Coding random self-similar river networks and calculating geometric distances: 1. General methodology / Codage de réseaux hydrographiques auto-similaires aléatoires et calcul de distances géométriques: 1. Méthodologie générale

Hydrologic analysis of urban drainage networks often encounters a number of issues, including data acquisition and preparation for modelling, which can be costly and time‐consuming processes. Moreover, it can get more challenging with missing data and complex loops inside networks. In this article, Gibbs’ model is applied to urban drainage networks to investigate the possibility of replacing an actual existing urban drainage network in terms of the shape and peak flow of the hydrographs at the outlet. The characteristic network configuration is given as a value of a parameter β of Gibbs’ model. Instead of the actual network, stochastic networks from Monte‐Carlo simulation are utilized to obtain a synthetic width function from the generated networks, and runoff hydrographs are estimated based on it. The results show that the synthetic width function and the resulting hydrographs obtained from the networks simulated by Gibbs’ model are close to those from the actual network. The result also shows that even the behaviour of a looped network can be approximated by equivalent dendritic networks generated by Gibbs’ model. The applicability of a stochastic network model in urban catchment implies a complement to modelling approaches in case of data unavailability. Moreover, the network property (β) is utilized not only to estimate the discharge hydrograph of a catchment but also as a key link to evaluate the effect from rainstorm movement in urban catchments. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Testing statistical self‐similarity in the topology of river networks

Mantilla

Troutman

Gupta

2010

J. Geophys. Res.

View full text Add to dashboard Cite

[1] Recent work has demonstrated that the topological properties of real river networks deviate significantly from predictions of Shreve's random model. At the same time the property of mean self-similarity postulated by Tokunaga's model is well supported by data. Recently, a new class of network model called random self-similar networks (RSN) that combines self-similarity and randomness has been introduced to replicate important topological features observed in real river networks. We investigate if the hypothesis of statistical self-similarity in the RSN model is supported by data on a set of 30 basins located across the continental United States that encompass a wide range of hydroclimatic variability. We demonstrate that the generators of the RSN model obey a geometric distribution, and self-similarity holds in a statistical sense in 26 of these 30 basins. The parameters describing the distribution of interior and exterior generators are tested to be statistically different and the difference is shown to produce the well-known Hack's law. The inter-basin variability of RSN parameters is found to be statistically significant. We also test generator dependence on two climatic indices, mean annual precipitation and radiative index of dryness. Some indication of climatic influence on the generators is detected, but this influence is not statistically significant with the sample size available. Finally, two key applications of the RSN model to hydrology and geomorphology are briefly discussed.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Coding random self-similar river networks and calculating geometric distances: 1. General methodology / Codage de réseaux hydrographiques auto-similaires aléatoires et calcul de distances géométriques: 1. Méthodologie générale

Cited by 4 publications

References 12 publications

Current awareness

Current awareness

Application of Gibbs’ model to urban drainage networks: a case study in southwestern Chicago, USA

Testing statistical self‐similarity in the topology of river networks

Contact Info

Product

Resources

About