Assessing land elevation in the Ayeyarwady Delta (Myanmar) and its relevance for studying sea level rise and delta flooding

Abstract

Abstract. 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 is 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 ten 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 and FABDEM, perform comparably well, showing the highest correspondence in comparison with AD-DEM and low elevation spot heights, the FABDEM outperforms the CoastalDEM v2.1 by the absence of remote sensing artefacts. The AD-DEM provides a high accuracy, open source and freely available, 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 latest 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 risk mitigation and adaptation strategies while also more inland delta population should be made aware to face a higher risk of flooding due to relative sea level rise in the next ~100 years.

Data, knowledge, and modeling challenges for science-informed management of river deltas

“…Such a difference has obviously major consequences when assessing what is at risk for different levels of rSLR. A recent study on the Ayeyarwady delta revealed similar large discrepancies between different global DEM and local control data 90 . On a global scale, attempts are being made to improve existing global DEMs for the coastal zones, for example using machine-learning 91,92 or interpolating space-borne LiDAR data (I.e.…”

“…ICEsat 2) for the coastal zone 93 . Such products allow improved modeling of coastal and delta products than "raw" DEMs but they should be validated using local groundtruth data, as large discrepancies may still occur 89,90 , and need to be updated periodically to reflect latest developments in land subsidence 28 . Discovering opportunities to curb rSLR will not only rely on better data, but also on new types of numerical modeling approaches.…”

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

“…The related data was obtained via the freely accessible global flood database from Tellman et al (2021). To consider RSLR in the hazard assessment, we used a RSLR scenario of 1 m to map areas falling below sea level based on a locally improved FABDEM from Seeger et al (2023b). The exposure to multiple flooding was assessed through two scenarios: The short-term scenario included monsoon flood frequency and storm surge flooding while the mid-to long-term scenario also integrated the impact of RSLR.…”

Rules of River Avulsion Change Downstream

“…FABDEM was created with a three-step algorithm to remove signals of forests and buildings from the 30-m Copernicus DEM (COPDEM30) 20 . FABDEM outperformed other global DEMs in deltaic flood models due to its better estimation of delta surface heights and a lack of striping or other artifacts 36,37 . Crucially, FABDEM was originally validated using ICESat-2 ATL08 data, showing good alignment with a mean error of -0.03 m and an error percentage < 2m of 80.42%.…”