Abstract
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.
Highlights
Floods are serious natural disasters, and forecasting flood disasters is a very effective non-engineering measure for flood control [1,2,3,4]
The initial parameters input to the Liuxihe model are determined by the physical characteristics of each unit, which can be generally divided into four categories, namely, topographic parameters, meteorological parameters, soil parameters and land-use parameters
The results show that the Liuxihe model can reflect the spatial variation in rainfall well, which is the greatest advantage of physically based distributed hydrological models (PBDHMs)
Summary
Floods are serious natural disasters, and forecasting flood disasters is a very effective non-engineering measure for flood control [1,2,3,4]. The lumped model regards the entire basin as a whole and cannot reflect the impact of reservoir impoundment on reservoir inflows Because of these difficulties, the use of conventional flood forecasting models can no longer meet the accuracy requirements for flood forecasting of reservoir flood control operations. With the continuous development of machine learning technology, some new algorithms have been widely used in flood forecasting, such as deep learning neural networks [29,30,31,32] These datadriven models mainly focus on the accuracy of the simulation results based on the given datasets and ignore the physical causality between input and output; they face some classical opposition due to reasons inherent in machine learning techniques (e.g., lack of transparency and difficulty of reproducing the results). This study is expected to provide a scientific basis for the application and development of the proposed reservoir inflow flood forecasting scheme
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