Author(s): Shie-Yui Liong; Dong Eon Kim; Jiandong Liu; Srivatsan Vijayaraghavan; Ngoc Son Nguyen; Jina Hur
Linked Author(s):
Keywords: Artificial neural network; Climate change; Downscaling; Remote sensing; Flood hazard
Abstract: Many urban cities in Southeast Asia witness severe flooding associated to increasing rainfall intensity and rapid urbanization often with poor urban planning. Two important inputs required in flood hazard assessment are: (1) high accuracy Digital Elevation Model (DEM), and (2) long rainfall record. High accuracy DEM is expensive and time consuming to acquire. Long rainfall records for areas of interest are often not available due to rapid urbanization. This study presents a notably cost-effective and efficient approach to derive high accuracy DEM and suggests to consider proxies for long rainfall data. To derive a high accuracy DEM, an approach with Artificial Neural Network (ANN) considers data from two freely accessible satellite data: Shuttle Radar Topography Mission (SRTM) and Sentinel-2 multispectral imagery. The study shows a significant DEM improvement; this DEM is then used in the flood simulations to generate flood maps useful for flood mitigation measures. Proxies for rainfall data resulting from climate change downscaling are proposed for urban planning and drainage system designs for areas where long rainfall data records are not available. Precipitation outputs from a Regional Climate Model (RCM) Weather Research and Forecasting (WRF) is used to derive the Intensity Duration Frequency (IDF) curves for the study area. Design storms, calculated from the IDF curves with different return periods, are then applied to numerical flood simulations to identify flood prone areas. The approach is demonstrated in a flood hazard study in Yogyakarta, Indonesia.
Year: 2018