文章摘要
马川惠,黄强,郭爱军.泾河流域水沙联合分布特征分析及其不确定性评估[J].水利学报,2019,50(2):273-282
泾河流域水沙联合分布特征分析及其不确定性评估
Characteristic analysis and uncertainty assessment of joint distribution of flow and sand in Jinghe River basin
投稿时间:2018-07-19  
DOI:10.13243/j.cnki.slxb.20180669
中文关键词: Copula 函数  联合重现期  蒙特卡洛  最大可能组合  泾河流域
英文关键词: Copula function  Joint return period  Monte Carlo  Most likely combination  Jinghe River basin
基金项目:国家重点研发计划项目(2017YFC0405901-3);水利部公益性行业基金(201501058)
作者单位E-mail
马川惠 西安理工大学 省部共建西北旱区生态水利国家重点实验室, 陕西 西安 710048  
黄强 西安理工大学 省部共建西北旱区生态水利国家重点实验室, 陕西 西安 710048 syhuangqiang@163.com 
郭爱军 西安理工大学 省部共建西北旱区生态水利国家重点实验室, 陕西 西安 710048  
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中文摘要:
      径流与泥沙是非独立的二维随机变量,若要对流域实现水沙并举的科学管理方案,开展水沙联合概率分析显得尤为必要。而在联合概率分析中,水沙样本系列容量一般较小,使得联合设计值估计具有不确定性。以泾河流域为例,本文提出基于蒙特卡洛法的两变量联合设计值不确定性量化方法。该方法基于Copula函数建立水沙联合分布模型,推求两变量联合设计值的最可能组合模式,利用蒙特卡洛抽样法分析样本不确定性对水沙联合设计值的影响,计算两变量设计值置信区间。结果表明,OR重现期为20年的情况下,联合设计值95%二元置信区间表现出较大的不确定性,对流域工程设计值的确定提出了巨大挑战,且随着重现期水平的增加,联合设计值的不确定性随之增加。
英文摘要:
      The flow and sediment are non-independent two-dimensional random variables,so it is very necessary to carry out joint probability analysis of flow and sediment in order to realize the scientific management scheme of flow and sediment. In the joint probability analysis, the limited flow and sediment sample size would induce quantile estimation uncertainty in bivariate probability analysis. Based on Monte Carlo method, this paper presents the quantitative uncertainty evaluation method of bivariate quantile estimation in the Jinghe River basin. The flow and sediment joint distribution model is established based on Copulas function and the most likely realizations of bivariate quantile estimation are derived. The Monte Carlo sampling method is used to analyze the influence of sample uncertainty of bivariate quantile estimation,and the confidence region of bivariate quantile estimation is calculated. The results demonstrate that when the OR return period is 20 years,the 95% confidence region of bivariate quantile estimation shows greater uncertainty. It is a great challenge to determine the quantile estimation of river basin engineering design. The uncertainty of bivariate quantile estimation increases with the increase of the joint return period level.
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