BAM: Bayesian AMHG‐Manning Inference of Discharge Using Remotely Sensed Stream Width, Slope, and Height. Issue 11 (24th November 2017) - Hagemann, M. W.; Gleason, C. J.; Durand, M. T.
Abstract: The forthcoming Surface Water and Ocean Topography (SWOT) NASA satellite mission will measure water surface width, height, and slope of major rivers worldwide. The resulting data could provide an unprecedented account of river discharge at continental scales, but reliable methods need to be identified prior to launch. Here we present a novel algorithm for discharge estimation from only remotely sensed stream width, slope, and height at multiple locations along a mass‐conserved river segment. The algorithm, termed the Bayesian AMHG‐Manning (BAM) algorithm, implements a Bayesian formulation of streamflow uncertainty using a combination of Manning's equation and at‐many‐stations hydraulic geometry (AMHG). Bayesian methods provide a statistically defensible approach to generating discharge estimates in a physically underconstrained system but rely on prior distributions that quantify the a priori uncertainty of unknown quantities including discharge and hydraulic equation parameters. These were obtained from literature‐reported values and from a USGS data set of acoustic Doppler current profiler (ADCP) measurements at USGS stream gauges. A data set of simulated widths, slopes, and heights from 19 rivers was used to evaluate the algorithms using a set of performance metrics. Results across the 19 rivers indicate an improvement in performance of BAM over previously tested methods and highlight a path forward in solving discharge estimation using solely satellite remote sensing. Key Points: A new method is presented for estimating discharge using satellite observations The method uses Bayesian inference via hydraulics‐derived likelihood and empirical priors from existing data sources Performance improvements over previous estimation algorithms are demonstrated on benchmark data sets