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A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

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Kratzert, Frederik, Klotz, Daniel, Hochreiter, Sepp, Nearing, Grey S.·2021·Hydrology and Earth System Sciences
DOI 10.5194/hess-25-2685-2021

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Abstract

A deep learning rainfall–runoff model can take multiple meteorological forcing products as input and learn to combine them in spatially and temporally dynamic ways. This is demonstrated with Long Short-Term Memory networks (LSTMs) trained over basins in the continental US, using the Catchment Attributes and Meteorological data set for Large Sample Studies (CAMELS). Using meteorological input from different data products (North American Land Data Assimilation System, NLDAS, Maurer, and Daymet) in a single LSTM significantly improved simulation accuracy relative to using only individual meteorological products. A sensitivity analysis showed that the LSTM combines precipitation products in different ways, depending on location, and also in different ways for the simulation of different parts of the hydrograph.

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  • Both LSTM and δ models showed better performance when driven by Daymet forcings.

    “Both the LSTM and δ models showed better performance when driven by Daymet forcings”

    From The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment (2023) · cites this paper as Kratzert et al., 2021

    The results discussion (around line 190) identifies DayMet as the best-performing single-forcing LSTM, supporting the LSTM part of the claim. The experimental design (section 2.3) describes LSTM models, but our copy does not report results for δ models, so their claimed improvement with DayMet is unsupported. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Metadata

Authors
Kratzert, F., Klotz, D., Hochreiter, S., & Nearing, G. S.
Affiliations
  • Johannes Kepler University of Linz (AT)
  • Google (United States) (US)
Corresponding author
  • Kratzert, Frederik
  • Klotz, Daniel
ORCID
Venue
Hydrology and Earth System Sciences
Publisher
Copernicus GmbH
Published
2021-05-20
Volume
25
Issue
5
Pages
2685-2703
ISSN
  • 1027-5606 (ISSN-L)
  • 1607-7938
DOI
10.5194/hess-25-2685-2021
OpenAlex
W3024402844
Cited by
213
References
2
Citations by year
2026: 37 · 2025: 40 · 2024: 61 · 2023: 27 · 2022: 27 · 2021: 17
FWCI
10.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Hydrology and earth system sciences (publishedVersion, cc-by)
  • EarthArXiv (California Digital Library) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • EarthArXiv (OSF Preprints) (submittedVersion)
  • EarthArXiv (OSF Preprints) (submittedVersion, cc-by)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders
  • National Aeronautics and Space Administration (80NSSC17K0541)
  • Nvidia
  • European Commission (872172, 956832, HORIZON2020)
  • Austrian Science Fund
  • Nuclear Safety and Security Commission
  • Österreichische Forschungsförderungsgesellschaft (873979, FFG-871302, FFG-872172, FFG-873979)
  • Bundesministerium für Bildung, Wissenschaft und Forschung (LIT-2019-8-YOU-213, LIT-2017-3-YOU-003, LIT-2018-6-YOU-214, LIT-2018-6-YOU-212)
  • Horizon 2020 Framework Programme (872172, 956832)
  • Global Water Futures
  • Janssen Pharmaceuticals
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 › Soil Moisture and Remote Sensing
Keywords
rainfall-runoff modeling, meteorological forcing data, data synergy, long short-term memory networks, camels, NLDAS, Daymet, hydrograph simulation, sensitivity analysis

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