| 长江上游夏季径流量年际增量预测模型及检验 |
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| 引用本文： | 庞轶舒,张俊,秦宁生,李金建.长江上游夏季径流量年际增量预测模型及检验[J].应用气象学报,2022,33(1):115-128. |
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| 作者姓名： | 庞轶舒 张俊 秦宁生 李金建 |
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| 作者单位： | 1.四川省气候中心，成都 610072 |
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| 基金项目： | 国家自然科学基金项目(41772173);中国气象局气候变化专项(CCSF202034);四川省科技计划项目(2019YJ0620)。 |
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| 摘 要： | 基于1980—2020年长江上游夏季径流量、降水和气温等资料, 采用小波分析、最优子集回归等方法, 分析径流量、降水量和气温的变化关系, 探讨引发径流量变化的前兆气候异常信号, 并构建径流量年际增量预测模型。结果表明: 径流量多寡直接取决于流域总降水量, 两者表现出显著的准两年周期振荡特征, 年际增量之间的相关系数(TCC)为0.88, 达到0.001显著性水平。流域平均气温对径流量变化影响相对较小。影响径流量变化的关键前兆气候信号为孟加拉湾冬季风、春季高原季风等8个气候特征量。所建模型在建模时段内(1981—2015年)的拟合值与观测值的TCC为0.81, 达到0.001显著性水平; 符号一致率(SCR)为77.1%, 在径流量变化异常年为100.0%;均方根误差为0.57。在2016—2020年的后报试验中, 模型预测与观测值的SCR为80.0 %, 均方根误差为0.99。经反演的预测径流量平均相对误差绝对值为19.3%。该模型对长江上游夏季径流量及其年际变化的预测准确率大于80%。
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| 关 键 词： | 长江上游径流量 年际增量 气候预测信号 径流量预测模型 |
| 收稿时间： | 2021-06-24 |
Forecast Model of Interannual Increment for Summer Runoff and Its Verification in the Upper Reaches of the Yangtze River |
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| Pang Yishu,Zhang Jun,Qin Ningsheng,Li Jinjian.Forecast Model of Interannual Increment for Summer Runoff and Its Verification in the Upper Reaches of the Yangtze River[J].Quarterly Journal of Applied Meteorology,2022,33(1):115-128. |
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| Authors: | Pang Yishu Zhang Jun Qin Ningsheng Li Jinjian |
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| Institution: | 1.Sichuan Climate Center, Chengdu 6100722.Institute of Plateau Meteorology, CMA, Chengdu/Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 6100713.Three Gorges Cascade Dispatching and Communication Center, Yichang 4430004.Chengdu University of Information Technology, Chengdu 610103 |
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| 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. |
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| Keywords: | runoff in the upper reaches of the Yangtze River annual increment climatic prediction signals forecast model for runoff |
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