Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation
AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantil...
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Gespeichert in:
| Autor*in: |
Guo, Shenglian [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © 2014 American Society of Civil Engineers |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of hydrologic engineering - Reston, Va. : Soc., 1996, 20(2015), 2 |
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| Übergeordnetes Werk: |
volume:20 ; year:2015 ; number:2 |
| Links: |
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| DOI / URN: |
10.1061/(ASCE)HE.1943-5584.0000979 |
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| Katalog-ID: |
OLC1960286668 |
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| 520 | |a AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. | ||
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10.1061/(ASCE)HE.1943-5584.0000979 doi PQ20160617 (DE-627)OLC1960286668 (DE-599)GBVOLC1960286668 (PRQ)a1582-6d24af629d5af0994a690ab2c6b0cb5764c8e7ef02eb3a864377ce24a9810e9a0 (KEY)0295891020150000020000200000usingabayesianprobabilisticforecastingmodeltoanaly DE-627 ger DE-627 rakwb eng 690 ZDB Guo, Shenglian verfasserin aut Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. Nutzungsrecht: © 2014 American Society of Civil Engineers Technical Papers Li, Xiang oth Chen, Hua oth Rosbjerg, Dan oth Liu, Dedi oth Enthalten in Journal of hydrologic engineering Reston, Va. : Soc., 1996 20(2015), 2 (DE-627)191495115 (DE-600)1295124-9 (DE-576)051377314 1084-0699 nnns volume:20 year:2015 number:2 http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000979 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GGO GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 AR 20 2015 2 |
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10.1061/(ASCE)HE.1943-5584.0000979 doi PQ20160617 (DE-627)OLC1960286668 (DE-599)GBVOLC1960286668 (PRQ)a1582-6d24af629d5af0994a690ab2c6b0cb5764c8e7ef02eb3a864377ce24a9810e9a0 (KEY)0295891020150000020000200000usingabayesianprobabilisticforecastingmodeltoanaly DE-627 ger DE-627 rakwb eng 690 ZDB Guo, Shenglian verfasserin aut Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. Nutzungsrecht: © 2014 American Society of Civil Engineers Technical Papers Li, Xiang oth Chen, Hua oth Rosbjerg, Dan oth Liu, Dedi oth Enthalten in Journal of hydrologic engineering Reston, Va. : Soc., 1996 20(2015), 2 (DE-627)191495115 (DE-600)1295124-9 (DE-576)051377314 1084-0699 nnns volume:20 year:2015 number:2 http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000979 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GGO GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 AR 20 2015 2 |
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10.1061/(ASCE)HE.1943-5584.0000979 doi PQ20160617 (DE-627)OLC1960286668 (DE-599)GBVOLC1960286668 (PRQ)a1582-6d24af629d5af0994a690ab2c6b0cb5764c8e7ef02eb3a864377ce24a9810e9a0 (KEY)0295891020150000020000200000usingabayesianprobabilisticforecastingmodeltoanaly DE-627 ger DE-627 rakwb eng 690 ZDB Guo, Shenglian verfasserin aut Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. Nutzungsrecht: © 2014 American Society of Civil Engineers Technical Papers Li, Xiang oth Chen, Hua oth Rosbjerg, Dan oth Liu, Dedi oth Enthalten in Journal of hydrologic engineering Reston, Va. : Soc., 1996 20(2015), 2 (DE-627)191495115 (DE-600)1295124-9 (DE-576)051377314 1084-0699 nnns volume:20 year:2015 number:2 http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000979 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GGO GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 AR 20 2015 2 |
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10.1061/(ASCE)HE.1943-5584.0000979 doi PQ20160617 (DE-627)OLC1960286668 (DE-599)GBVOLC1960286668 (PRQ)a1582-6d24af629d5af0994a690ab2c6b0cb5764c8e7ef02eb3a864377ce24a9810e9a0 (KEY)0295891020150000020000200000usingabayesianprobabilisticforecastingmodeltoanaly DE-627 ger DE-627 rakwb eng 690 ZDB Guo, Shenglian verfasserin aut Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. Nutzungsrecht: © 2014 American Society of Civil Engineers Technical Papers Li, Xiang oth Chen, Hua oth Rosbjerg, Dan oth Liu, Dedi oth Enthalten in Journal of hydrologic engineering Reston, Va. : Soc., 1996 20(2015), 2 (DE-627)191495115 (DE-600)1295124-9 (DE-576)051377314 1084-0699 nnns volume:20 year:2015 number:2 http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000979 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GGO GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 AR 20 2015 2 |
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10.1061/(ASCE)HE.1943-5584.0000979 doi PQ20160617 (DE-627)OLC1960286668 (DE-599)GBVOLC1960286668 (PRQ)a1582-6d24af629d5af0994a690ab2c6b0cb5764c8e7ef02eb3a864377ce24a9810e9a0 (KEY)0295891020150000020000200000usingabayesianprobabilisticforecastingmodeltoanaly DE-627 ger DE-627 rakwb eng 690 ZDB Guo, Shenglian verfasserin aut Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. Nutzungsrecht: © 2014 American Society of Civil Engineers Technical Papers Li, Xiang oth Chen, Hua oth Rosbjerg, Dan oth Liu, Dedi oth Enthalten in Journal of hydrologic engineering Reston, Va. : Soc., 1996 20(2015), 2 (DE-627)191495115 (DE-600)1295124-9 (DE-576)051377314 1084-0699 nnns volume:20 year:2015 number:2 http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000979 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GGO GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 AR 20 2015 2 |
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In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. 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using a bayesian probabilistic forecasting model to analyze the uncertainty in real-time dynamic control of the flood limiting water level for reservoir operation |
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Using a Bayesian Probabilistic Forecasting Model to Analyze the Uncertainty in Real-Time Dynamic Control of the Flood Limiting Water Level for Reservoir Operation |
| abstract |
AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. |
| abstractGer |
AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. |
| abstract_unstemmed |
AbstractDynamic control of the flood limiting water level (FLWL) is a valuable and effective way to maximize the benefits from reservoir operation without exceeding the design risk. In order to analyze the impacts of input uncertainty, a Bayesian forecasting system (BFS) is adopted. Applying quantile water inflow values and their uncertainties obtained from the BFS, the reservoir operation results from different schemes can be analyzed in terms of benefits, dam safety, and downstream impacts during the flood season. When the reservoir FLWL dynamic control operation is implemented, there are two fundamental kinds of dynamic control bounds. One is the flood subseasonal FLWL dynamic control bounds, which are based on the segmentation of the flood season and the ranges of the FLWL in every flood subseason (Scheme I); the other one is the flood seasonal FLWL dynamic control bound, which takes the flood season as a whole, thus producing only one boundary [Scheme II]. The Three Gorges Reservoir (TGR) in China was selected as a case study in this paper. The application results show that the thresholds of maximum outflow, which impact the downstream and maximum reservoir levels, are not exceeded during the flood season under the analyzed FLWL control schemes. The benefits in terms of the floodwater utilization rate, hydropower generation, and water level at the end of the flood season from two dynamic controls of the FLWL scheme are better than the current design, which applies a static FLWL. For comparison, also deterministic water inflow was tested. The proposed model in the paper emphasizes the importance of analyzing the uncertainties of the water inflow forecasting system for real-time dynamic control of the FLWL for reservoir operation. For the case study, the selected quantile inflow from the Bayesian forecasting system and the matching operation are beneficial for the decision makers of the Three Gorges Reservoir. |
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