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See UWRL webpage for selected recent research & projects

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Predicting channel-reach morphology at large scales using Machine Learning

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Field data collection   ->    Classification   ->    Prediction

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Learn more about the prediction of reach-scale geomorphic channel types in this e-poster and explore the interactive results.

 

 

 

 

 

 

 

Guillon et al 2020

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Hydrologic classification and modeling 

The flow regime acts as a major control on geomorphic processes and river ecosystems.

We have distinguished natural stream classes and alteration classes that are being used to organize regional environmental water management efforts for California.

Check out our blog post and publications.

Download the KMZ of the hydrologic classification:

Lane et al 2017; 2018

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The Functional Flows Calculator

The Functional Flows Calculator is a web tool for visualizing, downloading, and exploring unimpaired streamflow patterns, including hydrologic classes, reference hydrographs, and annual functional flow metrics. The time series signal processing algorithm code and GUI are publicly available at associated links.

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Nested longitudinal variability in channel width and depth have rarely been included in channel classifications. However, variability in topographic features of rivers, in conjunction with sediment supply and discharge, produces a mosaic of channel forms that provides unique habitats for sensitive aquatic species. We are investigating the utility of topographic variability attributes in distinguishing channel types and dominant geomorphic processes. Initial results indicate that incorporating variance in channel classification provides a quantitative basis for interpreting nonuniform as well as uniform geomorphic processes, which can improve our ability to distinguish linked channel forms and processes of geomorphic and ecological significance. These efforts are now being applied throughout the State of California through partnerships with several academic entities. 

Quantifying and synthesizing riverscape topographic variability 

Lane et al 2017

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© 2022 by Belize A. Lane

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