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John Wesley Powell Center for Analysis and Synthesis

Powell Center Working Group Project Information
Exploiting high-resolution topography for advancing the understanding of mass and energy transfer across landscapes: Opportunities, challenges, and needs

Principal Investigator(s):
Paola Passalacqua (UT at Austin)
Patrick Belmont (Utah State University)
Dennis M Staley (Geologic Hazards Team)
Jeffrey D Simley (National Map Program Development)

Ramon Arrowsmith (Arizona State University)
Collin Bode (UC Berkeley)
Bruce Call (Utah State University)
Christopher Crosby (UNAVCO)
Stephen B DeLong (Earthquake Science Center)
Nancy Glenn (Boise State University)
John J Kosovich (Biogeographic Characterization)
Dimitri Lague (Universite de Rennes)
Francis K Rengers (U.S. Geological Survey)
Harish Sangireddy (UT at Austin)
Keelin R Schaffrath (Grand Junction Office, CO Water Science Center)
David Tarboton (Utah State University)
Thad Wasklewicz (East Carolina University)
Joe Wheaton (Utah State University)

Award Date: 2014 
Title: Title: Title:

One of the grand challenges of Earth Surface Science and Natural Resource Management lies in the prediction of mass and energy transfer for large watersheds and landscapes. High resolution topography (lidar) datasets show potential to significantly advance our understanding of hydrologic and geomorphic processes controlling mass and energy transfer because they represent features at the appropriate fine scale on which surface processes operate. While lidar datasets have become readily available across the United States, challenges remain in extracting accurate and objective information relevant for hydrologic and geomorphic research, modeling, and prediction, as well as watershed management. We primarily focus our efforts on river channel networks and nearchannel environments (floodplains and riparian zones), as they often represent the most critical 1% of the landscape for mass and energy transfer. The goals of the proposed project are to (i) evaluate stateof- the-art feature extraction algorithms by testing them on landscapes of different characteristics; (ii) develop guidelines directed to lidar practitioners for filtering and feature extraction as a function of dominant landscape processes; (iii) improve scalability and usability of feature extraction tools to allow their distribution as more user-friendly, national-scale, production-grade tools.

Passalacqua, P., Belmont, P., Staley, D.M., Simley, J.D., Arrowsmith, R.J., Bode, C.A., Crosby, C., DeLong, S.B., Glenn, N.F., Kelly, S.A., Lague, D., Sangireddy, H., Schaffrath, K., Tarboton, D.G, Wasleqicz, T., and Whaton, J.M. (2015). Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review. Earth-Science Reviews 148: 174-193. doi:10.1016/j.earscirev.2015.05.012

Sangireddy, H., R. A. Carothers, C.P. Stark, P. Passalacqua (2016), Controls of climate, topography, vegetation, and lithology on drainage density extracted from high resolution topography data, Journal of Hydrology, 537, 271-282, doi:10.1016/j.jhydrol.2016.02.051.

Sangireddy, H., C.P. Stark, A. Kladzyk, P. Passalacqua (2016), GeoNet: An open source software for the automatic and objective extraction of channel heads, channel network, and channel morphology from high resolution topography data, Environmental Modeling and Software, 83, 58-73, doi:10.1016/j.envsoft.2016.04.026.

USGS Mission Areas:
Water Science
Natural Hazards Science

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