Date of Award

Spring 2004

Project Type


Program or Major

Earth Sciences

Degree Name

Doctor of Philosophy

First Advisor

S Lawrence Dingman


An evaluation of river hydraulic data currently or potentially available from satellite and other remote platforms was completed, and a set of discharge estimation models proposed that can use the remotely sensed information to estimate discharge with reasonable accuracy. Reasonable accuracy is defined as within +/-20% of the observed on average for a large number of estimates. The proposed estimation models are based on the Manning and Chezy flow resistance equations, and utilize combinations of potentially observable variables including water-surface width, maximum-channel (or bankfull) width, mean water depth, mean maximum-channel depth, mean water velocity, and channel slope. Both statistically and rationally derived prediction models are presented, developed and calibrated on a data base of river discharge measurements and a quasi-theoretical data base of synthetic data. It was found that the channel slope can be used in lieu of a measured water surface slope with very little reduction in prediction accuracy when considering many estimates. Notably absent from this list is a resistance variable, which is included in both the Manning and Chezy equations, because this variable cannot be observed or directly measured. One of the key outcomes of the research is that an exponent of 0.33 on the slope explains much of the variability in the resistance variable, and provides better predictive qualities than the traditional value of 0.5. A dimensionally homogeneous form of the Manning equation was developed which derives the slope exponent of 0.33 based on stable-bed grain size considerations. The prediction models were tested on two data sets of remotely sensed hydraulic information that included width, maximum channel width, and channel slope. Predictions were also made from a single radar image that also included remotely sensed surface velocity, demonstrating the potential for greatly improved accuracy with this additional information. Additionally, the prediction models were tested with channel slope information derived from a digital elevation model, and used to define river channel geometry for a continental scale runoff model.