Estimation of River Discharge Solely from Remote-Sensing Derived Data: An Initial Study Over the Yangtze River

Author:	Sichangi, A.W., Wang, L., Hu, Z.D.
Abstract:	A novel approach has been developed to estimating river discharge solely using satellite-derived parameters. The temporal river width observations from Moderate Resolution Imaging Spectroradiometer (MODIS), made at two stream segments a distance apart, are plotted to identify the time lag. The river velocity estimate is then computed using the time lag and distance between the width measurement locations, producing a resultant velocity of 0.96 m/s. The estimated velocity is comparable to that computed from in situ gauge-observed data. An empirical relationship is then utilized to estimate river depth. In addition, the channel condition values published in tables are used to estimate the roughness coefficient. The channel slope is derived from the digital elevation model averaged over a river section approximately 516 km long. Finally, the temporal depth changes is captured by adjusting the estimated depth to the Envisat satellite altimetry-derived water level changes, and river width changes from Landsat ETM+. The newly developed procedure was applied to two river sites for validation. In both cases, the river discharges were estimated with reasonable accuracy (with Nash-Sutcliffe values >0.50). The performance evaluation of discharge estimation using satellite-derived parameters was also analyzed. Since the methodology for estimating discharge is solely dependent on global satellite datasets, it represents a promising technique for use on rivers worldwide.
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Page number:	21
Issue:	9
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PubYear:	2018
Volume:	10
Publication name:	Remote Sensing
Abstract:	A novel approach has been developed to estimating river discharge solely using satellite-derived parameters. The temporal river width observations from Moderate Resolution Imaging Spectroradiometer (MODIS), made at two stream segments a distance apart, are plotted to identify the time lag. The river velocity estimate is then computed using the time lag and distance between the width measurement locations, producing a resultant velocity of 0.96 m/s. The estimated velocity is comparable to that computed from in situ gauge-observed data. An empirical relationship is then utilized to estimate river depth. In addition, the channel condition values published in tables are used to estimate the roughness coefficient. The channel slope is derived from the digital elevation model averaged over a river section approximately 516 km long. Finally, the temporal depth changes is captured by adjusting the estimated depth to the Envisat satellite altimetry-derived water level changes, and river width changes from Landsat ETM+. The newly developed procedure was applied to two river sites for validation. In both cases, the river discharges were estimated with reasonable accuracy (with Nash-Sutcliffe values >0.50). The performance evaluation of discharge estimation using satellite-derived parameters was also analyzed. Since the methodology for estimating discharge is solely dependent on global satellite datasets, it represents a promising technique for use on rivers worldwide.
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