Improvement of Flood Risk Model Performance by Incorporating Sediment Factors

Abstract

Timely flood hazard assessment is essential for determining flood risk levels and plays a critical role in flood prevention and mitigation, particularly in extreme rainfall events under global climate change. This study developed a flood risk model (FRM) incorporating sediment deposition as a key factor to address a gap often overlooked by traditional models for accurately analyzing flood risk distribution. Near-real-time FengYun satellite products were adopted in this model to investigate the causes and impacts of the catastrophic flood in Pakistan in 2022. The results show that the FRM is capable of accurately capturing the spatial distribution of flood risk compared to traditional models, with 98.1% of observed flood extent located within high-risk zones. The floods were mainly attributed to historically unprecedented monsoon rainfall in August—which totaled 2.2 times the 1961–2021 average, and 7.9 times in central Pakistan—while glacier melt contributed minimally. Meanwhile, sediment deposition significantly amplified the magnitude and severity of the flood. The model estimated an inundation of 5.0 × 104 km2, and an affected population of 28 million, aligning closely with field survey data. The traditional model without incorporating sediment thickness as an input parameter underestimates flood hazard risk evaluation by up to 23.3%, especially as intensification of extreme rainfall drives higher erosivity and increased suspended sediment concentrations in the future. Therefore, the sediment factors adopted in flood risk assessment would provide an effective framework for accurate flood hazard risk identification and support decision-making aimed at enhancing flood disaster prevention and mitigation.