## 5 Conclusions  
Traditional flood risk analysis solely focusing on high-risk areas may no longer be applicable as more extreme weather events are causing unanticipated floods in unprepared regions, creating flood management uncertainty. Relying on data from historical events or simply expanding the prevention scope based on future estimations cannot effectively address the problem. Therefore, in this study, we bridged, for the first time, past disaster experience-derived data (including historical flood inundations, especially extremes) and future information to develop a scenario-based data-driven risk analysis framework. The framework composed of flood inundation-prone and high-risk zones provides additional risk perception perspectives, which encourages the assessment of as many potential hazards as possible to tackle uncertainty. The whole framework is applicable to other regional flood-prone basins or catchments of large research scale, and this new flood response concept for risk awareness is also applicable for small-scale urban flood management. The hierarchical management panel derived from the framework further highlights the priorities and emphases of risk mitigation measures for various societal objectives, such as authority-, industry-, and community-led actions, improving the robustness of decision making. The illustrative case of the PLEEZ exposed a lack of risk awareness in the face of unforeseen adversities, as indicated by the vast areas identified as Rank 2 (3395 km\({}^{2}\)) and 3 (3238 km\({}^{2}\)) by our framework for catastrophic flooding in 2020, resulting in inadequate preventive measures and ultimately great damage and loss. Future scenarios reveal that in the PLEEZ, in the SSP2-RCP4.5 scenario, the high-risk area would increase significantly by 19.3% around 2040. Extreme rainfall intensity would be the main cause of future flood risk increase (with an average contribution of 78.1%), and a dramatic shift in land-use type, such as wetland and unused land being reclaimed for cultivation, would be the key factor inducing a sharp risk increase (with an average contribution of 71.9%), providing decision support for local risk response and resource management. In this work, multiple factors were considered in flood risk evaluation and management based on the CMIP5 dataset. However, to give more emphasis to the role of environmental (e.g., land use/cover) and social (e.g., demographic or economic) changes, the most recent CMIP6 global climate models, which considers both climate change and socioeconomic development, could be used in future evaluations, also for increasing the research applicability scope. Global warming poses the challenge of making regional floods unpredictable in both intensity and impact extent. The integration of historical and future multi-scenario information derived from satellite-based inundation and other multi-source Earth projection data promises the likelihood of being able to proactively address climate disturbance. Our framework inspires an innovative perspective of risk assessment (i.e., the combination and transformation of flood inundation prone and high risk). This new framework expands risk perception and facilitates a better understanding and response to flood risk in different catchments, especially in the case of limited disaster mitigation resources and in the face of increasing extreme flood events. The framework highlights the value of remote sensing observation data in traditional risk assessment frames and advocates for data-driven management to tackle uncertainty in increasingly unstable flood-prone basins.