David M. Bjerklie

TL;DR: In this article, the authors used multiple-regression analyses of hydraulic data from more than 1000 discharge measurements, ranging in magnitude from over 200,000 to less than 1 m3/s, to develop multi-variate river discharge estimating equations that use various combinations of potentially observable variables to estimate river discharge.

TL;DR: In this paper, a methodology to estimate in-bank river discharge exclusively from remotely sensed hydraulic data is developed, where water-surface width and maximum channel width measured from 26 aerial and digital orthophotos of 17 single channel rivers and 41 SAR images of three braided rivers were coupled with channel slope data obtained from topographic maps to estimate the discharge.

TL;DR: In this article, the authors evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily "remote sensing" measurements derived from hydraulic models corrupted with minimal observational errors, and found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-ofbank flows, multichannel planforms, and backwater effects.

TL;DR: In this paper, 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, including the Manning equation and the Prandtl-von Karman universal velocity distribution equation.

TL;DR: In this article, a method to estimate the bankfull velocity and discharge in rivers that uses the morphological variables of the river channel, including bankfull width, channel slope, and meander length, was developed and tested.