when the water level increases in the lagoon, nearly 25 GN Divisions (GNDs) out of 48 GNDs are under high flood risk. The developed online geospatial database in OpenStreetMap is an important asset, since it supports the preparation of an emergency flood risk management plan, which helps to accelerate the emergency response and flood mitigation plan for the study area.