Remote Sensing and Gis-Driven Model for Flood Susceptibility Assessment in the Upper Solo River Watershed

Abstract

This study aims to develop an expedited flood susceptibility model with remote sensing data that can be effectively utilized for a large catchment area. We apply the model to the Upper Solo River Watershed in Indonesia. The model incorporates the hydrological attributes of the watershed obtained from remote sensing data, including elevation, slope, flow accumulation, proximity to rivers, rainfall, drainage density, topographic wetness index, land use land cover, normalized difference vegetation index, soil moisture, and land surface curvature. The flood susceptibility criteria are generated using remote sensing datasets such as The Shuttle Radar Topography Mission (SRTM), Sentinel 2 Multispectral Instrument, Global Precipitation Measurement (GPM) v6, and NASA-USDA Enhanced SMAP Global Soil Moisture Data. Through utilizing remote sensing data and GIS analytic tools, this study has discovered that it is possible to create a flood susceptibility model for large catchment regions cost-efficiently. Our study indicates that areas in the surrounding of Surakarta City, the most populated city in this watershed, are the most susceptible. Therefore, the government and community should increase their capacity to cope with this potential disaster.