Estimation of River and Water‐Body Stage, Width and Gradients Using Radar Altimetry, Interferometric
            <scp>SAR</scp>
            and Laser Altimetry

Birkett, C. M.; Alsdorf, D. E.; Harding, David J.

doi:10.1002/0470848944.hsa065

Cited by 5 publications

(6 citation statements)

References 58 publications

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“…The third, fourth, and fifth panels in Table show the validation results when water level values from tp , envisat , or Jason 2 are used. Although generally similar accuracy in the range of decimeter was reported for altimetry missions over inland water bodies [ Birkett et al ., ], our results from envisat and Jason 2 outperform the results of tp in terms of correlation, nse and bias. However, due to very coarse resolution of envisat (35 day), the temporal resolution of densified time series by merging 13 vs s is around 5 days.…”

Section: Results and Validationmentioning

confidence: 66%

“…Developing such an algorithm based on current satellite altimetric data including envisat , CryoSat‐2, Jason 1&2, and saral / a lti k a would be highly beneficial for future altimetry satellites, including the operational Sentinel‐3 series (3A to be launched in early 2016), Jason‐ cs /Sentinel‐6 (Launch of JCS/S‐6A in 2020 and JCS/S‐6B in 2026) of the European Copernicus program and the Surface Water Ocean Topography ( swot ) (to be launched in 2020). Moreover, with the launch of lidar altimetry ICESat‐2 (to be launched in 2017), one can expect higher along‐track spatial‐resolution sampling than traditional radar altimetry [ Birkett et al ., ]. Such characteristic of laser altimeter systems provides the potential to measure the stage of narrow rivers (width less than 100 m) unresolved by radar altimetry.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal densification of river water level time series by multimission satellite altimetry

Tourian

Tarpanelli

Elmi

et al. 2016

Water Resources Research

113

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Limitations of satellite radar altimetry for operational hydrology include its spatial and temporal sampling as well as measurement problems caused by local topography and heterogeneity of the reflecting surface. In this study, we develop an approach that eliminates most of these limitations to produce an approximately 3 day temporal resolution water level time series from the original typically (sub)monthly data sets for the Po River in detail, and for Congo, Mississippi, and Danube Rivers. We follow a geodetic approach by which, after estimating and removing intersatellite biases, all virtual stations of several satellite altimeters are connected hydraulically and statistically to produce water level time series at any location along the river. We test different data-selection strategies and validate our method against the extensive available in situ data over the Po River, resulting in an average correlation of 0.7, Root-Mean-Square Error of 0.8 m, bias of 20.4 m, and Nash-Sutcliffe Efficiency coefficient of 0.5. We validate the transferability of our method by applying it to the Congo, Mississippi, and Danube Rivers, which have very different geomorphological and climatic conditions. The methodology yields correlations above 0.75 and Nash-Sutcliffe coefficients of 0.84 (Congo), 0.34 (Mississippi), and 0.35 (Danube). Maillard et al., 2015]. Such studies were motivated to a large extent by the premise that satellite altimetry may fill the gap left by the decline of gauge stations database. Surprisingly, other hydrological data like discharge are available in the open domain globally to a larger extent than in situ water level data. Since it cannot realistically be expected that the distribution and availability of global in situ water level stations will be improved in the future, of course with regional exceptions, research on the use of geodetic satellite data needs to be expanded. It is expected that time series from individual altimetric missions over most rivers are of poor quality, due to the neighboring topography and the heterogeneity of the reflecting surface. Therefore, the time series from individual missions often carry uncertainties and contain data outages, which limit the operational use of altimetry for improving the global river water level databases. As a result, these limitations inhibit also the operational use of altimetric water level into hydrological and hydrodynamic models [Alsdorf et al., 2007].

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Section: Results and Validationmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal densification of river water level time series by multimission satellite altimetry

Tourian

Tarpanelli

Elmi

et al. 2016

Water Resources Research

113

View full text Add to dashboard Cite

show abstract

“…6 and 7, see following sections), with good stability between March and November (for Eagle), and between May and October (for Stevens Village) even though during some of these periods the river may still be frozen. found in a number of texts (e.g., Harding and Jasinski, 2004;Calmant et al, 2004;Birkett et al, 2005;Carabajal et al, 2006;Zhang et al, 2011;Boy and Carabajal, 2013;Srivastava et al, 2013;Wang et al, 2013). The technique relies on the emission and reception of an optical wavelength laser-derived pulse and the measurement of the Range distance as derived from the timing of the returned energy distribution (the laser waveform) of the surface scattering elements (Harding and Carabajal, 2005).…”

Section: Water Surface Stage and Slope From Satellite Radar Altimetrymentioning

confidence: 99%

Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska

Bjerklie

Birkett

Jones

et al. 2018

Journal of Hydrology

102

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A methodology based on general hydraulic relations for rivers has been developed to estimate the discharge (flow rate) of rivers using satellite remote sensing observations. The estimates of discharge, flow depth, and flow velocity are derived from remotely observed water surface area, water surface slope, and water surface height, and demonstrated for two reaches of the Yukon River in Alaska, at Eagle (reach length 34.7 km) and near Stevens Village (reach length 38.3 km). The method is based on fundamental equations of hydraulic flow resistance in rivers, including the Manning equation and the Prandtl-von Karman universal velocity distribution equation. The method employs some new hydraulic relations to help define flow resistance and height of the zero flow boundary in the channel. Estimates are made both with and without calibration. The water surface area of the river reach is measured by using a provisional version of the U.S. Geological Survey (USGS) Landsat based product named Dynamic Surface Water Extent (DSWE). The water surface height and slope measurements require a self-consistent datum, and are derived from observations from the Jason-2 satellite altimeter mission. At both reach locations, the Jason-2 radar altimeter non-winter heights consistently tracked the stage recorded at USGS streamgages with a standard deviation of differences (error) during the non-winter periods of less than 7%. Part of the error may be due to differences in the gage and altimeter crossing locations with respect to the range of stage change and the response to changes in discharge at the upstream and downstream locations. For the 2 non-winter periods, the radar derived slope estimates (mean=0.0003) were constant over the mission lifetime, and in agreement with previously measured USGS water surface slopes and slopes determined from USGS topographic maps. The accuracy of the mean of the uncalibrated daily estimates of discharge varied between reaches, ranging from 13% near Stevens Village (N=90) to-21% at Eagle (N = 246) based on the absolute error, and 5% to-6% based on the error of the log of the estimates. Calibrating to the mean of USGS daily discharge estimates from the streamflow rating for the same period of record at each streamgage resulted in mean absolute errors ranging from 1% to 2%, and log errors ranging from 1% or less. The error pattern of the estimates shows that without calibration, even though the mean is well simulated, the high and low end values over the range of estimates may have significant bias. 1.0 Because of potential climate change effects on water quality, availability, and hydrology, the Yukon River basin is a major focus region for the USGS with ongoing studies that are setting the baseline for future changes within the river and its major tributaries. These programs are particularly examining the processes that affect or control water quality and availability. With climate change potentially causing permafrost regions to melt, the soil can be transformed into biogeochemically active zones alt...

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“…The tracking of river dynamics and discharge from remote platforms rely on width and stage observations, coupled with limited in-situ (ground-based) observations to develop time-series data with intervals between observations on the scale of days to weeks. [23][24][25][26][27][28][29][30][31][32] More recently, Refs. 9, 11, and 12 have developed discharge algorithms designed to apply observations of width, stage, and slope observations from the SWOT mission.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Satellite remote sensing of river discharge: a framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations

Bjerklie

Durand

Lenoir

et al. 2023

J. Appl. Rem. Sens.

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.This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points).

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Estimation of River and Water‐Body Stage, Width and Gradients Using Radar Altimetry, Interferometric SAR and Laser Altimetry

Cited by 5 publications

References 58 publications

Spatiotemporal densification of river water level time series by multimission satellite altimetry

Spatiotemporal densification of river water level time series by multimission satellite altimetry

Satellite remote sensing estimation of river discharge: Application to the Yukon River Alaska

Satellite remote sensing of river discharge: a framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations

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