Ruh-Ming Li

Flow Resistance in Vegetated Waterways

ABSTRACT Anew method to determine the flow capacity of a vegetated channel is presented. The method uses the vegetations length and stiffness as parameters. Graphs of field data of Mannings n versus the product VR, velocity times hydraulic radius, can be accurately repro-duced. A table of stiffness values for various types of veg-etation for which flow data were available is included. It is shown that the n —VR method of channel design cur-rently in use is based on the correct premise that n is a function of VR regardless of the relative values of V and R. The limitations of the n —VR method are stated. The advantage of the new method is that it is a numerical method which can be easily incorporated in computer programs for other purposes such as backwater or rout-ing models.

Flood Prediction with Causal Analysis

The traditional method of curve fitting, in order to predict low probability flood events, does not utilize any considerations of the physical processes which determine flood flows. Causal analysis is a disaggregation-aggregation method of data analysis and flood prediction. Raw extreme event data are partitioned into subsets. These subsets are characterized by the physical processes which cause the observed floods, i. e., low pressure storms, thunder-showers, etc. Once the subsets are determined and the data are partitioned, then distributions are fitted to each subset. By aggregating the subset distributions, the joint probability distribution of all flood causal processes may be determined. Knowledge of the joint probability distribution allows estimation of low probability events either by sampling or by theory. Identification of the probability distribution of independent meteorologic events, i. e., hurricanes, thunderstorms, etc., form the building blocks of the causal analysis joint probability predictive model. Thus, regionalized information may be employed in data scarce regions to aid in the predictive model formulation. This partitioning-aggregation procedure considers both physical and statistical principles and thus is an improvement over purely statistical techniques. Causal analysis is compared to the more simple curve-fitting method with a case study of a humid mountain river environment.