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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- Spatiotemporal characteristics and forecasting of short-term meteorological drought in China Q. Zhang et al. 10.1016/j.jhydrol.2023.129924
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- Application of Machine Learning in Water Resources Management: A Systematic Literature Review F. Ghobadi & D. Kang 10.3390/w15040620
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- Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets C. Mo et al. 10.3390/w16040530
- Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning Y. Liu et al. 10.3390/w16070990
- 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
- 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
- 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
- Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. 10.3390/w15112020
- Improving Daily and Monthly River Discharge Forecasts using Geostatistical Ensemble Modeling F. Rezaei et al. 10.1007/s11269-022-03292-0
- 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
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- 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
- Daily Streamflow Forecasts Based on Cascade Long Short-Term Memory (LSTM) Model over the Yangtze River Basin J. Li & X. Yuan 10.3390/w15061019
- 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
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- 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
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Latest update: 17 Jul 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...
