Opinion paper discussing the future of Hydrology, especially in the context of recent developments in Machine Learning
Abstract
This paper is derived from a keynote talk given at the Google’s 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall-runoff simulation indicate that there is significantly more information in large-scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence-based preferences for models based on a certain type of “process understanding” that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished.
Paper
Citation
@article{nearing2021role,
author = {Nearing, G. S. and Kratzert, F. and Sampson, A. K. and Pelissier, C. S. and Klotz, D. and Frame, J. M. and Prieto, C. and Gupta, H. V.},
title = {What Role Does Hydrological Science Play in the Age of Machine Learning?},
journal = {Water Resources Research},
volume = {57},
number = {3},
pages = {e2020WR028091},
doi = {https://doi.org/10.1029/2020WR028091},
year = {2021}
}