Evaluating flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar)

• Published in: Environmental research letters Vol. 19; no. 8; pp. 84007 - 84020
• Main Authors: Seeger, Katharina, Peffeköver, Andreas, Minderhoud, Philip S J, Vogel, Anissa, Brückner, Helmut, Kraas, Frauke, Oo, Nay Win, Brill, Dominik
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.08.2024
• Subjects: Accessibility; coastal lowlands; Datasets; Decision making; Deltas; Economic development; Evaluation; Flood hazards; Flooding; Floods; Food security; Hazard assessment; Land use; Lowlands; monsoon; Precipitation; Regional development; relative sea-level rise; river deltas; River discharge; River flow; Rivers; Satellite imagery; storm surge; Storm surges; Water discharge; Workflow; Myanmar (Burma)
• ISSN: 1748-9326; 1748-9326
• DOI: 10.1088/1748-9326/ad5b07

Abstract Coastal lowlands and river deltas worldwide are increasingly exposed to coastal, pluvial and fluvial flooding as well as relative sea-level rise (RSLR). However, information about both single and multiple flood-type hazards, their potential impact and the characteristics of areas, population and assets at risk is often still limited as high-quality data either does not exist or is not accessible. This often constitutes a main barrier for generating sound assessments, especially for scientific and public communities in the so-called Global South. We provide a standardised, integrative approach for the first-order assessment of these single and multiple flood-type hazards and show how this can be conducted for data-sparse, hardly accessible and inaccessible coastal lowlands such as the Ayeyarwady Delta in Myanmar by using only open accessible and freely available datasets of satellite imagery, global precipitation estimates, satellite-based river discharge measurements, elevation, land use, and population data. More than 70% of the delta, mainly used for agriculture, and about 40% of its present population are prone to flooding due to either monsoon precipitation and runoff, storm surge, and RSLR, or their combination, jeopardising food security and economic development in the region. The approach allows for the integration and combination of various datasets, combined in a highly flexible workflow that performs at low computational capacities, supporting the evaluation of flood-prone areas on regional and local scale for data-sparse coastal lowlands worldwide. It thereby allows to attribute different types of flood hazards, complements concepts of vulnerability and risk, and supports risk-informed decision making and development of effective multi-flooding adaptation strategies.