Surface Water Inventory and Monitoring (SWIM): Hands-on Examples for Improved Flood Mapping and Water Resource Monitoring

Kurzfassung

Over the last years, DLR has established automated workflows to derive the extent of open surface water bodies from various Earth Observation (EO) datasets. This workflows have consistently demonstrated their value in supporting flood mapping and water monitoring activities. In order to maximize the potential of these methods, we are developing an open access surface water product at 10-20 m spatial resolution based on Sentinel-1/2 data. The primary objective is to provide easy access to global high-resolution surface water information and offer interfaces for seamless integration into automated rapid mapping and monitoring workflows to support the disaster management and water monitoring community. The proposed SWIM product contains two publicly available data collections: 1) Water Extent (SWIM-WE) provides a dynamically updated set of binary masks identifying open surface water bodies extracted from both Sentinel-1/2 scenes. This data collection enables users to rapidly identify the water extent on a specific date or to analyze surface water dynamics over time covering large areas. 2) Reference Water (SWIM-RW) contains fused information on permanent and seasonal water bodies based on the SWIM-WE collection over an observation period of two years. This collection was designed to support disaster response workflows by providing pre-computed information on the "normal" hydrologic conditions to accelerate the identification of flooded areas. By considering seasonal water dynamics, potential overestimation of inundation extent can be limited. Users are also able to access additional assets containing the relative water frequency as well as quality layers. The described product will be available via OGC-Webservices on DLR GeoService. All published data assets can be accessed from spatio-temporal asset catalogs (STAC). We choose this technology as it allows for quick data search by filtering user-specific areas and time-frames. Matching water masks can also be projected and mosaicked efficiently using open-source tools such as odc-stac. In combination with the publication of the SWIM product, a set of Jupyter Notebooks that demonstrate common use cases will be made available. For the visualization of SWIM data in GIS software or web mapping applications, Web Map Services (WMS) will be hosted. In this conference contribution, we aim to introduce the SWIM product to a broader audience and provide hands-on examples on how the data can be used for effective flood disaster response and long-term water resource monitoring. To showcase a typical rapid mapping workflow using SWIM, the visualization of observed water extent at a specific date and location using the SWIM-WE WMS service is demonstrated on the example of the 2024 flood event in Southern Germany. Additionally, the automated identification of flooded areas using the SWIM-RW reference water mask is shown. The importance of considering seasonality in reference water layers is demonstrated by comparing inundation masks from real flood events in Germany and India. To highlight potential use-cases of the SWIM product apart from flood monitoring, time-series analysis of SWIM-WE items of water reservoirs in Germany is conducted to showcase the analysis of hydrologic drought conditions