The Surface Water and Ocean Topography (SWOT) satellite mission will, for the first time, provide simultaneous, high‐resolution measurements of water surface elevation and extent. Here we explore the applicability of SWOT's unique sampling to capture discharge frequency behavior throughout the Mississippi River Basin. Our findings suggest the mission may capture key variability in river discharge series. SWOT orbit specifications, US Geological Survey (USGS) discharge measurements, and potential uncertainty estimates are used to generate SWOT‐like river discharges. Frequency distributions and specific quantiles derived from synthetic SWOT discharge series are compared to those derived from daily USGS discharge series. Based on the Kolmogorov‐Smirnov test, SWOT temporal sampling has essentially no impact on derived frequency distributions. When including potential uncertainty, 78% of derived distributions are statistically identical. The combined effects of temporal sampling and discharge uncertainty mitigates the negative bias used for SWOT discharge uncertainty at larger discharge quantiles (i.e., ≥75% quantiles).
The Surface Water and Ocean Topography (SWOT) satellite mission, expected to launch in 2022, will enable near global river discharge estimation from surface water extents and elevations. However, SWOT’s orbit specifications provide non-uniform space–time sampling. Previous studies have demonstrated that SWOT’s unique spatiotemporal sampling has a minimal impact on derived discharge frequency distributions, baseflow magnitudes, and annual discharge characteristics. In this study, we aim to extend the analysis of SWOT’s added value in the context of hydrologic model calibration. We calibrate a hydrologic model using previously derived synthetic SWOT discharges across 39 gauges in the Ohio River Basin. Three discharge timeseries are used for calibration: daily observations, SWOT temporally sampled, and SWOT temporally sampled including estimated uncertainty. Using 10,000 model iterations to explore predefined parameter ranges, each discharge timeseries results in similar optimal model parameters. We find that the annual mean and peak flow values at each gauge location from the optimal parameter sets derived from each discharge timeseries differ by less than 10% percent on average. Our findings suggest that hydrologic models calibrated using discharges derived from SWOT’s non-uniform space–time sampling are likely to achieve results similar to those based on calibrating with in situ daily observations.
Estimating river discharge from observed surface water extents and elevations is central to the Surface Water and Ocean Topography (SWOT) mission. Although near global in coverage, SWOT will only observe rivers wider than 50 to 100 m, overlooking smaller tributaries draining into observable river reaches. This is problematic for the Metropolis-Manning (MetroMan) discharge algorithm, which assumes changes in discharge per location must be balanced by a change in cross-sectional area, not accounting for potential flow contributions SWOT will not observe within the inversion region analyzed. Here, we quantify the effect of these lateral inflows on the performance of estimated discharges along the Muskingum River using MetroMan. Three scenarios are considered: (1) disregarding lateral inflows, (2) providing MetroMan with observed lateral inflows, and (3) providing MetroMan with uncertain model-derived lateral inflows to assess the discharge algorithm's effectiveness. Scenarios are expanded to consider multiple lateral inflow magnitudes and distributions. Results indicate discharge retrievals were degraded once unaccounted lateral inflows exceeded 5% of average river discharge. When MetroMan is informed by observed lateral inflows, the derived discharges have a relative root-mean-square error (rRMSE) of 23% as compared to 360% when lateral inflows are neglected. More importantly, when MetroMan uses simulated lateral inflows, with peak flow condition percent errors as high as 93%, discharge retrieval performance is similar (rRMSE = 17%). These findings highlight the importance of accounting for lateral flows, even in the absence of perfect measurements.
Of the many ways Earth observations can be measured, collected, and archived, the vantage from space using satellite remote sensing offers the most synoptic, regular option at scale with seamless global spatial coverage and temporal continuity. In the field of Earth science, satellite remote sensing has contributed to numerous disciplines with the launch and continuation of Earth-observing satellites dedicated to routinely monitoring the pulse of our planet. For example, improvement in our ability to model and analyze physical processes can now be clearly attributed to satellite remote sensing applications in hydrologic science (
The upcoming Surface Water and Ocean Topography (SWOT) mission will measure rivers wider than 50–100 m using a 21-day orbit, providing river reach derived discharges that can inform applications like flood forecasting and large-scale hydrologic modelling. However, these discharges will not be uniform in time or coincident with those of neighboring reaches. It is often assumed discharge upstream and downstream of a river location are highly correlated in natural conditions and can be transferred using a scaling factor like the drainage area ratio between locations. Here, the applicability of the drainage area ratio method to integrate, in space and time, SWOT-derived discharges throughout the observable river network of the Mississippi River basin is assessed. In some cases, area ratios ranging from 0.01 to 100 can be used, but cumulative urban area and/or the number of dams/reservoirs between locations decrease the method’s applicability. Though the mean number of SWOT observations for a given reach increases by 83% and the number of peak events captured increases by 100%, expanded SWOT sampled time series distributions often underperform compared to the original SWOT sampled time series for significance tests and quantile results. Alternate expansion methods may be more viable for future work.
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