Application of the Probability Evaluation for the Seasonal Reservoir Operation on Flood Mitigation and Water Supply in the Chao Phraya River Watershed, Thailand

Komori, Daisuke; Mateo, Cherry May R.; Saya, Akane; Nakamura, Shinichirô; Kiguchi, Masashi; Klinkhachorn, Phonchai; Sukhapunnaphan, Thada; Champathong, Adisorn; Takeya, Kimio; TaikanOki,

doi:10.20965/jdr.2013.p0432

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…As a result, approximately 1400 mm of rainfall fell in the wet season. The Bhumibol and Sirikit dam reservoirs were 45 % filled and 51 % filled by 15 April 2011, respectively, and then both were 95 % filled by 5 October and 14 September 2011 (Komori et al, 2013;Mateo et al, 2014). The peak discharge at Nakhon Sawan was 4686 m 3 s −1 on 14 October 2011.…”

Section: Study Areamentioning

confidence: 99%

Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin

Sayama

Tatebe

Iwami

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. The Thailand floods in 2011 caused unprecedented economic damage in the Chao Phraya River basin. To diagnose the flood hazard characteristics, this study analyses the hydrologic sensitivity of flood runoff and inundation to rainfall. The motivation is to address why the seemingly insignificant monsoon rainfall, or 1.2 times more rainfall than for past large floods, including the ones in 1995 and 2006, resulted in such devastating flooding. To quantify the hydrologic sensitivity, this study simulated longterm rainfall-runoff and inundation for the entire river basin (160 000 km 2 ). The simulation suggested that the flood inundation volume was 1.6 times more in 2011 than for the past flood events. Furthermore, the elasticity index suggested that a 1 % increase in rainfall causes a 2.3 % increase in runoff and a 4.2 % increase in flood inundation. This study highlights the importance of sensitivity quantification for a better understanding of flood hazard characteristics; the presented basin-wide rainfall-runoff-inundation simulation was an effective approach to analyse the sensitivity of flood runoff and inundation at the river basin scale.

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Section: Study Areamentioning

confidence: 99%

Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin

Sayama

Tatebe

Iwami

et al. 2015

Nat. Hazards Earth Syst. Sci.

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“…[] ascribed the 2011 Thai flood to heavy rainfalls which are not yet attributable to climate change; they stressed the importance of nonmeteorological factors such as reservoir management in setting the scale of the disaster. Other studies in the past also suggest that the two largest reservoirs in the basin, Bhumibol and Sirikit, significantly affect the hydrologic regime in the Chao Phraya River Basin [ Tebakari et al ., ; Komori et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models

Mateo

Hanasaki

Komori

et al. 2014

Water Resources Research

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105

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A catastrophic flood event which caused massive economic losses occurred in Thailand, in 2011.Several studies have already been conducted to analyze the Thai floods, but none of them have assessed the impacts of reservoir operation on flood inundation. This study addresses this gap by combining physically based hydrological models to explicitly simulate the impacts of reservoir operation on flooding in the Chao Phraya River Basin, Thailand. H08, an integrated water resources model with a reservoir operation module, was combined with CaMa-Flood, a river routing model with representation of flood dynamics. The combined H08-CaMa model was applied to simulate and assess the historical and alternative reservoir operation rules in the two largest reservoirs in the basin. The combined H08-CaMa model effectively simulated the 2011 flood: regulated flows at a major gauging station have high daily NSE-coefficient of 92% as compared with observed discharge; spatiotemporal extent of simulated flood inundation match well with those of satellite observations. Simulation results show that through the operation of reservoirs in 2011, flood volume was reduced by 8.6 billion m 3 and both depth and area of flooding were reduced by 40% on the average. Nonetheless, simple modifications in reservoir operation proved to further reduce the flood volume by 2.4 million m 3 and the depth and area of flooding by 20% on the average. By modeling reservoir operation with a hydrodynamic model, a more realistic simulation of the 2011 Thai flood was made possible, and the potential of reducing flood inundation through improved reservoir management was quantified.

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“…In the upper reaches of the Chao Phraya River, Thailand's largest river, the Bhumibol (BB) and Sirikit (SK) dams are in operation. Many studies have focused on dam operations (Mateo et al, 2014;Shimosaka et al, 2018;Komori et al, 2013). In addition, predicted precipitation can be used to pre-operate the dam, which is expected to further improve operational accuracy.…”

Section: Introductionmentioning

confidence: 99%

Use of deep learning to identify optimal meteorological inputs to forecast seasonal precipitation

Zenkoji

Tebakari

Sakakibara

2022

Hydrological Research Letters

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Using deep learning to identify meteorological factors has enabled optimal predictions of Thailand's seasonal precipitation two months in advance. A combination of surface temperature and pressure, specific humidity, and wind speed (zonal and meridional components) was tested.Examining each combination of meteorological factor has created optimal input data for seasonal precipitation forecasts. In addition, the hyperparameters of each model were calculated by Bayesian optimization. Predictive model performance tended to be better when the weight for pressure was higher, while a higher weight for specific humidity reduced predictive performance. Finally, visualization of the positive neuron values in all the coupled layers of the first layer showed that the regions with the highest frequency of occurrence were the El Niño monitoring areas such as the "Indian Ocean Basin Wide" (IOBW) and "NINO WEST".

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Application of the Probability Evaluation for the Seasonal Reservoir Operation on Flood Mitigation and Water Supply in the Chao Phraya River Watershed, Thailand

Cited by 12 publications

References 8 publications

Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin

Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin

Assessing the impacts of reservoir operation to floodplain inundation by combining hydrological, reservoir management, and hydrodynamic models

Use of deep learning to identify optimal meteorological inputs to forecast seasonal precipitation

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