Satellite Video Remote Sensing for Estimation of River Discharge

We demonstrate that river discharge can be estimated by deriving water surface velocity estimates from satellite-derived video imagery when combined with high-resolution topography of channel geometry. Large Scale Particle Image Velocimetry (LSPIV) was used to map surface velocity from 28 s of 5 Hz satellite video acquired at a 1.2 m nominal ground spacing over the Darling River, Tilpa, Australia, during a 1-in-5-year flood. We stabilized and assessed the uncertainty of the residual motion induced by the satellite platform, enhancing our sub-pixel motion analysis, and quantified the sensitivity of image extraction rates on computed velocities. In the absence of in situ observations, LSPIV velocity estimates were validated against predictions from a calibrated 2D hydrodynamic model. Despite the confounding influence of selecting a surface velocity depth-averaging coefficient, inference of discharge was within 0.3%-15% compared with gauging station measurements. These results provide a valuable foundation for refining satellite video LSPIV techniques. Estimates of river flow are needed to manage water resources and flood risk. However, many of the world's rivers are not gauged, limiting hydrological understanding of river response to changing environmental conditions and storm events. We demonstrate that satellite video can be used to map velocity by tracking surface water features from one video frame to the next, and scaled to compute discharge where river geometry is known. Using a video of a flood on the River Tilpa, Australia, our results agree with ground-based measurements to within 0.3%-15%. The ability to deploy satellites to acquire video anywhere globally could contribute to measuring discharge on ungauged rivers. Satellite video acquired along 12.6 km of the River Darling, Australia, at 5 Hz for 28 s during a 1-in-5-year storm eventSatellite video-based velocities coupled with high resolution topography estimate riverine discharges to within 15% of in situ gauge dataParametrization of non-contact velocimetry and choice of a depth-averaging coefficient (alpha) influence the accuracy of discharge estimates