Regularly acquired, weather-independent synthetic aperture radar (SAR) observations such as those from the ESA's Sentinel-1 [5] and the upcoming NASA-ISRO SAR (NISAR) [6] missions have the potential to meet this critical data need. Previous case studies have identified SAR as a useful dataset for mapping floods and other weather-related hazards [7; 8; 9; 10; 11], yet novel data processing approaches are still needed to cope with the large data volumes associated with SAR and enable the automatic generation of hazard information at a low latency and across large spatial scales. The increasing availability and reduced latency of SAR observations has generated growing interest in the development of such automatic flood extent monitoring algorithms. The German Aerospace Center's (DLR) Center for Satellite-Based Crisis Information (ZKI) developed a thresholding algorithm to facilitate event-based flood water extent monitoring from Sentinel-1 and TerraSAR-X SAR data [7; 12]. Similar thresholding techniques were also developed by a variety of other teams [4; 13; 14] to provide flood extent information for individual events in China, Bangladesh, and the Mekong Delta. These papers show that thresholding techniques are applicable to areas with vegetation properties similar to the HKH. A recent work published in [15] developed an automatic system for flood extent and flood duration mapping using multi-sensor satellite observations. This system is based on four processing chains for the derivation of flood extents from Sentinel-1 and TerraSAR-X radar as well as from optical Sentinel-2 and Landsat satellite data. A literature search more specifically focused on SAR-based flood mapping in the HKH found a growing number of publications which applied SAR to map individual events or perform retrospective flood assessments. Most of these papers relied on Sentinel-1 data as a resource and use either thresholding, change detection, or clustering methods to identify open water. The authors of [16] applied the automatic, bimodal Otsu thresholding approach with Sentinel-1 SAR data to analyze a 2020 flooding event in northern India. Thresholding was also used in a paper focused on the 2019 flood season in Bangladesh [17]. The authors used Landsat-8 images to manually select a threshold which separated water from non-water pixels in Sentinel-1 SAR scenes. A paper focused on the 2020 Bangladesh monsoon season used an unsupervised clustering algorithm to identify open water pixels in Sentinel-1 images, resulting in pre-flood and mid-flood water extent maps of the country [18]. While their approach yielded good results, many processing steps remained manually performed, preventing more automatic, large-scale application of the algorithm. Change detection between pre- and mid-flood Sentinel-1 image pairs was used in [19] as part of a multi-sensor retrospective analysis of the maximum flood extents in recent Bangladesh monsoon seasons. Their approach calculated the differences between pre- and mid-flood Sentinel-1 images and then applied the change detection and thresholding (CDAT) approach [8] to identify flood water pixels. A similar change detection approach was also used in [20] to study flooding in the Ganga-Brahmaputra basin. Both of these papers focused on case studies and were not aimed at continuous, large-scale monitoring. In addition to these papers on flood extent mapping, there is also a growing body of work related to flood depth estimation from SAR remote sensing data. Most papers on this topic use a workflow where the water extent is mapped first, followed by a depth estimation step which is based on intersecting the identified water bodies with a digital elevation model (DEM). In [21], the lowest elevation within an identified water-covered area was used as a reference to scale the topographic height to the water depth. The auhtors of [22] used the approach in [23], where the boundary of a flooded area was first extracted and intersected with the DEM.