The Characteristics of an Integrated Flood Warning and Response System that can Facilitate Evidence-Based Decision-Making: A Case Study in Sri Lanka

Samansiri, Rankotge

Abstract

Flood is a frequently occurring hazard that imposes adverse effects on a significant number of human lives and causes substantial economic damage worldwide. Flood frequency and impact have increased drastically due to climate change issues and unplanned development in the recent past. It is observed that the number of victims due to floods is rising, hence, flood early warning and response systems (FEWRS) are very crucial in developing risk reduction strategies. Studies show that effective forecasting, warning, and response systems based on accurate real-time intelligence on disasters can reduce up to 35% of the average annual flood damage and by deploying a proper early warning and response system can reduce vulnerability and mortality rates. Even though an effective flood early warning and response system (FEWRS) is considered an essential tool for effective flood risk management and emergency response, no single operational solution that is applicable universally has been developed and implemented. There is a considerable gap in implementing a successful warning and response system due to a lack of policies, sound technological solutions and community engagement.

This research aims to investigate the characteristics of a system architecture that can be used to develop an effective flood warning and response system (FEWRS) which can offer timely intelligence to decision-makers. In addition, the research also aims to investigate additional social, institutional and governance issues that need to be addressed to extract the benefits of such an architecture. The Design Science Research (DSR) approach was used in this research to investigate the problem, capture user requirements, conduct artefact design and validation. Initially, the study conducted a structured literature survey to investigate the intelligence required for flood warning and response processes and the technology solutions that can offer such intelligence. Twenty-seven types of intelligence were identified, together with the technologies that can be used to extract such intelligence. Building on this literature findings, experts from government organisations, civil society organisations and community representatives, who are engaged with the flood warning and response activities in the Sri Lankan context, were interviewed to identify the characteristics of a system architecture for an effective FEWRS. These requirements were captured under multiple views such as process view, data view, technology view, stakeholder view, interface view, and usage scenario view. These views were used to define a modular architecture, loosely coupled with different components to enable an end-to-end, people-centric warning system. Although Sri Lanka was used as the basis for analysing the user requirements, the architecture was then generalised for other similar country contexts.

In addition, the study conducted a structured literature survey to investigate the possible failure factors of FEWRS, which need to be addressed to implement the proposed architecture successfully. The result shows twenty-four critical failure factors (CFF) that impact the success of implementing a successful flood warning and response process. Therefore, the study proposes several recommendations and guidelines for successfully addressing limitations in institutional leadership, multi-agency collaboration, data governance, community engagement and lack of funding to implement the proposed architecture within a multi-agency context.

Keywords: Flood warning and response system, Situational Intelligence, Climate Change, Disaster Risk Reduction, Enterprise Architecture

Citation

Samansiri, R. (2024). The Characteristics of an Integrated Flood Warning and Response System that can Facilitate Evidence-Based Decision-Making: A Case Study in Sri Lanka. (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Jan 24, 2024
• Publicly Available Date: Feb 27, 2024
• Award Date: Jan 26, 2024