Articles | Volume 26, issue 2
https://doi.org/10.5194/hess-26-265-2022
© 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-26-265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jan 2022
Research article |
| 18 Jan 2022
| 18 Jan 2022
Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning
Junjiang Liu
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Key Laboratory of Regional Climate-Environment for Temperate East
Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing
100029, China
Junhan Zeng
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Yang Jiao
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Yong Li
Guangxi Meteorological Disaster Prevention Center, Nanning 530022,
China
Lihua Zhong
Guangxi Meteorological Disaster Prevention Center, Nanning 530022,
China
Ling Yao
Guangxi Guiguan Electric Power Co., Ltd., Nanning 530029, China
Viewed
Total article views: 5,550 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Jul 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 4,276 | 1,181 | 93 | 5,550 | 63 | 72 |
- HTML: 4,276
- PDF: 1,181
- XML: 93
- Total: 5,550
- BibTeX: 63
- EndNote: 72
Total article views: 4,643 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Jan 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,699 | 878 | 66 | 4,643 | 57 | 65 |
- HTML: 3,699
- PDF: 878
- XML: 66
- Total: 4,643
- BibTeX: 57
- EndNote: 65
Total article views: 907 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Jul 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 577 | 303 | 27 | 907 | 6 | 7 |
- HTML: 577
- PDF: 303
- XML: 27
- Total: 907
- BibTeX: 6
- EndNote: 7
Viewed (geographical distribution)
Total article views: 5,550 (including HTML, PDF, and XML)
Thereof 5,207 with geography defined
and 343 with unknown origin.
Total article views: 4,643 (including HTML, PDF, and XML)
Thereof 4,391 with geography defined
and 252 with unknown origin.
Total article views: 907 (including HTML, PDF, and XML)
Thereof 816 with geography defined
and 91 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
46 citations as recorded by crossref.
- Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool D. Hah et al. 10.1016/j.envsoft.2022.105474
- Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al. 10.1016/j.ejrh.2023.101471
- Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting Z. Cui et al. 10.1016/j.jhydrol.2023.129688
- A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
- Enhancing interpretability of AI models in reservoir operation simulation: Exploring and mitigating principal inconsistencies through theory-guided multi-objective artificial neural networks A. Mahmoud et al. 10.1016/j.jhydrol.2024.131618
- Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
- Forecasting short- and medium-term streamflow using stacked ensemble models and different meta-learners F. Granata & F. Di Nunno 10.1007/s00477-024-02760-w
- Hybrid physically based and machine learning model to enhance high streamflow prediction S. López-Chacón et al. 10.1080/02626667.2024.2426720
- A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets J. Liu et al. 10.3390/rs15092447
- Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning Y. Liu et al. 10.3390/w16070990
- Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
- A spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure L. Deng et al. 10.1007/s00477-022-02352-6
- Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models S. Thalli Mani et al. 10.1016/j.ejrh.2022.101190
- Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al. 10.1007/s11269-022-03216-y
- Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. 10.3390/w15112020
- Data‐driven artificial intelligence‐based streamflow forecasting, a review of methods, applications, and tools H. Jahanbani et al. 10.1111/1752-1688.13229
- A new non-stationary standardised streamflow index using the climate indices and the optimal anthropogenic indices as covariates in the Wei River Basin, China M. Ren et al. 10.1016/j.ejrh.2023.101649
- Improving the probabilistic drought prediction with soil moisture information under the ensemble streamflow prediction framework G. Kim et al. 10.1007/s00477-024-02710-6
- Machine learning for postprocessing ensemble streamflow forecasts S. Sharma et al. 10.2166/hydro.2022.114
- A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
- A Two‐Stage Framework for Bias and Reliability Tests of Ensemble Hydroclimatic Forecasts T. Zhao et al. 10.1029/2022WR032568
- Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons B. Qu et al. 10.1088/1755-1315/1087/1/012052
- Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data T. Tu et al. 10.1016/j.jenvman.2024.121044
- Near-term forecasting of water reservoir storage capacities using long short-term memory E. Rohli et al. 10.1017/eds.2023.25
- Adapting subseasonal-to-seasonal (S2S) precipitation forecast at watersheds for hydrologic ensemble streamflow forecasting with a machine learning-based post-processing approach L. Zhang et al. 10.1016/j.jhydrol.2024.130643
- Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model J. Li et al. 10.1016/j.jhydrol.2024.130623
- Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
- Combining Satellite Imagery and a Deep Learning Algorithm to Retrieve the Water Levels of Small Reservoirs J. Wu et al. 10.3390/rs15245740
- Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks J. Farfán & L. Cea 10.1007/s40808-022-01540-1
- Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China P. Lu et al. 10.3390/foods12030542
- Spatiotemporal characteristics and forecasting of short-term meteorological drought in China Q. Zhang et al. 10.1016/j.jhydrol.2023.129924
- Impact assessment of cascade freshwater reservoir using the ecological security assessment (ESA) model across a four-year timescale J. Yin et al. 10.1016/j.ecolind.2023.110907
- Application of Machine Learning in Water Resources Management: A Systematic Literature Review F. Ghobadi & D. Kang 10.3390/w15040620
- Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al. 10.3390/w16040530
- Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
- Cascade Reservoirs: An Exploration of Spatial Runoff Storage Sites for Water Harvesting and Mitigation of Climate Change Impacts, Using an Integrated Approach of GIS and Hydrological Modeling A. Soomro et al. 10.3390/su142013538
- A long lead time forecast model applying an ensemble approach for managing the great Karun multi-reservoir system E. Mostaghimzadeh et al. 10.1007/s13201-023-01924-3
- Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling F. Rezaei et al. 10.1007/s11269-022-03292-0
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Daily Runoff Forecasting Using Novel Optimized Machine Learning Methods P. Parisouj et al. 10.1016/j.rineng.2024.103319
- Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation F. Zhou et al. 10.3390/rs15051395
- Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
- Daily Streamflow Forecasts Based on Cascade Long Short-Term Memory (LSTM) Model over the Yangtze River Basin J. Li & X. Yuan 10.3390/w15061019
- Climate warming outweighs vegetation greening in intensifying flash droughts over China M. Zhang et al. 10.1088/1748-9326/ac69fb
- Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications Y. Tang et al. 10.1007/s11600-022-00877-6
- Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction S. He et al. 10.1016/j.jhydrol.2022.127936
46 citations as recorded by crossref.
- Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool D. Hah et al. 10.1016/j.envsoft.2022.105474
- Long-lead daily streamflow forecasting using Long Short-Term Memory model with different predictors J. Li et al. 10.1016/j.ejrh.2023.101471
- Exploration of dual-attention mechanism-based deep learning for multi-step-ahead flood probabilistic forecasting Z. Cui et al. 10.1016/j.jhydrol.2023.129688
- A state-of-the-art review of long short-term memory models with applications in hydrology and water resources Z. Feng et al. 10.1016/j.asoc.2024.112352
- Enhancing interpretability of AI models in reservoir operation simulation: Exploring and mitigating principal inconsistencies through theory-guided multi-objective artificial neural networks A. Mahmoud et al. 10.1016/j.jhydrol.2024.131618
- Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method Z. Cui et al. 10.5194/hess-28-2809-2024
- Forecasting short- and medium-term streamflow using stacked ensemble models and different meta-learners F. Granata & F. Di Nunno 10.1007/s00477-024-02760-w
- Hybrid physically based and machine learning model to enhance high streamflow prediction S. López-Chacón et al. 10.1080/02626667.2024.2426720
- A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets J. Liu et al. 10.3390/rs15092447
- Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning Y. Liu et al. 10.3390/w16070990
- Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset A. Tounsi et al. 10.1007/s00521-023-08922-1
- A spatiotemporal graph convolution-based model for daily runoff prediction in a river network with non-Euclidean topological structure L. Deng et al. 10.1007/s00477-022-02352-6
- Enhanced streamflow simulations using nudging based optimization coupled with data-driven and hydrological models S. Thalli Mani et al. 10.1016/j.ejrh.2022.101190
- Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al. 10.1007/s11269-022-03216-y
- Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. 10.3390/w15112020
- Data‐driven artificial intelligence‐based streamflow forecasting, a review of methods, applications, and tools H. Jahanbani et al. 10.1111/1752-1688.13229
- A new non-stationary standardised streamflow index using the climate indices and the optimal anthropogenic indices as covariates in the Wei River Basin, China M. Ren et al. 10.1016/j.ejrh.2023.101649
- Improving the probabilistic drought prediction with soil moisture information under the ensemble streamflow prediction framework G. Kim et al. 10.1007/s00477-024-02710-6
- Machine learning for postprocessing ensemble streamflow forecasts S. Sharma et al. 10.2166/hydro.2022.114
- A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting G. Papacharalampous & H. Tyralis 10.3389/frwa.2022.961954
- A Two‐Stage Framework for Bias and Reliability Tests of Ensemble Hydroclimatic Forecasts T. Zhao et al. 10.1029/2022WR032568
- Analysis of statistical post-processing methods for multi-model ensemble runoff forecasts in flood seasons B. Qu et al. 10.1088/1755-1315/1087/1/012052
- Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data T. Tu et al. 10.1016/j.jenvman.2024.121044
- Near-term forecasting of water reservoir storage capacities using long short-term memory E. Rohli et al. 10.1017/eds.2023.25
- Adapting subseasonal-to-seasonal (S2S) precipitation forecast at watersheds for hydrologic ensemble streamflow forecasting with a machine learning-based post-processing approach L. Zhang et al. 10.1016/j.jhydrol.2024.130643
- Improving cascade reservoir inflow forecasting and extracting insights by decomposing the physical process using a hybrid model J. Li et al. 10.1016/j.jhydrol.2024.130623
- Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
- Combining Satellite Imagery and a Deep Learning Algorithm to Retrieve the Water Levels of Small Reservoirs J. Wu et al. 10.3390/rs15245740
- Improving the predictive skills of hydrological models using a combinatorial optimization algorithm and artificial neural networks J. Farfán & L. Cea 10.1007/s40808-022-01540-1
- Informer-Based Safety Risk Prediction of Heavy Metals in Rice in China P. Lu et al. 10.3390/foods12030542
- Spatiotemporal characteristics and forecasting of short-term meteorological drought in China Q. Zhang et al. 10.1016/j.jhydrol.2023.129924
- Impact assessment of cascade freshwater reservoir using the ecological security assessment (ESA) model across a four-year timescale J. Yin et al. 10.1016/j.ecolind.2023.110907
- Application of Machine Learning in Water Resources Management: A Systematic Literature Review F. Ghobadi & D. Kang 10.3390/w15040620
- Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al. 10.3390/w16040530
- Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model D. Lee & K. Ahn 10.1016/j.jenvman.2024.122114
- Cascade Reservoirs: An Exploration of Spatial Runoff Storage Sites for Water Harvesting and Mitigation of Climate Change Impacts, Using an Integrated Approach of GIS and Hydrological Modeling A. Soomro et al. 10.3390/su142013538
- A long lead time forecast model applying an ensemble approach for managing the great Karun multi-reservoir system E. Mostaghimzadeh et al. 10.1007/s13201-023-01924-3
- Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling F. Rezaei et al. 10.1007/s11269-022-03292-0
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Daily Runoff Forecasting Using Novel Optimized Machine Learning Methods P. Parisouj et al. 10.1016/j.rineng.2024.103319
- Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation F. Zhou et al. 10.3390/rs15051395
- Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang 10.3390/w16152199
- Daily Streamflow Forecasts Based on Cascade Long Short-Term Memory (LSTM) Model over the Yangtze River Basin J. Li & X. Yuan 10.3390/w15061019
- Climate warming outweighs vegetation greening in intensifying flash droughts over China M. Zhang et al. 10.1088/1748-9326/ac69fb
- Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications Y. Tang et al. 10.1007/s11600-022-00877-6
- Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction S. He et al. 10.1016/j.jhydrol.2022.127936
Latest update: 21 Nov 2024
Short summary
Hourly streamflow ensemble forecasts with the CSSPv2 land surface model and ECMWF meteorological forecasts reduce both the probabilistic and deterministic forecast error compared with the ensemble streamflow prediction approach during the first week. The deterministic forecast error can be further reduced in the first 72 h when combined with the long short-term memory (LSTM) deep learning method. The forecast skill for LSTM using only historical observations drops sharply after the first 24 h.
Hourly streamflow ensemble forecasts with the CSSPv2 land surface model and ECMWF meteorological...
