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oai:mountainscholar.org:10217/2357972025-12-30T03:26:56Zcom_10217_100532com_10217_100000com_10217_100369com_10217_100303col_10217_100538col_10217_100381

Mejía, Alfonso I., author Niemann, Jeffrey D., advisor Wohl, Ellen, committee member Ramírez, Jorge A., committee member 2022-09-28T21:43:01Z 2022-09-28T21:43:01Z 2006 https://hdl.handle.net/10217/235797 https://doi.org/10.25675/3.018636 Covers not scanned. Geomorphologists have long recognized that the geometry of channel network planforms can vary significantly between regions depending on the local lithologic and tectonic conditions. This tendency has led to the classification of channel networks using terms such as dendritic, parallel, pinnate, rectangular, and trellis. Unfortunately, available classification methods are scale dependent and have no connection to an underlying quantitative theory of drainage network geometry or evolution. In this study, a new method is developed to classify drainage networks based on their deviations from self-similarity. The planform geometry of dendritic networks is known to be self-similar. It is our hypothesis that parallel, pinnate, rectangular, and trellis networks correspond to distinct deviations from this self-similarity. To identify such deviations, three measures of channel networks are applied to ten networks from each classification. These measures are the incremental accumulation of drainage area along channels, the irregularity of channel courses, and the angles formed by merging channels. The results confirm and characterize the self-similarity of dendritic networks. Parallel and pinnate networks are found to be self-affine with Hurst exponents around 0.8 and 0.7, respectively. Rectangular and trellis networks are approximately self-similar although deviations from self-similarity are observed. Rectangular networks have more sinuous channels than dendritic networks across all scales, and trellis networks have a slower rate of area accumulation than dendritic networks across all scales. Such observations are used to build and test classification trees, which are found to perform well in classifying networks. masters theses English eng Colorado State University. Libraries Catalog record number (MMS ID): 991023622219703361 GB401.4.M555 2006 2000-2019 Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Geomorphology Drainage Hydrologic models Identification and characterization of dendritic, parallel, pinnate, rectangular and trellis networks based on deviations from planform self-similarity Text StillImage Master of Science (M.S.) Masters Civil and Environmental Engineering Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access