Catchment topography : improving hydrologic predictions with lidar analysis

dc.contributor.advisorPassalacqua, Paola
dc.contributor.committeeMemberMaidment, David R
dc.contributor.committeeMemberHodges, Ben R
dc.contributor.committeeMemberJohnson, Joel P
dc.contributor.committeeMemberLiljestrand, Howard
dc.creatorSangireddy, Harish 2015
dc.description.abstractChannels, floodplains, hillslopes, and ridges are characteristic topographic features of landscapes around us. These topographic features occur at a variety of spatial scales. Climate, vegetation, soil type, and terrain characteristics control the shape of a catchment and of the channel network. Increasingly extreme and unpredictable weather patterns demand for better prediction of catchment hydrologic responses. The key to predict catchment response lies in understanding the topographic patterns and how they are influenced by the underlying processes, climate, and anthropogenic modifications. With the availability of high resolution topographic data, the characterization of topographic features at the scales relevant to hydrology and geomorphic processes is now possible. Light Detection and Ranging (lidar) digital terrain models (DTMs) (meter and sub-meter resolution) allow us to accurately quantify patterns of landscape dissection (e.g., drainage density), channel head locations, surface runoff patterns and hillslope length scales. Coarse resolution datasets (30- 100m), such as Shuttle Radar Topographic Mission (SRTM), fail to capture local variability at relevant process scales and only resolve large scale topographic patterns. As we continue to collect high resolution data there is a growing need to develop new methods and algorithms to objectively extract topographic features, such as channels, and identify metrics able to characterize topography over large areas. The goals of the research presented here are to (i) identify the signature of climate, vegetation, topography and lithology on channel patterns, (ii) define new metrics to quantify catchment topography across a range of scales, (iii) improve existing feature extraction techniques for channel networks to upscale them to handle larger catchments, and, (iv) develop feature extraction tools for urban and highly engineered setting. The research will deepen our understanding about the effects of climate on channel patterns across a range of scales; the identification of new metrics will help characterize landscapes in an objective manner; improvement of existing feature extraction techniques to handle large catchments will help in designing best management practices for watersheds through distributed mapping of topographic attributes such as slope, curvature, and accumulation area; feature extraction in urban and engineered settings will improve the analysis of watersheds modified by humans.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.subjectChannel networks
dc.subjectDrainage density
dc.subjectFeature extraction
dc.subjectCatchment topography
dc.subjectHydrologic predictions
dc.subjectLidar analysis
dc.subjectCatchment response
dc.subjectTopographic patterns
dc.subjectHigh resolution topographic data
dc.subjectLight Detection and Ranging
dc.subjectDigital terrain models
dc.subjectEffects of climate on channel patterns
dc.titleCatchment topography : improving hydrologic predictions with lidar analysis
dc.type.materialtext, Architectural, and Environmental Engineering Engineering University of Texas at Austin of Philosophy

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