value of updating ensemble streamflow prediction in reservoir operations
This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programm...
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| Vydáno v: | Hydrological processes Ročník 24; číslo 20; s. 2888 - 2899 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Chichester, UK
John Wiley & Sons, Ltd
30.09.2010
Wiley |
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| ISSN: | 0885-6087, 1099-1085, 1099-1085 |
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| Abstract | This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. Copyright © 2010 John Wiley & Sons, Ltd. |
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| AbstractList | This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. Copyright © 2010 John Wiley & Sons, Ltd. This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the meteorological and hydrological variations during that month. The reservoir operating policies derived from a sampling stochastic dynamic programming model using ESP scenarios updated three times a month were applied to the Geum River basin to measure the value of updated ESP for 21 years with 100 initial storage combinations. The results clearly demonstrate that updating the ESP scenario improves the accuracy of the forecasts and consequently their operational benefit. This study also proves that the accuracy of the ESP scenario, particularly when high flows occur, has a considerable effect on the reservoir operations. Copyright 2010 John Wiley & Sons, Ltd. |
| Author | Eum, Hyung-Il Kim, Young-Oh |
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| Cites_doi | 10.1016/S0022-1694(01)00418-8 10.1016/j.eswa.2008.06.135 10.1061/JYCEAJ.0001714 10.1080/02508060108686957 10.1002/hyp.6528 10.1061/(ASCE)0733-9496(1991)117:1(28) 10.1016/S0022-1694(01)00419-X 10.1061/(ASCE)0733-9496(2007)133:1(4) 10.1029/WR025i002p00191 10.1061/(ASCE)0733-9496(1999)125:3(143) 10.1016/S0022-1694(01)00455-3 10.1029/WR013i005p00837 10.1016/S0022-1694(02)00292-5 10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2 10.1002/hyp.1009 10.1016/j.advwatres.2008.03.002 10.1061/(ASCE)0733-9496(1997)123:6(327) 10.1029/WR021i006p00779 10.1029/WR020i011p01499 10.1029/WR026i003p00447 |
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| Keywords | rivers models water policy ensemble streamflow prediction sampling runoff reservoirs accuracy digital simulation sampling stochastic dynamic programming water storage stochastic models reservoir operations water resource management prediction forecast accuracy streamflow |
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| References | Georgakakos KP, Krzysztofowicz R. 2001. Special issue on probabilistic and ensemble forecasting in hydrology. Journal of Hydrology 249: 1-196. Suiadee W, Tingsanchali T. 2007. A combined simulation-genetic algorithm optimization model for optimal rule curves of a reservoir: a case study of the Nam Oon Irrigation Project, Thailand. Hydrological Processes 21(23): 3211-3225. Yeh W-G, Becker L, Zettlemoyer R. 1982. Worth of inflow forecast for reservoir operation. Journal of Water Resources Planning and Management 108(3): 257-269. Kim Y-O, Eum H-I, Lee EG, Ko IH. 2007. Optimizing operational policies of a Korean multi-reservoir system using sampling stochastic dynamic programming with ensemble streamflow prediction. Journal of Water Resources Planning and Management 131(1): 4-14. Traction MS, Kalnay E. 1993. Ensemble forecasting at NMC: operation implementation. Weather and Forecasting 8: 379-398. Toth Z, Kalnay E. 1993. Ensemble forecasting at NMC: the generation of perturbations. Bulletin of the American Meteorological Society 74: 2317-2330. Jeong DI, Kim Y-O. 2002. Forecasting monthly inflow to Chungju dam using ensemble streamflow prediction. Journal of Korean Society of Civil Engineers 22(3-B): 321-331 (in Korean). Wilks DS. 1995. Forecast verification: statistical method in the atmospheric science, Academic Press: New York. Chiew FHS, Zhou SL, McMahon TA. 2003. Use of seasonal streamflow forecasts in water resources management. Journal of Hydrology 270(1-2): 135-144. Day GN. 1985. Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management 111(WR2): 147-170. Yao H, Georgakakos AP. 2001. Assessment of Flosom Lake response to historical and potential future climate scenarios 2. Reservoir management. Journal of Hydrology 249: 176-196. Kim Y-O, Jeong DI, Kim HS. 2001. Improving water supply outlooks in Korea with ensemble streamflow prediction. Water International 26(4): 563-568. Kim Y-O, Palmer RN. 1997. Value of seasonal flow forecasts in Bayesian stochastic programming. Journal of Water Resources Planning and Management 123(6): 327-335. Korea Water Resources Corporation. 2004. Development of a base technology for integrated real-time water management system. Ministry of Science and Technology: Korea (in Korean). Kelman J, Stedinger JR, Cooper LA, Hsu E, Yuan SQ. 1990. Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research 26(3): 447-454. Zahraie B, Hosseini SM. 2009. Development of reservoir operation policies considering variable agricultural water demands. Expert Systems with Applications 36(3): 4980-4987. Korea Water Resources Corporation. 1997. Optimal reservoir management system on real-time for the Nakdong River basin. Ministry of Construction and Transportation: Korea (in Korean). Korea Institute of Construction Technology. 2001. Water vision 2020. Ministry of Construction and Transportation: Korea (in Korean). Young GK. 1967. Finding reservoir operation rules. Journal of Hydraulics Division 93(HY6): 297. Georgakakos AP. 1989. Extended linear quadratic Gaussian (ELOG) control: further extensions. Water Resources Research 25(2): 191-201. Pereira MVF, Pinto LMVG. 1985. Stochastic optimization of a mutireservoir hydroelectric system: a decomposition approach. Water Resources Research 21(6): 779-792. Stedinger JR, Sule BF, Loucks DP. 1984. Stochastic dynamic programming models for reservoir operation optimization. Water Resources Research 20(11): 1499-1505. Faber BA, Stedinger JR. 2001. Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts. Journal of Hydrology 249(1-4): 113-133. Chang F-J, Hui S-C, Chen Y-C. 2002. Reservoir operation using grey fuzzy stochastic dynamic programming. Hydrological Processes 16(12): 2395-2408. Klemes V. 1977. Value of information in reservoir optimization. Water Resources Research 13(5): 857-850. US Army Corps of Engineers. 1991. User's manual SSARR model: Streamflow Synthesis and Reservoir Regulation, North Pacific Division of US Army Corps of Engineers, Portland, OR. Lund JR, Guzman J. 1999. Some derived operating rules for reservoirs in series or in parallel. Journal of Water Resources Planning and Management 125(3): 143-153. Huang W-C, Harboe R, Bogardi J. 1991. Testing stochastic dynamic programming models conditioned on observed or forecasted inflows. Journal of Water Resources Planning and Management 117(1): 28-36. Chaves P, Chang F-J. 2008. Intelligent reservoir operation system based on evolving artificial neural networks. Advances in Water Resources 31(6): 926-936. 2002; 16 1993; 8 1984; 20 1982; 108 1997 2003; 270 1995 2001; 26 2005 2004 1999; 125 2008; 31 1991 1991; 117 1989; 25 1985; 21 2001; 249 2009; 36 1990; 26 2001 1967; 93 2002; 22 2007; 131 1997; 123 1993; 74 1977; 13 2007; 21 1985; 111 1988 e_1_2_7_6_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 Korea Institute of Construction Technology (e_1_2_7_18_1) 2001 e_1_2_7_17_1 Jeong DI (e_1_2_7_12_1) 2002; 22 e_1_2_7_16_1 Wilks DS (e_1_2_7_30_1) 1995 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_11_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_27_1 Yeh W‐G (e_1_2_7_32_1) 1982; 108 Korea Water Resources Corporation (e_1_2_7_20_1) 2004 Day GN (e_1_2_7_5_1) 1985; 111 Young GK (e_1_2_7_33_1) 1967; 93 Korea Water Resources Corporation (e_1_2_7_19_1) 1997 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_24_1 e_1_2_7_23_1 Traction MS (e_1_2_7_28_1) 1993; 8 e_1_2_7_22_1 e_1_2_7_34_1 e_1_2_7_21_1 US Army Corps of Engineers (e_1_2_7_29_1) 1991 |
| References_xml | – reference: Faber BA, Stedinger JR. 2001. Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts. Journal of Hydrology 249(1-4): 113-133. – reference: Day GN. 1985. Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management 111(WR2): 147-170. – reference: Georgakakos KP, Krzysztofowicz R. 2001. Special issue on probabilistic and ensemble forecasting in hydrology. Journal of Hydrology 249: 1-196. – reference: Kelman J, Stedinger JR, Cooper LA, Hsu E, Yuan SQ. 1990. Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research 26(3): 447-454. – reference: Kim Y-O, Eum H-I, Lee EG, Ko IH. 2007. Optimizing operational policies of a Korean multi-reservoir system using sampling stochastic dynamic programming with ensemble streamflow prediction. Journal of Water Resources Planning and Management 131(1): 4-14. – reference: Yeh W-G, Becker L, Zettlemoyer R. 1982. Worth of inflow forecast for reservoir operation. Journal of Water Resources Planning and Management 108(3): 257-269. – reference: Kim Y-O, Jeong DI, Kim HS. 2001. Improving water supply outlooks in Korea with ensemble streamflow prediction. Water International 26(4): 563-568. – reference: Chaves P, Chang F-J. 2008. Intelligent reservoir operation system based on evolving artificial neural networks. Advances in Water Resources 31(6): 926-936. – reference: Chiew FHS, Zhou SL, McMahon TA. 2003. Use of seasonal streamflow forecasts in water resources management. Journal of Hydrology 270(1-2): 135-144. – reference: Toth Z, Kalnay E. 1993. Ensemble forecasting at NMC: the generation of perturbations. Bulletin of the American Meteorological Society 74: 2317-2330. – reference: Chang F-J, Hui S-C, Chen Y-C. 2002. Reservoir operation using grey fuzzy stochastic dynamic programming. Hydrological Processes 16(12): 2395-2408. – reference: Suiadee W, Tingsanchali T. 2007. A combined simulation-genetic algorithm optimization model for optimal rule curves of a reservoir: a case study of the Nam Oon Irrigation Project, Thailand. Hydrological Processes 21(23): 3211-3225. – reference: Klemes V. 1977. Value of information in reservoir optimization. Water Resources Research 13(5): 857-850. – reference: Lund JR, Guzman J. 1999. Some derived operating rules for reservoirs in series or in parallel. Journal of Water Resources Planning and Management 125(3): 143-153. – reference: Korea Water Resources Corporation. 1997. Optimal reservoir management system on real-time for the Nakdong River basin. Ministry of Construction and Transportation: Korea (in Korean). – reference: Young GK. 1967. Finding reservoir operation rules. Journal of Hydraulics Division 93(HY6): 297. – reference: Zahraie B, Hosseini SM. 2009. Development of reservoir operation policies considering variable agricultural water demands. Expert Systems with Applications 36(3): 4980-4987. – reference: Pereira MVF, Pinto LMVG. 1985. Stochastic optimization of a mutireservoir hydroelectric system: a decomposition approach. Water Resources Research 21(6): 779-792. – reference: Korea Water Resources Corporation. 2004. Development of a base technology for integrated real-time water management system. Ministry of Science and Technology: Korea (in Korean). – reference: Jeong DI, Kim Y-O. 2002. Forecasting monthly inflow to Chungju dam using ensemble streamflow prediction. Journal of Korean Society of Civil Engineers 22(3-B): 321-331 (in Korean). – reference: Wilks DS. 1995. Forecast verification: statistical method in the atmospheric science, Academic Press: New York. – reference: Yao H, Georgakakos AP. 2001. Assessment of Flosom Lake response to historical and potential future climate scenarios 2. Reservoir management. Journal of Hydrology 249: 176-196. – reference: Korea Institute of Construction Technology. 2001. Water vision 2020. Ministry of Construction and Transportation: Korea (in Korean). – reference: Stedinger JR, Sule BF, Loucks DP. 1984. Stochastic dynamic programming models for reservoir operation optimization. Water Resources Research 20(11): 1499-1505. – reference: Traction MS, Kalnay E. 1993. Ensemble forecasting at NMC: operation implementation. Weather and Forecasting 8: 379-398. – reference: Huang W-C, Harboe R, Bogardi J. 1991. Testing stochastic dynamic programming models conditioned on observed or forecasted inflows. Journal of Water Resources Planning and Management 117(1): 28-36. – reference: Georgakakos AP. 1989. Extended linear quadratic Gaussian (ELOG) control: further extensions. Water Resources Research 25(2): 191-201. – reference: Kim Y-O, Palmer RN. 1997. Value of seasonal flow forecasts in Bayesian stochastic programming. Journal of Water Resources Planning and Management 123(6): 327-335. – reference: US Army Corps of Engineers. 1991. User's manual SSARR model: Streamflow Synthesis and Reservoir Regulation, North Pacific Division of US Army Corps of Engineers, Portland, OR. – volume: 26 start-page: 447 issue: 3 year: 1990 end-page: 454 article-title: Sampling stochastic dynamic programming applied to reservoir operation publication-title: Water Resources Research – volume: 93 start-page: 297 issue: HY6 year: 1967 article-title: Finding reservoir operation rules publication-title: Journal of Hydraulics Division – year: 2005 – volume: 20 start-page: 1499 issue: 11 year: 1984 end-page: 1505 article-title: Stochastic dynamic programming models for reservoir operation optimization publication-title: Water Resources Research – volume: 123 start-page: 327 issue: 6 year: 1997 end-page: 335 article-title: Value of seasonal flow forecasts in Bayesian stochastic programming publication-title: Journal of Water Resources Planning and Management – volume: 31 start-page: 926 issue: 6 year: 2008 end-page: 936 article-title: Intelligent reservoir operation system based on evolving artificial neural networks publication-title: Advances in Water Resources – year: 2001 – volume: 249 start-page: 176 year: 2001 end-page: 196 article-title: Assessment of Flosom Lake response to historical and potential future climate scenarios 2. Reservoir management publication-title: Journal of Hydrology – volume: 21 start-page: 3211 issue: 23 year: 2007 end-page: 3225 article-title: A combined simulation‐genetic algorithm optimization model for optimal rule curves of a reservoir: a case study of the Nam Oon Irrigation Project, Thailand publication-title: Hydrological Processes – volume: 117 start-page: 28 issue: 1 year: 1991 end-page: 36 article-title: Testing stochastic dynamic programming models conditioned on observed or forecasted inflows publication-title: Journal of Water Resources Planning and Management – volume: 131 start-page: 4 issue: 1 year: 2007 end-page: 14 article-title: Optimizing operational policies of a Korean multi‐reservoir system using sampling stochastic dynamic programming with ensemble streamflow prediction publication-title: Journal of Water Resources Planning and Management – volume: 36 start-page: 4980 issue: 3 year: 2009 end-page: 4987 article-title: Development of reservoir operation policies considering variable agricultural water demands publication-title: Expert Systems with Applications – volume: 125 start-page: 143 issue: 3 year: 1999 end-page: 153 article-title: Some derived operating rules for reservoirs in series or in parallel publication-title: Journal of Water Resources Planning and Management – volume: 21 start-page: 779 issue: 6 year: 1985 end-page: 792 article-title: Stochastic optimization of a mutireservoir hydroelectric system: a decomposition approach publication-title: Water Resources Research – volume: 8 start-page: 379 year: 1993 end-page: 398 article-title: Ensemble forecasting at NMC: operation implementation publication-title: Weather and Forecasting – volume: 25 start-page: 191 issue: 2 year: 1989 end-page: 201 article-title: Extended linear quadratic Gaussian (ELOG) control: further extensions publication-title: Water Resources Research – volume: 16 start-page: 2395 issue: 12 year: 2002 end-page: 2408 article-title: Reservoir operation using grey 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| Snippet | This study proposes a new monthly ensemble streamflow prediction (ESP) forecasting system that can update the ESP in the middle of a month to reflect the... |
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| SubjectTerms | Accuracy carbon sequestration Copyrights dynamic models Dynamical systems Earth sciences Earth, ocean, space ensemble streamflow prediction Exact sciences and technology forecast accuracy Freshwater Geum Hydrology Hydrology. Hydrogeology Mathematical models prediction reservoir operations Reservoirs River basins Sampling sampling stochastic dynamic programming stream flow water reservoirs Water resources watersheds |
| Title | value of updating ensemble streamflow prediction in reservoir operations |
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