most flood-hit regions in Asia, has witnessed a continuous increase in flood risk in many places [2]. The consensus is that flood frequency will increase in the future, as confirmed by both historical statistics and scenario simulations [3; 4; 5]. In a global-scale study, a clear positive correlation between atmospheric warming and flood risk was shown, with China being projected to face the largest absolute impacts, affecting 40 million people and causing EUR 110 billion in damage per year at 4 \({}^{\circ}\)C warming [6]. Decision-making uncertainty is a critical issue to consider in flood risk assessment and has recently been in the spotlight in terms of, e.g., house elevation-related decisions [7] and green infrastructure design [8]. However, this aspect still receives little attention in traditional flood risk analysis frameworks referring to non-structural measures. Specifically, without the consideration of future scenarios, flood risk analysis built on historical knowledge may not adequately address the coming challenges posed by altered rainfall patterns with more frequent and intense extremes [9]. Conversely, the inherent uncertainty of climate projections and the nonstationarity of future rainfall patterns cast doubt on the reliability of future risk assessments [10; 11; 12; 13], underscoring the importance of historical events. Therefore, the current flood risk response requires more robust assessment methods to tackle the increased decision uncertainty by synthesizing historical records and future risk information. [PERSON] et al. [13] highlighted the importance of incorporating (currently) rare heavy rainfall events in future flood management evaluation, emphasizing the value of flood frequency information, and classified areas with more frequent flooding but small inundation depth as high risk in order to address uncertainty. According to them, future flood risk might also be underestimated if only historical rainfall data are used. Information on inundation frequency becomes an important element that needs to be considered in future flood risk assessment on the basis that flood-prone areas are hotspots for urban development [14], as evidenced by the recent finding that worldwide, development in these areas has greatly outpaced that in zones less likely to be inundated [15]. This means that traditional analysis solely focusing on high-risk areas may be inadequate in identifying risk variation. However, a universal risk analysis framework based on both historical events and future projections to address uncertainty is still lacking today. Hydrodynamic models are the dominant methods for replicating fluid motion and quantifying accurate risk [16]. Despite progress in model accuracy and computational efficiency, physical models for flood risk assessment are still not suitable for very large areas at high resolutions [17; 18]. Moreover, strategies based on physical infrastructure resistance cannot appropriately handle uncertainty and unexcepted change while providing substantial protection [19]. A good alternative is represented by data-driven models, which attempt to identify potential flood areas and risk by defining functional relationships between hydro-morphological variables and the presence or absence of inundations [18]. Hybrid models integrating a data-driven risk framework and hydraulic variables are emerging as a new beneficial approach; for instance, [PERSON] et al. [20; 21] combined machine learning and hydrodynamic modeling for assessing risk and predicting risk development. With the proliferation of open access Earth Observation data over the past decade, significant advancements have been made in the integration of satellite images into flood modeling [22; 23], making them very useful for better flood disaster response and management. The use of time series of satellite imagery is valuable in data-driven flood risk mapping and assessment, especially in data-scarce regions [24; 25].