The SWOT satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Here, we present the first discharge estimates from SWOT during the initial orbital configuration of the mission. A preliminary accuracy assessment from April of 2023 shows results consistent with pre-launch expectations: SWOT discharge can track dynamics without any gauge information. It captures discharge magnitude correctly in some cases but has bias in others. Correlations in a set of representative cases of SWOT performance ranged from 0.42 to 0.89, while the normalized root mean squared error was between 4.6% and 67.5%. Although we were very limited in the selection of river reaches, the feasibility of estimating river discharge from SWOT and these promising initial results are encouraging. As SWOT data are improved by reprocessing, we argue that the outlook for SWOT discharge is bright.

Current machine learning methods for discharge prediction often employ aggregated basin-wide hydrometeorological data (lumped modeling) for parametric and non-parametric training. This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating temporal-spatial hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. To test this hypothesis, we designed experiments comparing the performance of identical Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models forced with either lumped or distributed features. We gather meteorological forcing and static attributes for the Mackenzie basin in Canada- a large and unique basin. Importantly, discharge performance is assessed out-of-sample with k-fold replication across gauges. Results reveal a 9.6% improvement in the mean Nash-Sutcliffe Efficiency (NSE) and a 4.6% improvement in mean Kling-Gupta Efficiency (KGE) when LSTMs are trained with distributed information. Notably, the models exhibit consistently unbiased predictions, with a negligible relative bias (RBias ≈ 0.0) across all predictions. These experiments and results demonstrate the importance of integrating topologically guided geomorphologic and hydrologic information (distributed modeling) in data-driven discharge predictions.

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.