Journal of Geophysical Research Atmospheres · 2010 · 175 citations · 72 references
Natural ResourcesEarth SciencesHydrologySediment TransportStochastic ProcessesTransport PhenomenaBed Load TransportDiffusion ProcessesFluvial Sediment TransportSediment DynamicsSediment EntrainmentSediment Transport In RiversActive Layer DynamicsAnomalous DiffusionExner EquationExtreme Flood EventsFluvial Gravel TransportGravel TransportKolmogorov Forward EquationSediment Transport Equations
One way to study the mechanism of gravel bed load transport is to seed the bed with marked gravel tracer particles within a chosen patch and to follow the pattern of migration and dispersal of particles from this patch. In this study, we invoke the probabilistic Exner equation for sediment conservation of bed gravel, formulated in terms of the difference between the rate of entrainment of gravel into motion and the rate of deposition from motion. Assuming an active layer formulation, stochasticity in particle motion is introduced by considering the step length (distance traveled by a particle once entrained until it is deposited) as a random variable. For step lengths with a relatively thin (e.g., exponential) tail, the above formulation leads to the standard advection‐diffusion equation for tracer dispersal. However, the complexity of rivers, characterized by a broad distribution of particle sizes and extreme flood events, can give rise to a heavy‐tailed distribution of step lengths. This consideration leads to an anomalous advection‐diffusion equation involving fractional derivatives. By identifying the probabilistic Exner equation as a forward Kolmogorov equation for the location of a randomly selected tracer particle, a stochastic model describing the temporal evolution of the relative concentrations is developed. The normal and anomalous advection‐diffusion equations are revealed as its long‐time asymptotic solution. Sample numerical results illustrate the large differences that can arise in predicted tracer concentrations under the normal and anomalous diffusion models. They highlight the need for intensive data collection efforts to aid the selection of the appropriate model in real rivers.
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Probability, Random Variables, and Stochastic Processes
Irwin Miller, Anthanasios Papoulis · Technometrics · 1966 · 6.5K citations
Engineering, Mathematics, Physics +14
<i>Probability, Random Variables, and Stochastic Processes</i>
A. Papoulis, Joseph G. Hoffman · Physics Today · 1967 · 4.1K citations
An Introduction to Probability Theory.
Bert Fristedt, P. A. P. Moran · Journal of the American Statistical Association · 1986 · 3.4K citations