Dynamical Persistency in River Flows

TL;DR

This paper introduces a stochastic model with dynamically changing probability based on drainage area to explain the fractal and self-affine properties of river networks, highlighting dynamical persistency in river flows.

Contribution

It presents a novel stochastic framework that accounts for the fractal nature and dynamical persistency of river networks through a probability that varies with drainage area.

Findings

River networks exhibit self-affine properties due to dynamical persistency.

The model captures fractal features of river basins using a Markovian process.

Dynamical probability depends on drainage area, influencing network development.

Abstract

The universal fractality of river networks is very well known, however understanding of the underlying mechanisms for them is still lacking in terms of stochastic processes. By introducing probability changing dynamically, we have described the fractal natures of river networks stochastically. The dynamical probability depends on the drainage area at a site that is a key dynamical quantity of the system, meanwhile the river network is developed by the probability, which induces dynamical persistency in river flows resulting in the self-affine property shown in real river basins, although the process is a Markovian process with short-term memory.