A stochastic model for sediment yield using the Principle of Maximum Entropy

An edited version of this paper was published by AGU. Copyright 1987 American Geophysical Union.The principle of maximum entropy was applied to derive a stochastic model for sediment yield from upland watersheds. By maximizing the conditional entropy subject to certain constraints, a probability distribution of sediment yield conditioned on the probability distribution of direct runoff volume was obtained. This distribution resulted in minimally prejudiced assignment of probabilities on the basis of given information. The parameters of this distribution were determined from such prior information about the direct runoff volume and sediment yield as their means and covariance. The stochastic model was verified by using three sets of field data and was compared with a bivariate normal distribution. The model yielded sediment yield reasonably accurately.This study was supported in part by funds provided by the Geological Survey, U.S. Department of Agriculture, through the Louisiana Water Resources Research Institute, under the project, A Multivariate Stochastic Analysis of Flood Magnitude, Duration and Volume

TRANSFER OF HYDROLOGIC INFORMATION ALONG RIVERS PARTIALLY FED BY KARSTIFIED LIMESTONES

Two types of measures are used to find how much information can be transferred between the runoff series along the river and how much information the linear and non- linear (power- function non- linearity) regression analyses actually transfer. In the former case the relative measures of transferability are based on the entropy theory, and in the latter case the classical correlation coefficients for measuring the transferred information among the river flows are used. The Esencay River basin in Turkey is used as a demonstration case of complex composition of karstic and non- karstic formations feeding water to a river. It was found that the non- linear correlation always transfers more information than the linear correlation. The transferable information is nearly always greater than the transferred information of non- linear case, especially in summer/fall months of flow recession. Due to differences in responses of karstified and non- karstic subbasins to precipitation, it is likely that a search for the other non- linear relationships and the use of the multiple lag cross correlation and regression analysis, would significantly reduce the differences between the transferable and actually transferred information. The correlation and regression analysi