What Role Does Hydrological Science Play in the Age of Machine Learning?
Open accessAbstract
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.
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References 109 · Cited by 894
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- Authors
- Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., & Gupta, H. V.
- Affiliations
- University of California, Davis (US)
- Johannes Kepler University of Linz (AT)
- Natel Energy (United States) (US)
- Goddard Space Flight Center (US)
- Universidad de Cantabria (ES)
- University of Arizona (US)
- Corresponding author
- Nearing, Grey S.
- ORCID
- Venue
- Water Resources Research
- Publisher
- American Geophysical Union (AGU)
- Published
- 2021-03
- Volume
- 57
- Issue
- 3
- ISSN
- 0043-1397 (ISSN-L)
- 1944-7973
- OpenAlex
- W3092026988
- Cited by
- 894
- References
- 109
- Citations by year
- 2026: 243 · 2025: 178 · 2024: 158 · 2023: 115 · 2022: 122 · 2021: 73 · 2020: 4
- FWCI
- 28.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Water Resources Research (publishedVersion)
- EarthArXiv (California Digital Library) (submittedVersion)
- UCrea (University of Cantabria) (submittedVersion, cc-by)
- Funders
- National Aeronautics and Space Administration (80NSSC17K0541, 80NSSC18K0982)
- University of Alabama
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Environmental Science › Environmental Engineering › Urban Stormwater Management Solutions
- Keywords
- rainfall-runoff modeling, deep learning, machine learning in hydrology, flood forecasting
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