Estimating River Discharge With Swath Altimetry: A Proof of Concept Using AirSWOT Observations

Tuozzolo, S.; Lind, Greg D.; Overstreet, B. T.; Mangano, Joseph F.; Fonstad, Mark A.; Hagemann, Mark; Frasson, Renato Prata de Moraes; Larnier, Kévin; Garambois, Pierre‐André; Monnier, Jérôme; Durand, Michael

doi:10.1029/2018gl080771

Cited by 60 publications

(84 citation statements)

References 42 publications

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“…A key SWOT mission objective is to obtain water surface elevation (WSE) to ±10 cm vertical accuracy for 1 km 2 open water regions [66]. Previous work used CIR-derived water masks and spatially-averaged AirSWOT Ka-band interferometric radar data to estimate AirSWOT WSE accuracies of~9-10 cm in rivers and~21 cm in lakes [53,[67][68][69]. The conservative AirSWOT color-infrared (CIR) water mask provided here should enable unambiguous extraction of open water pixels used for spatial averaging of AirSWOT interferometric radar data, and, therefore, improve estimates of WSE accuracy (Figure 10).…”

Section: Utility Of Cir Open Water Classifications For the Swot Satelmentioning

confidence: 99%

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

et al. 2019

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The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km 2 of open surface water were mapped from 23,380 km 2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km 2 (40 m 2 ) to 15 km 2 . Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km 2 in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here.

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Section: Utility Of Cir Open Water Classifications For the Swot Satelmentioning

confidence: 99%

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

et al. 2019

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“…We use a 0.5 km 2 window, while Altenau et al use 1 km 2 areas. Tuozzolo et al [30] reported RMSE of 11.6 cm compared to in situ data over the Willamette River, Oregon, when using a 1 km shifting window to perform the averaging. Pitcher et al [29] tested window sizes of both 1 km 2 and 0.0625 km 2 for a study in the Yukon Flats Basin in Alaska, and reported RMSE values of 8 and 15 cm, respectively, for rivers only.…”

Section: Water Surface Elevationmentioning

confidence: 99%

“…Early studies had demonstrated the feasibility and potential of Ka-band InSAR for mapping water surfaces, using both ground-based [24] and airborne radar experiments [25,26]. Recent studies have demonstrated the capabilities of AirSWOT to measure rivers and lakes [27][28][29][30][31]. Altenau et al [27] used AirSWOT data to measure WSE, WSS, and estimate discharge [28] along the Tanana River, Alaska, USA.…”

Section: Introductionmentioning

confidence: 99%

“…Pitcher et al [29] used AirSWOT to map WSE for rivers and lakes in the Yukon Flats Basin, Alaska, reporting WSE RMS errors between 8 to 15 cm over rivers and 21 cm over lakes. Similarly, Tuozzolo et al [30] used AirSWOT data to estimate WSE, WSS, and river discharge over the Willamette River in Oregon, USA, reporting WSE and WSS RMSE of 11.6 cm and 3.2 cm/km, respectively, and discharge error of 10 to 31% depending on discharge algorithm and accurate prior estimate of mean annual discharge. While these uncertainties are slightly higher than that of Altenau et al [27,28] or Pitcher et al [29], Tuozzolo et al noted the Willamette is a thinner river, requiring smaller averaging windows (i.e., less pixels).…”

Section: Introductionmentioning

confidence: 99%

“…While these uncertainties are slightly higher than that of Altenau et al [27,28] or Pitcher et al [29], Tuozzolo et al noted the Willamette is a thinner river, requiring smaller averaging windows (i.e., less pixels). Appropriate methods for averaging and filtering of AirSWOT data enable accurate estimation of WSE and WSS in spite of the noise in AirSWOT measurements [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Water Surface Elevation and Slope in the Mississippi River Delta Using the AirSWOT Ka-Band Interferometric Synthetic Aperture Radar

et al. 2019

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AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT was designed as a calibration and validation instrument for the forthcoming Surface Water and Ocean Topography (SWOT) mission, an international spaceborne synthetic aperture radar mission planned for launch in 2022 which will enable global mapping of WSE and WSS. As an airborne instrument, capable of quickly repeating overflights, AirSWOT enables measurement of high frequency and fine scale hydrological processes encountered in coastal regions. In this paper, we use data collected by AirSWOT in the Mississippi River Delta and surrounding wetlands of coastal Louisiana, USA, to investigate the capabilities of Ka-band interferometry for mapping WSE and WSS in coastal marsh environments. We introduce a data-driven method to estimate the time-varying interferometric phase drift resulting from radar hardware response to environmental conditions. A system of linear equations based on AirSWOT measurements is solved for elevation bias and time-varying phase calibration parameters using weighted least squares. We observed AirSWOT WSE uncertainty of 12 cm RMS compared to in situ water level measurements when averaged over an area of 0.5 km 2 at incidence angles below 15 ∘ . At higher incidence angles, the observed AirSWOT elevation bias is possibly due to residual phase calibration errors or radar backscatter from vegetation. Elevation profiles along the Wax Lake Outlet river channel indicate AirSWOT can measure WSS over a 24 km distance with uncertainty below 0.3 cm/km, 8% of the true water surface slope as measured by in situ data. The data analysis and results presented in this paper demonstrate the potential of AirSWOT to measure water surface elevation and slope within highly dynamic and spatially complex coastal environments.

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A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission

Durand

Gleason

Pavelsky

et al. 2021

Preprint

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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.

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Estimating River Discharge With Swath Altimetry: A Proof of Concept Using AirSWOT Observations

Cited by 60 publications

References 42 publications

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

Mapping Water Surface Elevation and Slope in the Mississippi River Delta Using the AirSWOT Ka-Band Interferometric Synthetic Aperture Radar

A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission

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