SWOT for Monitoring Terrestrial Water Storage Changes: Quality Assessment and Combination with other Remote Sensing Data

Principal Investigator: Christian Schwatke (Deutsches Geodätisches Forschungsinstitut, Technical University of Munich, DGFI-TUM​)

Co-Investigator(s): Denise Dettmering, Daniel Scherer

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Monitoring the Earth's water cycle is increasingly critical, especially as climate change continues to impact hydrological systems. Accurately tracking the dynamics of inland water bodies, such as lakes and rivers, requires robust datasets that can measure changes in water levels and surface areas, even in remote areas. Several satellite sensors, such as optical imagery and satellite altimetry, have been used in this effort. While satellite altimetry measures water levels, optical imagery provides information on surface area changes of inland waters. However, existing technologies have limitations, particularly in their ability to simultaneously measure both water levels and surface areas, as well as in the spatial resolution needed to monitor smaller water bodies. The Surface Water and Ocean Topography (SWOT) satellite mission addresses these limitations.

One of the key resources for hydrological applications is the Database of Hydrological Time Series of Inland Waters (DAHITI), managed by DGFI-TUM. DAHITI offers freely accessible hydrological products such as time series data for water levels, surface areas, and volume variations of inland waters, derived from satellite altimetry and optical imagery. These datasets are important for understanding how inland water bodies respond to environmental changes, but the current process requires combining data from different satellites, limiting the simultaneous measurement of water levels and surface areas.

The SWOT mission presents a breakthrough in this regard. Equipped with both a conventional nadir altimeter and a new Ka-band Radar Interferometer (KaRIn), SWOT can provide simultaneous measurements of water levels and surface areas of inland water bodies. This new technology enables a more detailed and high-resolution dataset for hydrological studies. For water bodies larger than 1 km², SWOT is expected to achieve height accuracies better than 11 cm, and for smaller water bodies (250 m² to 1 km²), accuracy better than 25 cm is anticipated.

In this project, SWOT data will be integrated into the DAHITI apporach to enhance the estimation of water level time series, surface area time series, and volume variations of lakes and reservoirs. The inclusion of SWOT's simultaneous water level and surface area data will also improve the understanding of hypsometry - the relationship between water levels and surface areas. This will result in more accurate estimations of storage changes in water bodies, particularly for those that had previously been difficult to monitor due to the limitations of conventional satellite technologies.

Additionally, SWOT’s wide swath coverage will allow for the monitoring of inland water bodies that have not been captured before. All data products derived from SWOT, including those that combine multi-mission data with other satellite sensors, will undergo validation using in-situ measurements to ensure accuracy. Once validated, the products will be made freely available through DAHITI web portal (https://dahiti.dgfi.tum.de/).

In conclusion, the integration of SWOT data into DAHITI represents a significant advancement in the monitoring and modeling of inland water systems. The project is expected to enhance the accuracy of water level and surface area measurements and contribute valuable insights for hydrological and climate change studies.