Leveraging multi-source data for enhanced flood damage modeling with explicit input uncertainty management

Accurate flood damage models are vital for supporting risk management decisions, but they rely heavily on detailed and reliable data on hazard, vulnerability, and exposure. Therefore, limited data availability, accessibility, and completeness can introduce substantial uncertainty into damage estimates. This study introduces INSYDE 2.0, a multi-variable, physically based flood damage model for residential buildings and their contents, capable of handling missing input data uncertainty. Though originally developed and validated for Italy, this framework can be effectively applied to other regions with proper modifications. INSYDE 2.0 includes built-in functions that replace missing values with data sampled from probability distributions designed to account for the hazard and building characteristics of the specific area under investigation. These distributions are based on a combination of official data, numerical simulations, and virtual surveys of households listed on real estate platforms. A key advantage of this probabilistic approach is the possibility of explicitly accounting for uncertainty in computed damage, offering more informative estimations for decision-makers. This contrasts with deterministic models, which can sometimes provide a false sense of precision by delivering a single, definitive output.