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Grey S. Nearing, Benjamin L. Ruddell, Martyn P. Clark, Bart Nijssen, and Christa Peters-Lidard

the purpose of this paper is not to argue for using statistical, data-driven, or regression models in place of physically based or process-based models for operational forecasting of terrestrial hydrological systems. We do not want to do this because of the potential for nonstationarity—some type of mechanistic understanding of the system is necessary to predict under changing conditions ( Milly et al. 2008 ). That being said, we cannot ignore the fact that regression models regularly outperform

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Maik Renner, Axel Kleidon, Martyn Clark, Bart Nijssen, Marvin Heidkamp, Martin Best, and Gab Abramowitz

forecast system . J. Hydrometeor. , 10 , 623 – 643 , https://doi.org/10.1175/2008JHM1068.1 . 10.1175/2008JHM1068.1 Best , M. J. , P. M. Cox , and D. Warrilow , 2005 : Determining the optimal soil temperature scheme for atmospheric modelling applications . Bound.-Layer Meteor. , 114 , 111 – 142 , https://doi.org/10.1007/s10546-004-5075-3 . 10.1007/s10546-004-5075-3 Best , M. J. , and Coauthors , 2011 : The Joint UK Land Environment Simulator (JULES), model description - Part 1

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