An Analytical and Numerical Study of Optimal Channel Networks

TL;DR

This paper investigates the Optimal Channel Network model for river systems through analytical and numerical methods, revealing fractal properties, universality classes, and robustness to heterogeneity and rainfall variations.

Contribution

It provides exact calculations of power law exponents, explores effects of disorder, and compares model minima with real river data, advancing understanding of river network morphology.

Findings

Exponents show mean field behavior except at two parameter limits.

Heterogeneity leads to new universality classes.

Model minima statistics resemble real river data.

Abstract

We analyze the Optimal Channel Network model for river networks using both analytical and numerical approaches. This is a lattice model in which a functional describing the dissipated energy is introduced and minimized in order to find the optimal configurations. The fractal character of river networks is reflected in the power law behaviour of various quantities characterising the morphology of the basin. In the context of a finite size scaling Ansatz, the exponents describing the power law behaviour are calculated exactly and show mean field behaviour, except for two limiting values of a parameter characterizing the dissipated energy, for which the system belongs to different universality classes. Two modified versions of the model, incorporating quenched disorder are considered: the first simulates heterogeneities in the local properties of the soil, the second considers the effects of a non-uniform rainfall. In the region of mean field behaviour, the model is shown to be robust to both kinds of perturbations. In the two limiting cases the random rainfall is still irrelevant, whereas the heterogeneity in the soil properties leads to new universality classes. Results of a numerical analysis of the model are reported that confirm and complement the theoretical analysis of the global minimum. The statistics of the local minima are found to more strongly resemble observational data on real rivers.