Articles | Volume 25, issue 6
Hydrol. Earth Syst. Sci., 25, 2997–3015, 2021
https://doi.org/10.5194/hess-25-2997-2021
Hydrol. Earth Syst. Sci., 25, 2997–3015, 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 et al.

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Cited articles

Adamowski, J. F.: Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis, J. Hydrol., 353, 247–266, 2008. a
Altman, D. G. and Bland, J. M.: Statistics notes Variables and parameters, Brit. Med. J., 318, 1667, 1999. a
Aubert, D., Loumagne, C., and Oudin, L.: Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model, J. Hydrol., 280, 145–161, 2003. a
Bernard, S., Heutte, L., and Adam, S.: Influence of hyperparameters on random forest accuracy, in: International Workshop on Multiple Classifier Systems, Springer, Berlin, Heidelberg, 171–180, 2009. a, b
Boyle, D. P., Gupta, H. V., and Sorooshian, S.: Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods, Water Resour. Res., 36, 3663–3674, 2000. a
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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.