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
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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
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- 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
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- 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
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- 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
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Latest update: 13 Dec 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...
