The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50‐100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning‐Strickler equation, complementing in‐situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters (e.g. friction coefficient, bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT‐like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst‐case SWOT discharge accuracy.

The water in Earth's rivers propagates as waves through space and time across hydrographic networks. A detailed understanding of river dynamics globally is essential for achieving accurate knowledge of surface water storage and fluxes to support water resources management and water‐related disaster forecasting and mitigation. Global in situ information on river flows are crucial to support such an investigation but remain difficult to obtain at adequate spatiotemporal scales, if they even exist. Many expectations are placed on remote sensing techniques as key contributors. Despite a rapid expansion of satellite capabilities, however, it remains unclear what temporal revisit, spatial coverage, footprint size, spatial resolution, observation accuracy, latency time, and variables of interest from satellites are best suited to capture the space‐time propagation of water in rivers. Additionally, the ability of numerical models to compensate for data sparsity through model‐data fusion remains elusive. We review recent efforts to identify the type of remote sensing observations that could enhance understanding and representation of river dynamics. Key priorities include: (a) resolving narrow water bodies (finer than 50–100 m), (b) further analysis of signal accuracy versus hydrologic variability and relevant technologies (optical/SAR imagery, altimetry, microwave radiometry), (c) achieving 1–3 days observation intervals, (d) leveraging data assimilation and multi‐satellite approaches using existing constellations, and (e) new variable measurement for accurate water flux and discharge estimates. We recommend a hydrology‐focused, multi‐mission observing system comprising: (a) a cutting‐edge single or dual‐satellite mission for advanced surface water measurements, and (b) a constellation of cost‐effective satellites targeting dynamic processes.

Satellite altimetry has revolutionized river monitoring, particularly for hydrologists working on river flow monitoring in sparsely or ungauged areas. Despite this, there's a lack of a comprehensive evaluation of radar and lidar altimeters with varying sensor specifications for river water level retrieval, seasonal change characterization, and water surface slope (WSS) using gauged long-term water level and global navigation satellite system (GNSS) data. This study addresses this gap by combined evaluation of radar (ENVISAT to Sentinel-3) and lidar (ICESat-1, ICESat-2) altimeters along the Ganga River, from Prayagraj to Varanasi. We found that, all the radar altimetry missions showed better accuracy for water level retrievals (R2 > = 0.8; RMSE 0.11 to 1.16 m) and water level change quantification (RMSE 0.59 m). However, Sentinel-3 with SAR acquisition mode outperformed (RMSE 0.11 to 0.14 m) all the radar missions having low resolution mode. Despite lidar missions' high vertical accuracy, they show relatively lower accuracy in water level time series generation due to non-repeating characteristics. In contrary, ICESat-2 demonstrates potential in capturing spatial and seasonal variability of WSS, enhancing the accuracy of SWOT discharge products when combined with SWOT River database (SWORD). This study provides a comprehensive baseline for end-users interested in utilizing radar and lidar missions for various hydrological applications, including river discharge estimation. Moreover, the studied river reach shares the SWOT calibration orbit, allowing the utilization of generated satellite and in-situ databases for the effective evaluation of SWOT measurements.

The Surface Water and Ocean Topography (SWOT) mission offers novel global observations of river hydrodynamics, yet its performance across varied river morphologies remains understudied. We evaluate SWOT node and raster products over a ∼210 km stretch of the Ganga River, India, during the fast‐sampling phase, using GNSS‐based continuous measurements, two in situ stations, and four altimetry virtual stations. Node products show slightly better water surface elevation (WSE) accuracy than raster products, with RMSEs of 0.19 m (GNSS), 0.09 m (in situ station, Varanasi), and 0.88 m (virtual stations), though quality filters reduce temporal resolution. Raster data, however, captures 2D WSE variability, enhancing spatial sampling in wide river cross‐sections. SWOT‐derived water surface slopes (WSS) yield mixed results across SWORD reaches (RMSE: 2.54 cm/km). Strong backscatter from main river channel waters (mean +13.34 dB) aids width retrieval. Our analysis highlights SWOT's potential for river hydrodynamic applications while underscoring the need of evaluation in complex riverine environments.