Articles | Volume 26, issue 21
https://doi.org/10.5194/hess-26-5493-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-5493-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
04 Nov 2022
Technical note |
| 04 Nov 2022
| 04 Nov 2022
Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks
Grey S. Nearing
CORRESPONDING AUTHOR
Google Research, Mountain View, CA, USA
Daniel Klotz
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Jonathan M. Frame
Department of Geological Sciences, University of Alabama, Tuscaloosa, AL, USA
Martin Gauch
LIT AI Lab & Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Oren Gilon
Google Research, Tel Aviv, Israel
Frederik Kratzert
Google Research, Vienna, Austria
Alden Keefe Sampson
Upstream Tech, Alameda, CA, USA
Guy Shalev
Google Research, Tel Aviv, Israel
Sella Nevo
Google Research, Tel Aviv, Israel
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Cited
29 citations as recorded by crossref.
- Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma
- Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al.
- Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method S. Yang et al.
- How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al.
- Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al.
- Improving trans-regional hydrological modelling by combining LSTM with big hydrological data S. Tang et al.
- Does MC-LSTM model improve the reliability of streamflow prediction in human-influenced watersheds? G. Sahu et al.
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider
- Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al.
- Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al.
- Global prediction of extreme floods in ungauged watersheds G. Nearing et al.
- HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al.
- Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting C. Liu et al.
- Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al.
- Metamodeling of a physically based pesticide runoff model with a long short-term memory approach G. Métayer et al.
- GRU-Based Reservoir Operation with Data Integration for Real-Time Flood Control L. Li & K. Jun
- Augmenting observation network design and assimilation frequency in distributed hydrological models: insights from the LISFLOOD-based hydrological data assimilation framework K. Kurugama et al.
- The NextGen Water Resources Modeling Framework: Community Innovation at the Intersection of Hydrologic, Data and Computer Sciences F. Ogden et al.
- Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al.
- From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta
- A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan
- Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al.
- Quantitative Analysis of Input Schemes and Key Variable Contributions in River Runoff Forecasting Models H. Zhang et al.
- Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models A. Jamaat et al.
- Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both worlds V. Grey et al.
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al.
- Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al.
- Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies N. Wang et al.
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al.
29 citations as recorded by crossref.
- Alternate pathway for regional flood frequency analysis in data-sparse region N. Mangukiya & A. Sharma
- Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al.
- Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method S. Yang et al.
- How to enhance hydrological predictions in hydrologically distinct watersheds of the Indian subcontinent? N. Mangukiya et al.
- Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell E. Acuña Espinoza et al.
- Improving trans-regional hydrological modelling by combining LSTM with big hydrological data S. Tang et al.
- Does MC-LSTM model improve the reliability of streamflow prediction in human-influenced watersheds? G. Sahu et al.
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider
- Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al.
- Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment B. Sabzipour et al.
- Global prediction of extreme floods in ungauged watersheds G. Nearing et al.
- HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin F. Kratzert et al.
- Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting C. Liu et al.
- Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama H. Gholizadeh et al.
- Metamodeling of a physically based pesticide runoff model with a long short-term memory approach G. Métayer et al.
- GRU-Based Reservoir Operation with Data Integration for Real-Time Flood Control L. Li & K. Jun
- Augmenting observation network design and assimilation frequency in distributed hydrological models: insights from the LISFLOOD-based hydrological data assimilation framework K. Kurugama et al.
- The NextGen Water Resources Modeling Framework: Community Innovation at the Intersection of Hydrologic, Data and Computer Sciences F. Ogden et al.
- Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model J. Wu et al.
- From gauged to ungauged: Large-scale deep learning rainfall-runoff modelling for reliable streamflow estimation in India's diverse basins S. Barbhuiya & V. Gupta
- A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan
- Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al.
- Quantitative Analysis of Input Schemes and Key Variable Contributions in River Runoff Forecasting Models H. Zhang et al.
- Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models A. Jamaat et al.
- Harnessing the strengths of machine learning and geostatistics to improve streamflow prediction in ungauged basins; the best of both worlds V. Grey et al.
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al.
- Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al.
- Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies N. Wang et al.
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al.
Saved (final revised paper)
Latest update: 16 May 2026
Short summary
When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on
bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.
When designing flood forecasting models, it is necessary to use all available data to achieve...
