This paper presents a method for the propagation of uncertainty, modeled in a probabilistic framework, through a model-based simulation of rainflow on a rough terrain. The adopted model involves a system of conservation equations with associated nonlinear state equations. The topography, surface runoff coefficient, and precipitation data are all modeled as spatially varying random processes. The Karhunen-Loeve expansion is used to represent these processes in terms of a denumerable set of random variables. The predicted state variables in the model are identified with their coordinates with respect to the basis formed by the Polynomial Chaos random variables. A system of linear algebraic deterministic equations are derived for estimating these coordinates.

Issue Section:
Technical Papers
Keywords:
hydrology, rain, flow simulation, Karhunen-Loeve transforms, stochastic processes, surface topography, polynomials
Topics:
Chaos, Flow (Dynamics), Polynomials, Stochastic processes, Surface roughness, Modeling, Uncertainty
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