Randomness and Self-similarity in the Topology of River Networks and its Implications for predicting scaling in floods (Invited)
Abstract
Properties of randomness (Random model) and mean self-similarity (Tokunaga model) in the topology of river networks have been investigated independently of each other for over forty years. It has been observed that the random model does not predict the observed topology of river networks but Tokunaga model does. However, it does not include randomness that is an important feature of river networks. A new class of river network models called random self-similar networks (RSN) that combines self-similarity and randomness has been introduced to understand important topological features observed in river networks. We will present new results from a set of 30 basins located across the continental United States and representing a wide range of hydroclimatic variability that support the hypothesis of statistical self-similarity postulated by the RSN model. The generators of the RSN model obey a geometric distribution, and self-similarity holds in a statistical sense in 26 of these 30 basins. We will describe how topological self-similarity in river networks provides a theoretical framework to understand observed scaling (power laws) in peak flows by solving mass and momentum conservation equations that describe river-flow generation and its transport in a river network for a rainfall-runoff event.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2010
- Bibcode:
- 2010AGUFMNG42A..04G
- Keywords:
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- 1821 HYDROLOGY / Floods;
- 1874 HYDROLOGY / Ungaged basins;
- 4440 NONLINEAR GEOPHYSICS / Fractals and multifractals;
- 4475 NONLINEAR GEOPHYSICS / Scaling: spatial and temporal