**1. Introduction**

Preparing a contingency plan before disasters is essential to increase the capacity of personnel in charge of disaster response and enhance local resilience to disasters. The Sendai Framework for Disaster Risk Reduction 2015–2030 [1], adopted at the Third United Nations World Conference on Disaster Risk Reduction in 2015, addresses the importance of "Enhancing disaster preparedness for effective response and to 'Build Back Better' in recovery, rehabilitation and reconstruction" as the fourth priority action. More specifically, its paragraph 33 states that national and local governments shall prepare or review and periodically update disaster preparedness and contingency policies, plans and programmes with the involvement of the relevant institutions, considering climate change scenarios and their impact on disaster risk and facilitating, as appropriate, the participation of all sectors and relevant stakeholders [1].

In order to achieve effective disaster response, it is important first to assume possible disasters, then quantify expected disaster damage and conduct contingency planning based on the scenarios of the possible disasters. One of the practical tools to carry out this process is evidence-based flood contingency planning, which is based on scientific approaches such as flood simulation and quantitative risk assessment. This planning method, however, is not always feasible to disaster-prone areas in Asia due to the lack of data on natural and social conditions. To overcome such a challenge, the International Centre for Water Hazard and Risk Management (ICHARM) focuses on flood disasters and proposes an effective method for local communities to predict the dynamic change of inundation using flood simulation, assess flood risk with key indicators, decide coping strategies against the identified flood risk and develop a contingency plan beforehand. This method is first applied to one of the flood-prone areas in Asia, Calumpit Municipality in the Pampanga River basin of the Philippines, to verify its effectiveness in areas where the availability of natural and socio-economic data is limited.
