Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-2997-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2997-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of random forests for short-term daily streamflow forecasting in rainfall- and snowmelt-driven watersheds
Leo Triet Pham
CORRESPONDING AUTHOR
Department of Forestry, Michigan State University, East Lansing, Michigan, USA
Lifeng Luo
Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, USA
Andrew Finley
Department of Forestry, Michigan State University, East Lansing, Michigan, USA
Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, USA
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Latest update: 18 Mar 2024
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.
Model evaluation metrics suggest that RF performs better in snowmelt-driven watersheds. The...