EGU25-754, updated on 14 Mar 2025
https://doi.org/10.5194/egusphere-egu25-754
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Oral | Tuesday, 29 Apr, 12:00–12:10 (CEST)
 
Room -2.93
Quantifying input volumes in Australia’s largest playa lake using SWOT data
Atul Kumar Rai1, Timothy J. Cohen1, Moshe Armon2, and Samuel K. Marx1
Atul Kumar Rai et al.
  • 1Environmental Futures, University of Wollongong, NSW 2522, Australia
  • 2The Fredy & Nadine Herrmann Institute of Earth Sciences The Hebrew University of Jerusalem, Jerusalem 9190401 Israel

Australia's drylands, covering nearly 70% of the continent exhibit the most variable precipitation and streamflow regimes globally. The endorheic Lake Eyre Basin (LEB) terminates at Kati Thanda-Lake Eyre (KT–LE), Australia’s largest lake and drains 1.14 M km2. This basin experiences remarkable ecological fluctuations with spectacular boom and bust cycles during extreme flooding events. This vast unregulated river basin, despite its ecological significance, has limited stream gauges and no lake monitoring, making the lake's water balance a real challenge due to its vast size, remote location and complex lake geometry. Recent observations reveal significant water loss in endorheic basins worldwide, emphasizing the urgency for improved freshwater monitoring solutions for KT – LE and its basin. Therefore, in this study, we present a space-based monitoring solution to estimate the storage volume of the KT–LE as an alternative to in situ measurements.  To do so, we utilized the data from the Surface Water and Ocean Topography (SWOT) satellite, launched in December 2022, to monitor the 2024 KT-LE filling event. The duration of this event was between March and October 2024. The predicted maximum lake storage volume (recorded on 1st May) reached 0.82 Km3 with a predicted average depth of -14.2 metres AHD (Australian Height Datum). We cross-compared the volume estimates from three bathymetry digital elevation models to evaluate the derived estimates in the absence of in situ data. We achieved the accuracy of the derived water surface elevation estimates with a root mean square error (RMSE) of <0.6 meters. This research highlights the potential of SWOT data for addressing critical data gaps in hydrological monitoring and advancing water balance assessments in arid and semi-arid regions and in large wide and shallow playa lakes.

How to cite: Rai, A. K., Cohen, T. J., Armon, M., and Marx, S. K.: Quantifying input volumes in Australia’s largest playa lake using SWOT data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-754, https://doi.org/10.5194/egusphere-egu25-754, 2025.