The development of a real-time flooding operation model in the Tseng-Wen Reservoir

Chen, Yu Wen; Tsai, Jung‐Fa; Chang, Liang-Cheng; Ho, Chia-Cheng; Chen, You Cheng

doi:10.2166/nh.2013.301

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Floods are the most frequent type of natural disaster happening everywhere in the world. The severity of flooding is very visible in a country where there is no sufficient structural affordability due to financial limitations (Chen et al 2014;Abaya et al 2009). Currently, river floods are a global issue causing a serious problem to the residents living in the riverside (Cirella and Iyalomhe 2018).…”

Section: Introductionmentioning

confidence: 99%

Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

Tamiru

Wagari

2021

Model. Earth Syst. Environ.

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This study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.

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Section: Introductionmentioning

confidence: 99%

Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

Tamiru

Wagari

2021

Model. Earth Syst. Environ.

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“…However, computation costs increase exponentially as resolution increases. A study [18] was conducted on a two-dimensional model with grid resolutions ranging from 2.5 to 50 m, and better results were obtained using a grid with smaller intervals. Also, the Digital Terrain Model (DTM) source is very important for hydrodynamic analysis results [19].…”

Section: Introductionmentioning

confidence: 99%

The Influence of the Main Factors on the Accuracy of Hydrological Modelling of Flooded Lands

Burshtynska,

Zayats,

Halochkin

et al. 2023

Water

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This paper proposes a general methodological approach to hydrological modeling for determining the areas of flooded land in the plain part of the Dniester riverbed, the second largest river in Ukraine. The purpose of the study is the selection of parameters for modeling flooded zones in the plain section of the Dniester riverbed, taking into account the rising water level caused by the freshet that occurred in the western part of Ukraine on 22–24 June 2020. The proposed study examines the workflow of hydrological modeling of the flooded land zone and the main components of this scheme: the construction of the DTM, considering the roughness of the riparian territory by Manning’s coefficients, and indications of water rise. In the experiment, the influence of DTM reproduction resolution and Manning coefficients was analyzed, and their optimal values were selected, which allowed obtaining the parameters of hydrological modeling with a higher probability. The identified flooding areas were tested using a high-resolution space image during the flood in June 2020. The distance between the profiles affects not only the value of the modeling area but also their detail. The accuracy of the modeled flooded area is 5.1% for a 5 m interval between the profiles, 6.9% for 50 m, 8.2% for 100 m, and 10.8% for 200 m. These results allow determining the degree of influence of the distance between intervals on the modeling accuracy. Using different values of Manning’s coefficients for individual sections with different bedding surfaces and the selected spacing between profiles, which was 50 m, the accuracy of the modeling was investigated. After the modelling, the simulated flood areas were obtained in vector form, which allows for determining their areas and comparing them with the test flooded area. In the presented research, the RMSE of determining the flooded areas is about 5%. The test area of 600.6 hectares was determined with an accuracy of 0.8%.

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“…It is well-known that reservoirs can reduce the size and frequency of flood peaks in downstream reaches by storing flood water to attenuate flood peaks and to avoid the coincidence of floods from downstream tributaries of the same river (Xu, 1990;Lee et al, 2001;Cheng and Chau, 2004;De Paes and Brandão, 2013;Chen et al, 2014). There is a lot of research to quantitatively assess the role of reservoirs on flood control.…”

Section: Introductionmentioning

confidence: 99%

Evaluating functions of reservoirs′ storage capacities and locations on daily peak attenuation for Ganjiang River Basin using Xinanjiang model

Zheng²,

et al. 2016

Chin. Geogr. Sci.

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Flooding is the most prevalent and costly natural disaster in the world and building reservoirs is one of the major structural measures for flood control and management. In this paper, a framework was proposed to evaluate functions of reservoirs′ locations and magnitudes on daily peak flow attenuation for a large basin of China, namely Ganjiang River Basin. In this study, the Xinanjiang model was adopted to simulate inflows of the reservoirs and flood hydrographs of all sub-catchments of the basin, and simple reservoir operation rules were established for calculating outflows of the reservoirs. Four reservoirs scenarios were established to analyze reservoirs′ locations on daily peak flow attenuation. The results showed that: 1) reservoirs attenuated the peak discharges for all simulated floods, when the flood storage capacities increase as new reservoirs were built, the peak discharge attenuation by reservoirs showed an increasing tendency both in absolute and relative measures; 2) reservoirs attenuated more peak discharge relatively for small floods than for large ones; 3) reservoirs reduced the peak discharge more efficiently for the floods with single peak or multi peaks with main peak occurred first; and 4) effect of upstream reservoirs on peak attenuation decreased from upper reaches to lower reaches; upstream and midstream reservoirs played important roles in decreasing peak discharge both at middle and lower reaches, and downstream reservoirs had less effect on large peak discharge attenuation at outlet of the basin. The proposed framework of evaluating functions of multiple reser-voirs′ storage capacities and locations on peak attenuation is valuable for flood control planning and management at basin scale.

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The development of a real-time flooding operation model in the Tseng-Wen Reservoir

Cited by 9 publications

References 14 publications

Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

The Influence of the Main Factors on the Accuracy of Hydrological Modelling of Flooded Lands

Evaluating functions of reservoirs′ storage capacities and locations on daily peak attenuation for Ganjiang River Basin using Xinanjiang model

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