融合AR模型和MCMC方法的水文模拟不确定性分析  

Uncertainty analysis of hydrological simulation with auto regressive model and MCMC method

作　　者：贺新月 曾献奎[1] 王栋[1] HE Xinyue;ZENG Xiankui;WANG Dong(School of Earth Sciences and Engineering,Nanjing University,Nanjing 210023,China)

机构地区：[1]南京大学地球科学与工程学院,江苏南京210023

出　　处：《河海大学学报（自然科学版）》2020年第2期116-122,共7页Journal of Hohai University(Natural Sciences)

基　　金：国家重点研发计划重点专项(2016YFC0402802)。

摘　　要：为提高水文模型参数识别的可靠性,融合自回归模型与马尔可夫链-蒙特卡洛方法(auto regressive model based modified Markov Chain-Monte Carlo,AR-MCMC),利用自回归模型刻画残差序列的自相关性,修正MCMC方法中的残差协方差矩阵。通过新疆提孜那甫河流域融雪径流模型(SRM)的案例分析发现:融雪径流模拟的残差序列具有显著的自相关性;修正残差协方差矩阵后,边缘似然值更大;综合考虑多项评价指标,AR-MCMC方法在识别期与验证期推求的预测区间均优于MCMC方法;对比2种方法在识别期与验证期的纳什系数,采用AR-MCMC方法依次为0.86、0.89,而采用MCMC方法依次为0.84、0.87,即AR-MCMC方法获取的模型拟合效果更好。分析结果表明,相对于传统的MCMC方法,AR-MCMC方法能够更好地对研究区融雪径流过程进行模拟预测。To improve the reliability of parameter identification in the hydrologic modelling,this study combined auto regressive model with Markov chain Monte Carlo method,and proposed an auto regressive(AR)model based modified Markov Chain-Monte Carlo(AR-MCMC).The residual covariance matrix in the traditional MCMC method is modified by using the AR model.Based on a case study of snowmelt runoff model(SRM)in Xinjiang’s Tiznavu watershed,it can be found that the residual sequence of snowmelt runoff simulation has significant autocorrelation.By correcting the residual covariance matrix,the marginal likelihood of AR-MCMC is larger than that of MCMC.Based on the assessments of multiple indexes,AR-MCMC method has a better prediction interval than MCMC.In addition,when comparing the Nash coefficients in calibration and verification periods,they are 0.86 and 0.89 respectively for AR-MCMC,while 0.84 and 0.87 respectively for MCMC.Therefore,the snowmelt runoff simulation obtained by AR-MCMC method is better than that obtained by MCMC.

关 键 词：水文模拟不确定性 残差协方差矩阵 似然函数 自回归模型 MCMC AR-MCMC 融雪径流模型 提孜那甫河流域 

分 类 号：P333.9[天文地球—水文科学]