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This folder includes datasets that were produced to assess flood hazards and exposure in the Ayeyarwady Delta in Myanmar by applying the new standardised, integrative approach of Seeger, K., Peffeköver, A., Minderhoud, P. S. J., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2024): Evaluating flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar). Environmental Research Letters. The README includes the names of files to be used for citation as well as a brief explanation when necessary. All processing details are given in the paper and related supplementary material.

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.

The local digital elevation model (DEM) of the Ayeyarwady Delta, referred to as AD-DEM, was generated based on elevation data of topographic maps at scale of 1:50,000 published in 2014 while source data was compiled between 2000 and 2004. Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate ~5100 elevation points (spot heights) and ~13600 elevation points extracted from contour data of the topographic maps. Elevation values higher than 10 m were excluded from interpolation and the SRTM water body mask created in 2000 was applied to the processed AD-DEM. The AD-DEM was transformed from its original vertical reference of local mean sea level at Kyaikkhami tide gauge to continuous mean sea level based on the mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; https://doi.org/10.5194/os-17-789-2021) that we transposed to EGM96) in order to account for sea level variations along the Myanmar coast. The AD-DEM contains itself some uncertainty due to the lack of evenly distributed spot heights in areas of the upper delta, for which a separate shapefile is provided. However, we highlight to consider the AD-DEM as being the currently best available model against the background of the lacking possibility of ground truthing and being independent from satellite-based measurements. For further information on data processing, including DEM interpolation, determination of local mean sea level and vertical datum conversions, as well as DEM performance, see the corresponding paper and supplementary material. File name: ADDEM_Con250m_lesseq10_MDT_AD_MMR2000_masked_maskedSRTM.tif File format: GEOTIFF file Spatial reference: MMR2000_46N Vertical reference: local continuous mean sea level, i.e., mean dynamic topography (CNES-CLS18 dataset of Mulet et al. (2021; https://doi.org/10.5194/os-17-789-2021) transposed to EGM96 Cell size: 750 × 750 m File name: DataPoorAreas_MMR2000.shp File format: ESRI Shapefile

This digital elevation model is a version of the FABDEM V1-0 of Hawker et al. (2022; https://doi.org/10.1088/1748-9326/ac4d4f) that was adjusted for the Ayeyarwady Delta in Myanmar by local spot height data from topographic maps (scale 1:50,000) published in 2014 while source data was compiled between 2000 and 2004. The FABDEM V1-0 (Laurence Hawker, Jeffrey Neal (2021): FABDEM V1-0. https://doi.org/10.5523/bris.25wfy0f9ukoge2gs7a5mqpq2j7; CC BY-NC-SA 4.0) was projected to the Myanmar 2000 datum and clipped to the Ayeyarwady Delta region of interest. The vertical reference of the FABDEM V1-0 was transformed to EGM96 before applying a conversion to continuous mean sea level based on mean dynamic topography data (CNES-CLS18 dataset of Mulet et al. (2021; https://doi.org/10.5194/os-17-789-2021) that we transposed to EGM96). Subsequently, inland water bodies were masked using the water body mask of the Copernicus DEM (Airbus Defence and Space, 2020: Copernicus Digital Elevation Model Product Handbook Version 3.0, Airbus, 38 pp.) and cell values with an elevation of more than 7 m below mean sea level were removed. From the topographic maps, the local spot heights outside of areas masked in the AD-DEM (Seeger et al. (2023): Local digital elevation model for the Ayeyarwady Delta in Myanmar (AD-DEM) derived from digitised spot and contour heights of topographic maps. Doi; CC-BY 4.0) were subtracted from elevation values of the FABDEM V1-0 at the same locations (~3630 spot heights). Empirical Bayesian Kriging with empirical data transformation and exponential modelling was applied to interpolate the height residuals and export the raster data at ~30 m grid cell resolution. The mask layer of the AD-DEM was applied to the height residual raster in order to correct for interpolations in areas of data paucity. Subsequently, the interpolated height residuals were subtracted from the pre-processed FABDEM. In delta areas outside the masked regions of the height residual raster, the

With their low lying, flat topography, river deltas and coastal plains are extremely prone to relative sea level rise and other water-related hazards. This calls for accurate elevation data for flood risk assessments, especially in the densely populated Southeast Asian deltas. However, in data-poor countries such as Myanmar, where high accuracy elevation data are not accessible, often only global satellite-based digital elevation models (DEMs), suffering from low vertical accuracy and remote sensing artefacts, can be used by the public and scientific community. As the lack of accurate elevation data hampers the assessment of flood risk, studying available information on land elevation and its reliability is essential, particularly in the context of sea level rise impact. Here, we assess the performance of 10 global DEMs in the Ayeyarwady Delta (Myanmar) against the new, local, so-called AD-DEM, which was generated based on topographical map elevation data. To enable comparison, all DEMs were converted to a common vertical datum tied to local sea level. While both CoastalDEM v2.1 (Kulp and Strauss, 2021) and FABDEM (Hawker et al., 2022) perform comparably well, showing the highest correspondence in comparison with AD-DEM and low-elevation spot heights, FABDEM outperforms CoastalDEM v2.1 by the absence of remote sensing artefacts. The AD-DEM provides a high-accuracy, open and freely available, and independent elevation dataset suitable for evaluating land elevation data in the Ayeyarwady Delta and studying topography and flood risk at large scale, while small-scale investigations may benefit from a FABDEM locally improved with data from the AD-DEM.Based on the latest Intergovernmental Panel on Climate Change (IPCC) projections of sea level rise, the consequences of DEM selection for assessing the impact of sea level rise in the Ayeyarwady Delta are shown. We highlight the need for addressing particularly low-lying populated areas within the most seaward districts with