ArticleJournal of the American Water Resources Association2023
Random Forest models to estimate bankfull and low flow channel widths and depths across the conterminous United States.
Article in Journal of the American Water Resources Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Interpretable machine learning for predicting rating curve parameters using channel geometry and hydrological attributes across the United States.Scientific reports · 2025Article
- Article
- Assessing riparian functioning condition for improved ecosystem services: A case study of the Back Creek watershed (Virginia, USA).Journal of environmental management · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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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Registered trials
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