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Random Forest models to estimate bankfull and low flow channel widths and depths across the conterminous United States. | LitMetric

Random Forest models to estimate bankfull and low flow channel widths and depths across the conterminous United States.

J Am Water Resour Assoc

Oak Ridge Institute for Science and Education c/o Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division (Doyle), U.S. Environmental Protection Agency, Corvallis, Oregon USA; Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division (Hill, Leibowitz, Ebersole), U.S. Environmental Protection Agency, Corvallis, Oregon, USA.

Published: October 2023

AI Article Synopsis

Article Abstract

Channel dimensions (width and depth) at varying flows influence a host of instream ecological processes, as well as habitat and biotic features; they are a major consideration in stream habitat restoration and instream flow assessments. Models of widths and depths are often used to assess climate change vulnerability, develop endangered species recovery plans, and model water quality. However, development and application of such models require specific skillsets and resources. To facilitate acquisition of such estimates, we created a dataset of modeled channel dimensions for perennial stream segments across the conterminous U.S. We used random forest models to predict wetted width, thalweg depth, bankfull width, and bankfull depth from several thousand field measurements of the National Rivers and Streams Assessment. Observed channel widths varied from <5 m to >2000 m and depths varied from <2 m to >125 m. Metrics of watershed area, runoff, slope, land use, and more were used as model predictors. The models had high pseudo R-squared values (0.70 to 0.91) and median absolute errors within ±6% to ±21% of the interquartile range of measured values across ten stream orders. Predicted channel dimensions can be joined to 1.1 million stream segments of the 1:100K resolution National Hydrography Dataset Plus (version 2.1). These predictions, combined with a rapidly growing body of nationally available data, will further enhance our ability to study and protect aquatic resources.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631553PMC
http://dx.doi.org/10.1111/1752-1688.13116DOI Listing

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