The paper explores the necessity and feasibility of a decision support system (DSS) for flood emergency management, focusing on the pre-flood, flood, and post-flood phases. It builds upon previous work on modular DSS models, integrating various subroutines into a comprehensive logical model. The authors aim to create a disaster management metamodel to develop a DM language, which will serve as a representational layer for DM data, enabling the system to mix and match DM actions based on the evolving disaster. The DSS is designed to enhance decision-making through accurate and multi-layered information, addressing the challenges of climate change and urban vulnerabilities. The system's structure is divided into three layers: expression, application, and data, each designed to handle different aspects of flood management. The paper also includes a case study using a bushfire disaster to validate the DM metamodel, demonstrating its applicability in specific catastrophic scenarios. Statistical analyses are conducted to assess the dynamic interactions between different subroutines, and the results are used to refine the model. The study concludes that the proposed DSS can significantly improve flood warning, monitoring, damage assessment, and emergency response, providing a robust foundation for flood management.The paper explores the necessity and feasibility of a decision support system (DSS) for flood emergency management, focusing on the pre-flood, flood, and post-flood phases. It builds upon previous work on modular DSS models, integrating various subroutines into a comprehensive logical model. The authors aim to create a disaster management metamodel to develop a DM language, which will serve as a representational layer for DM data, enabling the system to mix and match DM actions based on the evolving disaster. The DSS is designed to enhance decision-making through accurate and multi-layered information, addressing the challenges of climate change and urban vulnerabilities. The system's structure is divided into three layers: expression, application, and data, each designed to handle different aspects of flood management. The paper also includes a case study using a bushfire disaster to validate the DM metamodel, demonstrating its applicability in specific catastrophic scenarios. Statistical analyses are conducted to assess the dynamic interactions between different subroutines, and the results are used to refine the model. The study concludes that the proposed DSS can significantly improve flood warning, monitoring, damage assessment, and emergency response, providing a robust foundation for flood management.