Articles | Volume 27, issue 2
https://doi.org/10.5194/hess-27-501-2023
© Author(s) 2023. 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-27-501-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jan 2023
Research article |
| 26 Jan 2023
| 26 Jan 2023
The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
Marc F. P. Bierkens
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
Deltares, Daltonlaan 600, 3584 BK Utrecht, the Netherlands
Vincent Beijk
Rijkswaterstaat, Water, Verkeer en Leefomgeving, Griffioenlaan 2, Utrecht, the Netherlands
Niko Wanders
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
Viewed
Total article views: 3,274 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 31 Mar 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,495 | 714 | 65 | 3,274 | 58 | 58 |
- HTML: 2,495
- PDF: 714
- XML: 65
- Total: 3,274
- BibTeX: 58
- EndNote: 58
Total article views: 2,154 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 26 Jan 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,727 | 380 | 47 | 2,154 | 52 | 52 |
- HTML: 1,727
- PDF: 380
- XML: 47
- Total: 2,154
- BibTeX: 52
- EndNote: 52
Total article views: 1,120 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 31 Mar 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 768 | 334 | 18 | 1,120 | 6 | 6 |
- HTML: 768
- PDF: 334
- XML: 18
- Total: 1,120
- BibTeX: 6
- EndNote: 6
Viewed (geographical distribution)
Total article views: 3,274 (including HTML, PDF, and XML)
Thereof 3,152 with geography defined
and 122 with unknown origin.
Total article views: 2,154 (including HTML, PDF, and XML)
Thereof 2,115 with geography defined
and 39 with unknown origin.
Total article views: 1,120 (including HTML, PDF, and XML)
Thereof 1,037 with geography defined
and 83 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
- FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America L. Arnal et al. 10.5194/hess-28-4127-2024
- Hydrological regimes explain the seasonal predictability of streamflow extremes Y. Du et al. 10.1088/1748-9326/acf678
- A framework for incorporating rainfall data into a flooding digital twin A. Green et al. 10.1016/j.jhydrol.2025.132893
- Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
- Machine learning for predicting shallow groundwater levels in urban areas A. LaBianca et al. 10.1016/j.jhydrol.2024.130902
- Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation X. Zhang et al. 10.1162/dint_a_00221
- Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
- Machine learning and global vegetation: random forests for downscaling and gap filling B. van Jaarsveld et al. 10.5194/hess-28-2357-2024
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
- Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al. 10.3390/w17060907
- A Digital Twin Dam and Watershed Management Platform D. Park & H. You 10.3390/w15112106
- Holistic uncertainty quantification and attribution for real-time seasonal streamflow predictions: Insights from input, parameter and initial condition L. Liu et al. 10.1016/j.ejrh.2025.102427
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models S. Hauswirth et al. 10.3389/frwa.2023.1108108
13 citations as recorded by crossref.
- Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
- FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America L. Arnal et al. 10.5194/hess-28-4127-2024
- Hydrological regimes explain the seasonal predictability of streamflow extremes Y. Du et al. 10.1088/1748-9326/acf678
- A framework for incorporating rainfall data into a flooding digital twin A. Green et al. 10.1016/j.jhydrol.2025.132893
- Operational low-flow forecasting using LSTMs J. Deng et al. 10.3389/frwa.2023.1332678
- Machine learning for predicting shallow groundwater levels in urban areas A. LaBianca et al. 10.1016/j.jhydrol.2024.130902
- Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation X. Zhang et al. 10.1162/dint_a_00221
- Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
- Machine learning and global vegetation: random forests for downscaling and gap filling B. van Jaarsveld et al. 10.5194/hess-28-2357-2024
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
- Long Short-Term Memory (LSTM) Networks for Accurate River Flow Forecasting: A Case Study on the Morava River Basin (Serbia) I. Leščešen et al. 10.3390/w17060907
- A Digital Twin Dam and Watershed Management Platform D. Park & H. You 10.3390/w15112106
- Holistic uncertainty quantification and attribution for real-time seasonal streamflow predictions: Insights from input, parameter and initial condition L. Liu et al. 10.1016/j.ejrh.2025.102427
2 citations as recorded by crossref.
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models S. Hauswirth et al. 10.3389/frwa.2023.1108108
Latest update: 16 May 2025
Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
Forecasts on water availability are important for water managers. We test a hybrid framework...
