The advent of wide-swath interferometry, exemplified by the Surface Water and Ocean Topography (SWOT) mission, is transforming inland water observation with unprecedented spatial coverage. This study explores the potential of wide-swath-derived discharge data to enhance the dynamics of river networks at the continental scale through hydrological modeling and data assimilation. Using the NASA’s RAPID model in a synthetic framework, we evaluate discharge improvements in the Mississippi River basin under high-frequency wide-swath observing configurations. Results show significant enhancements, with data assimilation and bias correction propagating corrections beyond observed reaches, benefiting upstream and downstream regions. Notably, observing as little as 1\% of river segments can improve discharge estimates across 25\% of the network. However, uncertainties persist in smaller tributaries due to error covariance gaps and equifinality. These findings underscore the transformative potential of integrating SWOT data with river models to address critical gaps in global-scale hydrological predictions.

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 the 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: (1) a cutting-edge single or dual-satellite mission for advanced surface water measurements, and (2) a constellation of cost-effective satellites targeting dynamic processes.

The forthcoming Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new datasets for both gaged and ungaged basins. SWOT discharge products will provide discharge for all river reaches wider than 100 m, but at lower accuracy and temporal resolution than what is possible in situ. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present here for the first time a complete estimate of SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge timeseries. We expect that discharge uncertainty will be less than 30% for two thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge timeseries will be dominated by random error and are expected to be estimated to within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.

Variational data assimilation (VAR-DA) has been implemented to estimate the unknown input parameters of a new agricultural subsurface drainage model (SIDRA-RU) through assimilating discharge observations. The adjoint model of SIDRA-RU has been successfully generated by means of the automatic differentiation tool (TAPENADE). First, the adjoint model is used to explore the local and global adjoint sensitivities of the valuable function defined over the drainage discharge simulations with respect to model input parameters. Next, the most influential parameters are estimated by applying the VAR-DA embedded into a simple stochastic procedure in order to achieve the global minimum. The performed sensitivity analysis shows that the most influential parameters on drainage discharge are those controlling the dynamics of the water table; the second most influential parameters manage the starting date of the drainage season. Compared to an alternative gradient-free calibration performance, the estimation of these governing parameters by the VAR-DA method improves the overall quality of the drainage discharge prediction, in particular in terms of the cumulative water volume. Improved parameters generate less than 5 mm (1%) of the discrepancy between simulated and observed water volume, based on the five years of daily discharge observations on the Chantemerle agricultural parcels (36 ha). Preliminary numerical tests allow identifying the potential presence of local minima as well as equifinality issues. The latter can be highlighted by the self-compensation of both the physical soil parameters and the main conceptual parameters. Moreover, the proposed techniques may be applied to a panel of hydrological and water quality models.