| 基于MCMC参数优化的融雪洪水联合设计分析 |
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| 引用本文： | 何朝飞,陈伏龙,张志君,杨宽,何新林,龙爱华. 基于MCMC参数优化的融雪洪水联合设计分析[J]. 长江科学院院报, 2020, 37(11): 52-58. DOI: 10.11988/ckyyb.20190961 |
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| 作者姓名： | 何朝飞 陈伏龙 张志君 杨宽 何新林 龙爱华 |
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| 作者单位： | 1.石河子大学 水利建筑工程学院,新疆 石河子 832000;2.中国水利水电科学研究院流域水循环模拟与调控国家重点实验室,北京 100038 |
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| 基金项目： | 国家自然科学基金项目(51769029);国家重点研发计划项目(2017YFC0404301);石河子大学高层次人才科研启动资金专项(RCZK2018C23) |
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| 摘 要： | 洪水的发生是由多种特征属性所推动的,由于其具有多个并发或连续驱动因素的内在影响,从而大大加剧了其发生的不可估计性。在许多风险评估和设计应用中,极端情况和复合事件的多危险情往往被忽略。针对单变量洪水的设计缺陷性,以玛纳斯河出山口控制流域为研究区,选用GEV(Generalized Extreme Value)分布和GPD(Generalized Pareto Distribution)分别构建峰量边际分布,利用MCMC(Markov Chain Monte Carlo)参数优化的Gaussian Copula函数构建极端情况下的联合概率分布,并以500 a一遇设计洪水为例,推求其设计洪水过程线。结果表明:基于MCMC优化参数的Gaussian Copula函数的联合分布拟合效果均大于相关性指标法和局部优化算法;变量间相互影响下的设计值均相应大于以某一单变量控制下的洪水设计值,其中在设计洪水过程线90 h的设计增长率为24.19%。因此,以参数优化下的联合分布所建立的防洪新标准,可为玛纳斯河流域水库汛期防洪减灾安全设计提供更加科学合理的依据。
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| 关 键 词： | 融雪 洪水 联合设计 峰量拟合 MCMC Copula函数 重现期 |
| 收稿时间： | 2019-08-07 |
| 修稿时间： | 2019-10-21 |
Joint Design Analysis of Snowmelt Flood Based on MCMC Parameter Optimization |
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| HE Chao-fei,CHEN Fu-long,ZHANG Zhi-jun,YANG Kuan,HE Xin-lin,LONG Ai-hua. Joint Design Analysis of Snowmelt Flood Based on MCMC Parameter Optimization[J]. Journal of Yangtze River Scientific Research Institute, 2020, 37(11): 52-58. DOI: 10.11988/ckyyb.20190961 |
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| Authors: | HE Chao-fei CHEN Fu-long ZHANG Zhi-jun YANG Kuan HE Xin-lin LONG Ai-hua |
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| Affiliation: | 1. College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China;2. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of WaterResources and Hydropower Research, Beijing 100038, China |
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| Abstract: | Driven by multiple characteristic attributes, flood is inherently affected by multiple concurrent or continuous driving factors, which aggravates the unpredictability of flood occurrence. In many risk assessment and design applications, the multi-hazards of extreme situations and compound events are often ignored. In view of the design flaws of univariate floods, the peak volume marginal distribution was constructed using GEV (Generalized Extreme Value) distribution and GPD (Generalized Pareto Distribution). The joint probability distribution under extreme conditions was established using the Gaussian Copula function with parameters optimized by MCMC (Markov Chain Monte Carlo). Furthermore, the design flood process line was derived with 500-year-event flood as an example. The control basin of the Manas River was taken as the research area. Results demonstrate that the joint distribution fitting effect of the Gaussian Copula function with MCMC-optimized parameters is better than that of the correlation index method and the local optimization algorithm. The design value under mutual influence of the variables is correspondingly greater than the flood design value under the control of a single variable. The design growth rate of 90 h in the design flood process line is 24.19%. Therefore, the new flood control standard established by the joint distribution under parameter optimization provides a more scientific and reasonable basis for the safety design of flood control and disaster mitigation for the Manas River reservoir in flood season. |
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| Keywords: | snowmelt flood joint design peak fitting MCMC Copula function recurrence period |
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