Analysis of the Influence of Manning's Coefficient in Dam Failure Flood Simulations Using Remote Sensing Data

Abstract

Dam failures pose significant risks to both life and infrastructure, making precise flood simulations essential to mitigate their impacts. This study investigates the influence of Manning's roughness coefficient on dam failure flood simulations, utilizing three freely accessible high-resolution Land Use and Land Cover (LULC) datasets: MapBiomas, ESRI Annual Land Cover, and Dynamic World, all processed via Google Earth Engine. The analysis encompasses two dams located in the semi-arid region of Pernambuco, Brazil, using the HEC-RAS 6.5 model to explore variations in key hydraulic parameters such as peak flow, flood propagation time, and inundated areas. Additionally, the study emphasizes the importance of accurately defining the Self-Rescue Zone (SRZ), a critical emergency response area defined within 30 minutes of flood wave propagation, typically as required by several dam safety legislations. Differences in SRZ dimensions were analysed based on the LULC datasets, revealing significant variations linked to Manning's coefficient. The results highlight the potential of LULC data in failure simulations, especially in data-deprived regions. However, they reinforce the substantial impact of LULC datasets on hydraulic modelling results, which may influence emergency planning.