考虑误差异分布的赣江流域径流概率预报
作者:
作者单位:

1.扬州大学水利科学与工程学院;2.河海大学水文水资源学院

中图分类号:

P33;TV124

基金项目:

国家自然科学基金资助项目(52109036,42371021,52379007);河海大学水灾害防御全国重点实验室“一带一路”水与可持续发展科技基金面上项目(2022491111);水利部水文气象灾害机理与预警重点实验室开放基金(HYMED202210,HYMED202203)联合资助。


Probability Streamflow Forecasting of Ganjiang River Basin Considering the Heterogeneous Distribution Characteristic of Forecasting Errors
Author:
Affiliation:

1.College of Hydraulic Science and Engineering,Yangzhou University;2.College of Hydrology and Water Resources,Hohai University

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    摘要:

    以赣江流域为研究对象,基于CN05.1降水数据和SWAT(Soil and Water Assessment Tool Model)模型,进行确定性径流预报,在此基础上,分析预报误差规律,采用考虑误差异分布的HRD(Heterogeneous Residual Distribution)方法,通过误差标准化模块和高斯变换模块消除流量对误差的影响,构建高斯误差的时间序列关系,估计误差的概率分布,进而实现径流概率预报。通过1963—2013年水文气象数据分析表明:(1)SWAT模型在外洲站的日径流模拟纳什效率系数在0.84以上,洪量误差和年平均洪峰误差均在10%以内,模拟精度较好;(2)HRD模型能够考虑流量级别对预报误差的影响,提供合理的概率预报结果,预报区间的覆盖率判定系数在0.66以上,且中位数预报和均值预报均优于SWAT模型预报结果,基准系数在0.64以上。研究可以为高精度径流预报提供新的思路,对流域水资源管理和防洪减灾具有重要的理论和实践意义。

    Abstract:

    Based on CN05.1 precipitation data and the SWAT (Soil and Water Assessment Tool Model) model, a deterministic runoff forecast was carried out in Ganjiang River Basin. On this basis, the characteristic of prediction error was analyzed and provided a probabilistic streamflow forecasting using the HRD (Heterogeneous Residual Distribution) method, which takes the heterogeneous distribution characteristic of errors into account. By using the residual standardization module and Gaussian anamorphosis module, the influence of streamflow on errors is eliminated, then temporal structure of Gaussian errors is constructed, and the probability distribution of errors is estimated, thereby achieving probabilistic streamflow forecasting. The results of study on hydrological and meteorological data in 1963-2013 indicate: (1) The SWAT model has good precision in simulating daily runoff at Waizhou station with Nash–Sutcliffe Efficiency bigger than 0.84 and flood volume error and the average annual flood peak error are both within 10%. (2) The HRD model can effectively consider the impact of streamflow on forecasting errors and provide a reasonable probabilistic forecasting with containing ratio coefficient above 0.66. Moreover, both median and mean forecasts are superior to the results of deterministic prediction, with the benchmark efficiency above 0.64. This study can provide new ideas for high-precision runoff forecasting, and has important theoretical and practical significance for water resource management and flood disaster prevention and mitigation in the basins.

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  • 收稿日期:2024-08-25
  • 最后修改日期:2024-12-18
  • 录用日期:2025-01-15