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Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model

School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
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Water 2021, 13(3), 272; https://doi.org/10.3390/w13030272
Received: 26 November 2020 / Revised: 19 January 2021 / Accepted: 20 January 2021 / Published: 23 January 2021
For reservoir basins, complex underlying surface conditions, short flood confluence times, and concentrated water volumes make inflow flood forecasting difficult and cause forecast accuracies to be low. Conventional flood forecasting models can no longer meet the required forecast accuracy values for flood control operations. To give full play to the role of reservoirs in flood control and to maximize the use of reservoir flood resources, high-precision inflow flood forecasting is urgently needed as a support mechanism. In this study, the Baipenzhu Reservoir in Guangdong Province was selected as the study case, and an inflow flood forecast scheme was designed for the reservoir by a physically based distributed hydrological model, the Liuxihe model. The results show that the Liuxihe model has strong applicability for flood forecasting in the studied reservoir basin and that the simulation results are very accurate. This study also found that the use of different Digital Elevation Model (DEM) data sources has a certain impact on the structure of the Liuxihe model, but the constructed models can both simulate the inflow flood process of the Baipenzhu Reservoir well. At the same time, the Liuxihe model can reflect the spatial variation in rainfall well, and using the Particle swarm optimization (PSO) algorithm to optimize the initial model parameters can greatly reduce the uncertainty of the model forecasts. According to China’s hydrological information forecast standards, the Liuxihe model forecast schemes constructed by the two data sources are rated as Grade A and can be used for real-time flood forecasting in the Baipenzhu Reservoir basin. View Full-Text
Keywords: reservoir basin; flood forecast; Liuxihe model; DEM; rainfall distribution; forecast accuracy reservoir basin; flood forecast; Liuxihe model; DEM; rainfall distribution; forecast accuracy
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MDPI and ACS Style

Xu, S.; Chen, Y.; Xing, L.; Li, C. Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model. Water 2021, 13, 272. https://doi.org/10.3390/w13030272

AMA Style

Xu S, Chen Y, Xing L, Li C. Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model. Water. 2021; 13(3):272. https://doi.org/10.3390/w13030272

Chicago/Turabian Style

Xu, Shichao, Yangbo Chen, Lixue Xing, and Chuan Li. 2021. "Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model" Water 13, no. 3: 272. https://doi.org/10.3390/w13030272

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