17:15 〜 19:15
[AHW24-P02] Towards an Improved Estimation of Surface Water Storage Using SWOT
キーワード：SWOT, Surface Water Storage (SWS), Hydrological Cycle, SWOT-simulator, Lakes and Reservoirs, Rivers
Accurate assessment of trends and variability in various surface water storage (SWS) components, i.e., reservoirs, lakes, and rivers, is essential to understand the actual state of the hydrological cycle and support effective water resource management. However, inherent uncertainties implicit in the various in-situ, model-based, or remotely sensed data hampers such assessments at required spatiotemporal scales. The Surface Water and Ocean Topography (SWOT) satellite has offered a transformative approach for monitoring these components since December 2022. Using the high-resolution data of water surface elevation and the extent of surface area, it provides 2 to 4 observations every 21 days. By integrating SWOT with the Area-Elevation-Capacity (AEC) curves, it is possible to get improved estimates of SWS changes.
Here, we provide a brief overview of the capabilities of the early SWOT data and SWOT simulator for various SWS components. Synthetic SWOT-based storage change estimates have shown favorable results in 20 reservoirs in the Mekong River basin with an average error of <8%. Similarly, for the selected reservoirs of size >1 km2 in California, minimal errors in the reservoir surface area (error <5%) and height (error <15 cm) were observed, which increased for mountainous and elliptical reservoirs. The Hirakud Reservoir, located within the Mahanadi River basin, reservoir storage estimated using SWOT has a minimum bias of −2.19% whereas the maximum bias is −7.65%. Fewer studies have also been conducted for rivers in which SWOT-based discharge estimates for rivers wider than 100 m are expected to have an uncertainty of <30% for most of the global reaches and errors regarding temporal variations in river discharge time series are expected to be less than 15% for nearly all reaches. Similarly, the primary applications for the lakes have shown promising results. For example, SWOT has captured both short-term fluctuations and long-term trends in the Qinghai–Tibetan Plateau, with accuracy improving with the increasing lake area. The assessment of surface water storage change accuracy for Arctic lakes shows relative errors of <5% for lakes >1 km2 whereas one-hectare size lakes show errors of ~20%. Despite a few limitations associated with the spatial scale, these initial studies ascertain the effectiveness of SWOT to improve the mapping of SWS components by providing continuous high-resolution records. These early findings underscore the potential of SWOT in advancing the understanding of various earth system processes and efficient water allocation and management.
Here, we provide a brief overview of the capabilities of the early SWOT data and SWOT simulator for various SWS components. Synthetic SWOT-based storage change estimates have shown favorable results in 20 reservoirs in the Mekong River basin with an average error of <8%. Similarly, for the selected reservoirs of size >1 km2 in California, minimal errors in the reservoir surface area (error <5%) and height (error <15 cm) were observed, which increased for mountainous and elliptical reservoirs. The Hirakud Reservoir, located within the Mahanadi River basin, reservoir storage estimated using SWOT has a minimum bias of −2.19% whereas the maximum bias is −7.65%. Fewer studies have also been conducted for rivers in which SWOT-based discharge estimates for rivers wider than 100 m are expected to have an uncertainty of <30% for most of the global reaches and errors regarding temporal variations in river discharge time series are expected to be less than 15% for nearly all reaches. Similarly, the primary applications for the lakes have shown promising results. For example, SWOT has captured both short-term fluctuations and long-term trends in the Qinghai–Tibetan Plateau, with accuracy improving with the increasing lake area. The assessment of surface water storage change accuracy for Arctic lakes shows relative errors of <5% for lakes >1 km2 whereas one-hectare size lakes show errors of ~20%. Despite a few limitations associated with the spatial scale, these initial studies ascertain the effectiveness of SWOT to improve the mapping of SWS components by providing continuous high-resolution records. These early findings underscore the potential of SWOT in advancing the understanding of various earth system processes and efficient water allocation and management.