Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-2997-2021
https://doi.org/10.5194/hess-25-2997-2021
Research article
 | 
03 Jun 2021
Research article |  | 03 Jun 2021

Evaluation of random forests for short-term daily streamflow forecasting in rainfall- and snowmelt-driven watersheds

Leo Triet Pham, Lifeng Luo, and Andrew Finley

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Latest update: 24 Feb 2024
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Short summary
Model evaluation metrics suggest that RF performs better in snowmelt-driven watersheds. The largest improvements in forecasts compared to benchmark models are found among rainfall-driven watersheds. RF performance deteriorates with increases in catchment slope and soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study.