The Xinanjiang model has been successfully and widely applied in humid and semi-humid regions of China for rainfall–runoff simulation and flood forecasting. However, its forecasting precision is seriously affected by antecedent precipitation (Pa). Commonly applied methods relying on the experience of individual modelers are not standardized and difficult to transfer. In particular, the Xinanjiang daily model may result in obvious errors in the determination of Pa. Thus, a practical method for estimating Pa is proposed in this paper, which is based on a genetic algorithm (GA) and is estimated during a rising flood period. In the optimization process of a GA, Pa values form a chromosome, the root-mean-squared error between the observed and simulated streamflow is chosen as the fitness function. Simultaneously, the best individual reserved strategy is adopted between correction periods to avoid complete independence between each optimization process as well as to ensure the stability of the algorithm. Twenty-seven historical floods observed at the gauge station of the Shuangpai reservoir in Hunan Province of China are used to test the presented algorithm for estimation of Pa, and the results demonstrate that the proposed method significantly improves the quality of flood forecasting in the Xinanjiang model.
Weather Information Service Engine Project (WISE)
Weather Information Service Engine Project (WISE)is dedicated to resolve urban environmental issues including high-resolution weather forecasting, urban flooding prediction, road meteorology and urban carbon dynamics, and new urban service systems to minimize and mitigate the impacts of natural disasters and climate change on urban dwellers. WISE is targeting South Korea’s capitol, the megacity Seoul, and surrounding urban areas. Recently they started a project called the City Energy Monitoring System,...
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