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Streamflow forecasting method with a hybrid physical process-mathematical statistic
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DOI：10.1007/s00477-023-02542-w.png)
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Abstract
En 中文
The complex topology of river networks and the numerous factors influencing streamflow make it challenging to forecast streamflow in large river basins. Improving the accuracy of streamflow forecasting in these basins is of great importance for water resource planning and management. In this paper, considering the spatial variability of impacts of human activities on streamflow, a streamflow forecasting method with a hybrid physical process-mathematical statistic is proposed to obtain better results by realizing the complementary advantages of conventional methods and the streamflow into Hongze Lake in China is forecasted by the proposed method. Firstly, the physical process-based Soil and Water Assessment Tool (SWAT) is used to forecast the streamflow of tributaries with few water storage projects such as reservoirs in the catchment area. Secondly, considering the correlation between mainstream streamflow and tributaries streamflow as well as the correlation between mainstream streamflow and its previous streamflow, streamflow forecasting models based on the vine copula function are constructed to forecast the mainstream streamflow and its confidence intervals, where there are many water storage projects in the catchment area. Then, the total streamflow into Hongze Lake and its confidence intervals are obtained by coupling the outputs of the above two parts. Finally, statistical indicators are chosen to evaluate the forecasting effects in terms of the credibility of deterministic forecasting as well as the reliability and acuity of probabilistic forecasting. The results demonstrate that the proposed method outperforms existing SWAT and long and short-term memory (LSTM) neural network methods in terms of forecasting performance. Consequently, it presents an effective alternative for addressing complex hydrological forecasting tasks in large river basins.
Keywords:
Streamflow forecasting
Spatiotemporal correlation
Soil and Water Assessment Tool (SWAT)
Vine copula function
Hongze Lake
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