Abstract
<jats:p>Rapid urbanization, aging drainage infrastructure, and increasingly heavy rainstorm events have made urban pluvial flooding a major problem for modern cities. Urban planning, risk assessment, and mitigation strategy design all depend on accurate modeling of these types of floods. However, the quality of the input data, especially the topographic information controlling overland flow and the depiction of drainage networks, has a significant impact on the model's effectiveness. Despite their acknowledged significance, these kinds of input data are frequently chosen based more on availability than on a methodical assessment, which could result in inaccurate flood forecasts.This study examines how preprocessing and input data quality affect urban pluvial flood simulations, with a focus on 1D–2D coupled modeling. The interactions between subterranean drainage systems and overland runoff are explicitly represented using an integrated framework that combines SWMM for one-dimensional (1D) drainage dynamics and IBER for two-dimensional (2D) surface flow. The study develops a fully coupled 1D–2D IBER–SWMM framework to overcome the drawbacks of purely 2D simulations, allowing dynamic and bidirectional water exchange between the surface and the drainage network. Municipal datasets were used to obtain inlet locations and typologies.The 1.43 km² study area is a heavily populated part of the Sampierdarena district that is regularly impacted by pluvial floods brought on by rainfall events with very short return periods (3 years).All things considered, this study highlights the crucial significance of methodical terrain data selection and preprocessing in 1D–2D coupled urban flood models. The fundamental methodological development that improves the physical consistency of surface–subsurface interactions and boosts predictive reliability is the merging of IBER and SWMM. The study offers a structured framework for more transparent and reliable urban flood simulations by clearly quantifying the impact of topographic data attributes on hydrodynamic outcomes. In light of growing hydrological extremes, our contribution promotes robust and data-driven urban flood risk management, supporting both scientific advancement and practical decision-making.</jats:p>