Jida Wang (University of Illinois Urbana-Champaign, United States); George Allen (Virginia Tech, United States); Huilin Gao (Texas A&M University, United States)

River-lake interfaces prevail across global hydrological systems and host unique processes for modulating flow and sediment regimes. In particular, the inlet corridor where a river flows into a lake is often characterized by an “M1” surface-water profile, or the so-called backwater curve, where water elevation transitions asymptotically from the sloping river flow upstream to the horizontal lake surface downstream. This phenomenon is pronounced when a low-gradient, subcritical river flows into a large reservoir, where backwater effect can be expressed tens of kilometers upstream. The scale and location of the backwater curve vary seasonally, reflecting the dynamic interactions between inflow regimes and reservoir regulations. Characterizing backwater dynamics is important not only because it helps accurately locate the shifting inlet boundary of a reservoir, but also because it contributes to the estimation of the reservoir “wedge water storage”, i.e., the additional volume of water superelevated by backwater. This wedge storage is critical for reservoir water management but has been largely ignored due to observation challenges.

The Surface Water and Ocean Topography (SWOT) satellite mission, with its repeated wide-swath, fine-resolution water surface elevation data, offers unprecedented potential for capturing river-lake hydrodynamics. In this study, we leverage multiple SWOT L2 data products, including pixel cloud and standard vector datasets, to retrieve multi-temporal surface-water profiles along the inflow corridors of selected reservoirs worldwide. Using principles from open-channel hydraulics, we decompose each retrieved profile into horizontal, static, and wedge storage zones, which enables more accurate estimation of dynamic reservoir extents and storage changes. We further investigate how surface-water profiles evolve throughout reservoir operation cycles, highlighting seasonal amplification of backwater effects and their implications for water management.

This study demonstrates SWOT’s unique capability in capturing complex river-lake interactions and highlights the critical importance of resolving backwater dynamics for improved reservoir monitoring. It also serves as a proof of concept for our global application of SWOT data in generating L4 reservoir products that explicitly account for backwater effects.