Evaluating SWOT Capabilities for Spatiotemporal River Discharge Estimation over the Ganga River in Northern India

River discharge is an important hydrological parameter that reflects basin characteristics and is useful in various water-resource applications. Continuous and spatially distributed measurements are essential for understanding upstream–downstream water dynamics and flood propagation. The Surface Water and Ocean Topography (SWOT) satellite mission, which measures the three-dimensional water surface dynamics of a river system, presents a unique opportunity to estimate river discharge. In this study, we evaluate the potential of SWOT observations for spatiotemporal river discharge estimation over sections of the Ganga River and its tributaries. The study utilizes a SWOT high-rate Level-2 pixel-cloud (PIXC) dataset with multiple tiles covering the Ganga and Ghaghara Rivers. Elevation values from PIXC were processed to generate raster data by applying various quality flags and geoid/tidal corrections. A novel dynamic window selection approach is proposed to extract the elevation values over virtual stations. This approach robustly samples the most appropriate elevation values, discarding noisy data and handling missing values. Discharge is estimated using Manning’s equation, combining SWOT-derived water surface elevation, slope, and river depth variations, with river width estimates derived from Landsat-8/9 data for multiple channel geometries. Results were compared with GloFAS v4.0 reanalysis discharge, gauge observations, and field measurements over the Ganga River. Comparative analysis revealed that over nodal and morphologically stable reaches of the river, SWOT successfully captured the seasonal discharge variability and flood peaks, achieving performance metrics of NSE = 0.94 and R 2 = 0.95 at #VS13 near Gandhighat, Patna, along the Ganga river. However, over multithread/braided or shallow reaches, SWOT showed degraded performance. The developed methodology demonstrates the potential of SWOT for reliable discharge estimation and provides a way forward for improving algorithms over complex and braided river environments.