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Cited by 633Hydrological Forecasting Using AIHydrology and Watershed Management StudiesStock Market Forecasting Methods

Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales

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Feng, Dapeng, Fang, Kuai, Shen, Chaopeng·2020·Water Resources Research
DOI 10.1029/2019wr026793
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Abstract

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short‐term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental‐scale median Nash‐Sutcliffe Efficiency coefficient value of 0.86. Integrating moving‐average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow‐dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation‐energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater‐dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high‐aridity basins with 1‐day flash peaks. Despite this limitation, there is much promise for a deep‐learning‐based forecast paradigm due to its performance, automation, efficiency, and flexibility.

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Cited by

Sorted by citations

Authors
Feng, D., Fang, K., & Shen, C.
Affiliations
Pennsylvania State University (US)
Corresponding author
Shen, Chaopeng
ORCID
Venue
Water Resources Research
Publisher
American Geophysical Union (AGU)
Published
2020-09
Volume
56
Issue
9
ISSN
  • 0043-1397 (ISSN-L)
  • 1944-7973
DOI
10.1029/2019wr026793
OpenAlex
W3038342057
Cited by
633
References
3
Citations by year
2026: 121 · 2025: 125 · 2024: 128 · 2023: 102 · 2022: 98 · 2021: 49 · 2020: 10
FWCI
22.62
Type
journal article
Language
en
Open access
Licence
Locations
  • Water Resources Research (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, other-oa)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (EAR#1832294)
  • U.S. Department of Energy (DE‐SC0016605)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
  • Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Keywords
streamflow forecasting, long short-term memory networks, data integration, continental-scale hydrology, peak flow estimation, snow-dominated basins, flash floods, arid basins, prairie potholes, Great Lakes Basin

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