| 引用本文: | 李彬权,朱畅畅,梁忠民,陈云瑶,蒋晓蕾,张涛涛.大渡河猴子岩水库入库洪水过程预报-实时校正-概率预报集成.湖泊科学,2023,35(4):1481-1490. DOI:10.18307/2023.0443 |
| Li Binquan,Zhu Changchang,Liang Zhongmin,Chen Yunyao,Jiang Xiaolei,Zhang Taotao.Integration of process forecast, real-time correction and probabilistic forecast of inflow floods in Houziyan Reservoir of Dadu River. J. Lake Sci.2023,35(4):1481-1490. DOI:10.18307/2023.0443 |
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| 大渡河猴子岩水库入库洪水过程预报-实时校正-概率预报集成 |
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李彬权1,2, 朱畅畅3, 梁忠民1,2, 陈云瑶1, 蒋晓蕾4, 张涛涛5
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1.河海大学水文水资源学院, 南京 210098;2.河海大学水安全与水科学协同创新中心, 南京 210024;3.南京大学环境规划设计研究院集团股份公司, 南京 210008;4.扬州大学水利科学与工程学院, 扬州 225009;5.苏州科技大学环境科学与工程学院, 苏州 215009
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| 摘要: |
| 与传统确定性预报相比,洪水概率预报能够为防洪调度决策提供更为丰富的信息。以大渡河猴子岩水库以上流域为研究区,建立新安江次洪模型,并采用动态系统响应曲线进行实时洪水预报校正。在确定性预报校正基础上,建立基于水文不确定性处理器(HUP)的次洪概率预报模型,定量分析预报不确定性,实现入库洪水概率预报。结果表明:①利用猴子岩流域2009-2019年水文气象资料,建立的新安江次洪模型整体精度较高,率定期和验证期的洪量和洪峰相对误差均在±20%以内,平均确定性系数分别为0.69和0.72;经动态系统响应曲线校正后,洪峰和洪量误差均有降低,率定期和验证期的确定性系数分别提高0.13和0.09。②以2020年3场洪水未来48 h预报降雨为输入,新安江模型预报精度不高,且随着预见期增长而降低,但经动态系统响应曲线校正后,整体预报精度有所提高,洪量相对误差减小幅度超50%,确定性系数提高幅度超60%。③HUP次洪概率预报模型提供的分布函数中位数Q50的预报精度在一定程度上优于校正后的确定性预报;提供的90%置信区间覆盖率均在90%左右,离散度均在0.40以下,能以相对较窄的区间覆盖大部分实测值,具有较高的可靠度。 |
| 关键词: 新安江模型 动态系统响应曲线 水文不确定性处理器 概率预报 大渡河流域 猴子岩水库 |
| DOI：10.18307/2023.0443 |
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| 基金项目:国家自然科学基金项目(U2240209,41877147)资助。 |
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| Integration of process forecast, real-time correction and probabilistic forecast of inflow floods in Houziyan Reservoir of Dadu River |
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Li Binquan1,2, Zhu Changchang3, Liang Zhongmin1,2, Chen Yunyao1, Jiang Xiaolei4, Zhang Taotao5
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1.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, P. R. China;2.Cooperative Innovation Center for Water Safety & Hydro Science, Hohai University, Nanjing 210024, P. R. China;3.Academy of Environmental Planning & Design Co., Ltd, Nanjing University, Nanjing 210008, P. R. China;4.College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, P. R. China;5.School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, P. R. China
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| Abstract: |
| Compared with traditional deterministic forecasting, flood probabilistic forecasting can provide more abundant information for flood control scheduling decisions. Taking the basin above the Houhouyan Reservoir of the Dadu River as the study area, the Xin'anjiang model for flood forecasting was established, and the dynamic system response curve method was used to conduct real-time flood forecast correction. Furthermore, a probabilistic flood forecasting model based on the Hydrological Uncertainty Processor (HUP) was established to quantitatively analyze the forecast uncertainty and then produced the probabilistic forecasting. The results showed that: (a) Using the hydrometeorological data in the Houziyan Reservoir Basin from 2009 to 2019, the overall accuracy of the Xin'anjiang model was high. The relative errors of flood volume and flood peak during the calibration and validation periods were within ±20%, and the corresponding average Nash-Sutcliffe efficiency coefficients were 0.69 and 0.72, respectively. After the dynamic system response curve correction, both flood peak and flood volume errors were reduced, and the Nash-Sutcliffe efficiency coefficients of the calibration and validation periods were increased by 0.13 and 0.09, respectively. (b) Taking the forecast rainfall for the next 48 hours of the three floods in 2020 as the input, the forecast accuracy of the Xin'anjiang model was not high, and it decreased with the growth of the forecast period. However, after the dynamic system response curve correction, the overall forecast accuracy had been improved, and the flood volume error had reduced by more than 50%, and the Nash-Sutcliffe efficiency coefficient was increased by more than 60%. (c) The forecast accuracy of the median (Q50) of the probability distribution function provided by the HUP model was better than the corrected deterministic forecast to a certain extent; the coverage rate of the provided 90% confidence interval was about 90%, and the dispersion was below 0.40, which could cover most of the measured flow values in a relatively narrow interval, with high reliability. |
| Key words: Xin'anjiang Model dynamic system response curve Hydrologic Uncertainty Processor (HUP) probabilistic forecasts Dadu River Basin Houziyan Reservoir |
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