Articles | Volume 19, issue 8
https://doi.org/10.5194/hess-19-3695-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-3695-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Aug 2015
Research article |
| 27 Aug 2015
Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework
Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway
A. Sharma
School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
R. Mehrotra
School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
K. Alfredsen
Department of Hydraulic and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway
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- Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting M. Li et al. 10.5194/hess-20-3561-2016
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- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
- Two-stage robust unit commitment with the cascade hydropower stations retrofitted with pump stations C. Ju et al. 10.1016/j.apenergy.2023.120675
- Improving efficiencies of flood forecasting during lead times: an operational method and its application in the Baiyunshan Reservoir P. Liu et al. 10.2166/nh.2018.051
- The future of forecasting for renewable energy C. Sweeney et al. 10.1002/wene.365
- Improving hydropower inflow forecasts by assimilating snow data J. Magnusson et al. 10.2166/nh.2020.025
- Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm X. Liu et al. 10.3390/w15152700
- Estimating Reservoir Inflow and Outflow From Water Level Observations Using Expert Knowledge: Dealing With an Ill‐Posed Water Balance Equation in Reservoir Management J. Song et al. 10.1029/2020WR028183
- Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year F. Delgado-Ramos & C. Hervás-Gámez 10.3390/w10081038
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- Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting Z. Liu et al. 10.1007/s11269-016-1275-0
- AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts X. Zha et al. 10.1061/(ASCE)HE.1943-5584.0001955
- Application of periodic autoregressive process to the modeling of the Garonne river flows E. Ursu & J. Pereau 10.1007/s00477-015-1193-3
- Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts A. Gragne et al. 10.1016/j.jhydrol.2015.05.039
19 citations as recorded by crossref.
- Optimal advanced release scheme based on effective forecast horizon to minimize flood at downstream of a hydroelectric project D. Devi & A. Kumar Sarma 10.1016/j.jhydrol.2024.130822
- Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events T. Zhao et al. 10.1111/1752-1688.12575
- Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy A. Elshall et al. 10.5194/gmd-12-2009-2019
- Sensitivity of the HMS model to various modelling characteristics: case study of Bin El Ouidane basin (High Atlas of Morocco) A. Ahbari et al. 10.1007/s12517-018-3911-x
- Ensemble Based Forecasting and Optimization Framework to Optimize Releases from Water Supply Reservoirs for Flood Control V. Ramaswamy & F. Saleh 10.1007/s11269-019-02481-8
- Improved error modelling for streamflow forecasting at hourly time steps by splitting hydrographs into rising and falling limbs M. Li et al. 10.1016/j.jhydrol.2017.10.057
- Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting M. Li et al. 10.5194/hess-20-3561-2016
- Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems J. Hunter et al. 10.5194/hess-22-2987-2018
- Temporally varied error modelling for improving simulations and quantifying uncertainty L. Liu et al. 10.1016/j.jhydrol.2020.124914
- Two-stage robust unit commitment with the cascade hydropower stations retrofitted with pump stations C. Ju et al. 10.1016/j.apenergy.2023.120675
- Improving efficiencies of flood forecasting during lead times: an operational method and its application in the Baiyunshan Reservoir P. Liu et al. 10.2166/nh.2018.051
- The future of forecasting for renewable energy C. Sweeney et al. 10.1002/wene.365
- Improving hydropower inflow forecasts by assimilating snow data J. Magnusson et al. 10.2166/nh.2020.025
- Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm X. Liu et al. 10.3390/w15152700
- Estimating Reservoir Inflow and Outflow From Water Level Observations Using Expert Knowledge: Dealing With an Ill‐Posed Water Balance Equation in Reservoir Management J. Song et al. 10.1029/2020WR028183
- Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year F. Delgado-Ramos & C. Hervás-Gámez 10.3390/w10081038
- Impact of aerosols on reservoir inflow: A case study for Big Creek Hydroelectric System in California F. Kabir et al. 10.1002/hyp.13265
- Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting Z. Liu et al. 10.1007/s11269-016-1275-0
- AR-GARCH with Exogenous Variables as a Postprocessing Model for Improving Streamflow Forecasts X. Zha et al. 10.1061/(ASCE)HE.1943-5584.0001955
2 citations as recorded by crossref.
- Application of periodic autoregressive process to the modeling of the Garonne river flows E. Ursu & J. Pereau 10.1007/s00477-015-1193-3
- Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts A. Gragne et al. 10.1016/j.jhydrol.2015.05.039
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Short summary
We present a forecasting system comprising additively set-up conceptual and simple error model. Parameters of the conceptual model were left unaltered, as are in most operational set-ups, and the data-driven model was arranged to forecast the corrective measures the conceptual model needs. We demonstrate that the present procedure could effectively improve forecast accuracy over extended lead times with a reliability degree varying inter-annually and inter-seasonally.
We present a forecasting system comprising additively set-up conceptual and simple error model....
