Articles | Volume 28, issue 13
https://doi.org/10.5194/hess-28-2871-2024
© Author(s) 2024. 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-28-2871-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jul 2024
Research article |
| 04 Jul 2024
| 04 Jul 2024
A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks
Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen 1350, Denmark
Julian Koch
Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen 1350, Denmark
Simon Stisen
Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen 1350, Denmark
Lars Troldborg
Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen 1350, Denmark
Raphael J. M. Schneider
Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen 1350, Denmark
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Cited
15 citations as recorded by crossref.
- Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al. 10.1016/j.envsoft.2025.106648
- Performance of multiple tree-based models for estimating daily streamflow in the Cau River Basin T. Tuan Thach 10.2166/wpt.2025.077
- Physics-Informed Deep Learning for Karst Spring Prediction: Integrating Variational Mode Decomposition and Long Short-Term Memory with Attention L. Zhao et al. 10.3390/w17142043
- Long-term forecasting of monthly reservoir inflow using deep and machine-learning-based algorithms B. Ghenaati et al. 10.1016/j.engappai.2025.112175
- Data-driven model as a post-process for daily streamflow prediction in ungauged basins J. Choi & S. Kim 10.1016/j.heliyon.2025.e42512
- Hydrological Predictive Modeling for Indian River: Leveraging LSTM and GRU Attention Mechanisms S. Lachure & A. Tiwari 10.1007/s42979-025-04289-3
- A novel framework for multi-step water level predicting by spatial–temporal deep learning models based on integrated physical models S. Zhang et al. 10.1016/j.jhydrol.2025.133683
- Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. 10.1016/j.jhydrol.2025.134270
- Climate Change Impacts on River Hydraulics: A Global Synthesis of Hydrological Shifts, Ecological Consequences, and Adaptive Strategies B. Nile et al. 10.1007/s41101-025-00375-y
- Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
- Precipitation recycling impacts on runoff in arid regions of China and Mongolia: a machine learning approach R. Li et al. 10.1080/02626667.2025.2456211
- Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach L. Deng et al. 10.1016/j.jhydrol.2025.132895
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. 10.5194/essd-17-1551-2025
- Multiple data-driven approaches for estimating daily streamflow in the Kone River basin, Vietnam T. Thach 10.1007/s12145-024-01390-8
- Hybrid approaches enhance hydrological model usability for local streamflow prediction Y. Du & I. Pechlivanidis 10.1038/s43247-025-02324-y
13 citations as recorded by crossref.
- Exploring Kolmogorov-Arnold neural networks for hybrid and transparent hydrological modeling X. Jing et al. 10.1016/j.envsoft.2025.106648
- Performance of multiple tree-based models for estimating daily streamflow in the Cau River Basin T. Tuan Thach 10.2166/wpt.2025.077
- Physics-Informed Deep Learning for Karst Spring Prediction: Integrating Variational Mode Decomposition and Long Short-Term Memory with Attention L. Zhao et al. 10.3390/w17142043
- Long-term forecasting of monthly reservoir inflow using deep and machine-learning-based algorithms B. Ghenaati et al. 10.1016/j.engappai.2025.112175
- Data-driven model as a post-process for daily streamflow prediction in ungauged basins J. Choi & S. Kim 10.1016/j.heliyon.2025.e42512
- Hydrological Predictive Modeling for Indian River: Leveraging LSTM and GRU Attention Mechanisms S. Lachure & A. Tiwari 10.1007/s42979-025-04289-3
- A novel framework for multi-step water level predicting by spatial–temporal deep learning models based on integrated physical models S. Zhang et al. 10.1016/j.jhydrol.2025.133683
- Catchment features-based interpretation of performance of the conceptual hydrological and deep learning models using large sample hydrologic data D. Sourya et al. 10.1016/j.jhydrol.2025.134270
- Climate Change Impacts on River Hydraulics: A Global Synthesis of Hydrological Shifts, Ecological Consequences, and Adaptive Strategies B. Nile et al. 10.1007/s41101-025-00375-y
- Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
- Precipitation recycling impacts on runoff in arid regions of China and Mongolia: a machine learning approach R. Li et al. 10.1080/02626667.2025.2456211
- Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach L. Deng et al. 10.1016/j.jhydrol.2025.132895
- CAMELS-DK: hydrometeorological time series and landscape attributes for 3330 Danish catchments with streamflow observations from 304 gauged stations J. Liu et al. 10.5194/essd-17-1551-2025
2 citations as recorded by crossref.
Latest update: 06 Oct 2025
Short summary
We developed hybrid schemes to enhance national-scale streamflow predictions, combining long short-term memory (LSTM) with a physically based hydrological model (PBM). A comprehensive evaluation of hybrid setups across Denmark indicates that LSTM models forced by climate data and catchment attributes perform well in many regions but face challenges in groundwater-dependent basins. The hybrid schemes supported by PBMs perform better in reproducing long-term streamflow behavior and extreme events.
We developed hybrid schemes to enhance national-scale streamflow predictions, combining long...
