Forecast Model of Interannual Increment for Summer Runoff and Its Verification in the Upper Reaches of the Yangtze River

Abstract

The upper reaches of the Yangtze River is the hydropower resources and flood control focus for the whole river. Summer is an important period for flood diversion operation and hydropower development. Therefore, the relationships between summer runoff, precipitation and surface air temperature are analyzed, and the precursory physical climate signals for the runoff in the upper reaches of the Yangtze River are analyzed. By optimal subset regression and some other statistical methods, an annual increment prediction model with multi climatic factors for the runoff is built. The results show that the runoff directly depends on total precipitation in the basin, and they both show a slow downward trend in the past 40 years with a prominent quasi biennial oscillation. Their temporal correlation coefficient (TCC) is 0.81, exceeding the significant level of 0.001. By contrast, the average temperature of the watershed shows a significant upward trend, while influents less on the amount of runoff. On interannual time scale, the decisive role of precipitation on runoff is more prominent, while the influence of average temperature further weakens. Based on physical mechanism analysis, 8 key climate preceding signals of runoff are selected. They are the Bay of Bengal monsoon and Australian High in winter, Indonesia Australia meridional wind shear, meridional position of the northern hemisphere polar vortex, Ural Mountain circulation, plateau monsoon and the temperature at high altitude basin in spring, and autumn sea level pressure dipole of the Indian Ocean. The prediction model for summer runoff built on these factors is tested by TCC, sign consistent rate (SCR), root mean square error (RMSE), absolute relative error (AE) and some other techniques. By the indication of test, fitting rate of the model is 0.81 during its modeling period from 1981 to 2015. In addition, SCR between the simulated and observed value is 77.1%, which is 100.0% for the abnormal years, and the RMSE is 0.57. After inversion calculation, TCC of the simulated with observed runoff is 0.66, exceeding the significant level of 0.001, and the average AE is 14.5%. In the post-test from 2016 to 2020, SCR and RMSE of the model are 80.0% and 0.99, respectively. The average AE of predicted runoff is 19.3%. Overall, the prediction accuracy of this model for summer runoff and its interannual variation characteristics of the upper reaches of the Yangtze River is more than 80%. Compared with the existing prediction models, prediction skills of this model are significantly improved, indicating a potential applicability.

Keywords

• annual increment
• climatic prediction signals
• forecast model for runoff