Flood inundation assessment in the data-scarce region using hydrodynamic models and google earth engine -A case of Ghed region, Ozat River basin, Gujarat, India

Abstract

Abstract Flooding is an inevitable phenomenon of nature; however, its effect can be reduced via flood assessment. Therefore, flood inundation mapping is vital for flood assessment and mitigation planning in developing countries. But, flood assessment needs massive data sets to perform the flood simulation. Hence, the availability of precious observed data for flood assessment plays a significant role in research methodology to overcome the limitation and barriers for efficient modeling. The present study aims to evaluate the inundated area of Ghed region using 2-dimensional (2D) hydrodynamic analysis. The new HEC-RAS v6 uses an open-source digital elevation model (DEM) for hydraulic analysis to develop flood inundation, velocity, depth, arrival time, and percentage time inundation maps. The results are validated with 2017 and 2021 satellite images, hence the machine-learning algorithm generated in the Google Earth Engine (GEE) cloud platform to visualize the flooded area. In GEE, a flood mapping algorithm (FMA) generates data from sentinel 1-C band synthetic-aperture radar (SAR) sensors and compares it to the 2D model's output. The observed data sets are used to validate the hydrodynamic models for calibration of Manning roughness value in the case of a 1D model and water depth study for a 2D model. In this context, regression analysis was employed to validate water surface elevation, and four key locations were compared for maximum water depth. It has been determined that more than 170 km2 of land has been flooded yearly. The satellite image examination identifies frequently flooded areas via derivation of post-flood scenarios in GEE. The findings of this research aid decision-makers in developing an early warning system and establishing new hydraulic structures.