An automated approach to estimate large-scale flood volumes based on SAR satellite imagery and different DEMs - a risk management support

Abstract

Flood volume estimates provide valuable information for flood risk management applications. Up to now, quick volume estimates for large-scale areas are rare as hydraulic models require complex parameterization and GRACE (Gravity Recovery and Climate Experiment) gravity measurements deliver estimates on basin-scale. A new approach is taken to develop a novel flood volume estimation method for large-scale areas based on globally available and open access earth observation data. For this approach, an automatically derived water mask from SAR imagery is combined with a digital elevation model to estimate water levels for areas of horizontal water surface. A slope-dependent tiling algorithm and a threshold for flood and non-flood pixel separation allow accurate calculation of water levels. Model performance is assessed by in situ gauge and altimeter measurements for two study sites along the rivers Mekong and Brahmaputra. The results show consistency with validation data and support the transferability potential of the proposed algorithm. With regard to risk management this simple, quick and globally applicable model demonstrates major advantages compared to common approaches and allows new implementations of large-scale flood volume information in time-critical situations.