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.

Flood inundation modelling across large data sparse areas has been increasing in recent years, driven by a desire to provide hazard information for a wider range of locations. The sophistication of these models has steadily advanced over the past decade due to improvements in remote sensing and modelling capability. There are now several global flood models (GFMs) that seek to simulate water surface dynamics across all rivers and floodplains regardless of data scarcity. However, flood models in data sparse areas lack river bathymetry because this cannot be observed remotely, meaning that a variety of methods for approximating river bathymetry have been developed from uniform flow or downstream hydraulic geometry theory. We argue that bathymetry estimation in these models should follow gradually varying flow theory to account for both uniform and nonuniform flows. We demonstrate that existing methods for bathymetry estimation in GFM’s are only accurate for kinematic reaches and are unable to simulate unbiased water surface profiles for reaches with diffusive or shallow water wave properties. The use of gradually varied flow theory to estimate bathymetry in a GFM reduced water surface profile errors by 66% and eliminated bias due to backwater effects. For a large-scale test case in Mozambique this reduced flood extends by 40% and floodplain storage by 79% at the 1 in 5 year return period. The results have significant implications for the role floodplains play in attenuating river discharges because previous GFM’s based on uniform flow theory will overstate the role of the floodplain.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage anomaly (VCWS) on 26 major world rivers. We find mainstem VCWS climatology amplitude (VCWSCA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.2%, range 0.05-13.8% across world rivers), despite the fact that mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate VCWSCA (within {plus minus}50%) in only 19.2 % and 23.1 % of rivers considered (by model). These findings demonstrate VCWS is a useful measurement for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

To help store water, facilitate navigation, generate energy, mitigate floods, and support industrial and agricultural production, people have built and continue to build obstructions to natural flow in rivers. However, due to the long and complex history of constructing and removing such obstructions, we lack a globally consistent record of their locations and types. Here, we used a consistent method to visually locate and classify obstructions on 2.1 million km of large rivers (width ≥ 30m) globally. We based our mapping on Google Earth Engine’s high resolution images from 2018–2020, which for many places have meter-scale resolution. The resulting dataset, the Global River Obstruction Database (GROD), consists of 29,877 unique obstructions, covering six different obstruction types: dam, lock, low head dam, channel dam, and two types of partial dams. By classifying a subset of the obstructions multiple times, we are able to show high classification consistency (87% mean balanced accuracy) for the three types of obstructions that fully intersect rivers: dams, low head dams, and locks. The classification of the three types of partial obstructions are somewhat less consistent (61% mean balanced accuracy). Overall, by comparing GROD to similar datasets, we estimate GROD likely captured 90% of the obstructions on large rivers. We anticipate that GROD will be of wide interest to the hydrological modeling, aquatic ecology, geomorphology, and water resource management communities.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage (CWS) change on 26 major world rivers. We find mainstem volumetric CWS climatology amplitude (CA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.78%, range 0.04-12.54% across world rivers), despite mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate CA (within ±50%) in only 11.5 % (CaMa-Flood) and 30.7 % (HyMap) of rivers considered. These findings demonstrate volumetric CWS is a useful quantity for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.