城镇化平原河网水动力参数动态反演
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摘要
随着中国平原河网地区城镇化建设的不断推进,洪水实时模拟和预测预警成为了城镇地区雨洪管理的主要研究内容和重要技术支撑,其中水动力参数动态反演是洪水实时模拟和预测预警的难点之一。作者利用糙率先验知识,探索了城镇化平原区河网水动力模型重要参数糙率的动态反演技术。首先,基于糙率空间变化的连续性和缓变性,提出了以糙率空间分布平滑性作为先验知识,建立了表征糙率空间变化的平滑度矩阵。然后,将糙率空间分布平滑性作为软约束条件,并结合适用于非线性动态系统实时校正的扩展卡尔曼滤波算法,建立了一个稳健的城镇化平原河网糙率参数动态反演模型,从而进行河网水动力模型重要参数糙率的动态反演。通过一个四级河道组成的河网的应用分析,系统检验了糙率空间分布平滑度权重系数、糙率初始估值和测站个数对河道糙率动态反演效果的影响。结果表明:1)通过调整糙率空间分布平滑度权重系数,可以有效控制糙率动态反演的稳定性和空间反演特性;2)靠近监测点的河道糙率反演值趋于真实值,远离监测点的河道糙率反演值趋于空间分布平滑;3)靠近监测点的河道糙率反演值受初始估值影响较小,远离监测点的河道糙率反演值受初始估值影响较大;4)糙率初始估值越精确,监测点个数越多,则动态反演开始阶段的波动调整幅度越小,糙率系统误差越小,反演效果越好。所提出的模型综合了河道糙率先验知识和糙率反演的特征,可有效用于城镇化平原河网糙率参数的动态反演。
With the continuous development of urbanization plain of China, the real-time flood simulation and forecasting has become the main research content and important technical support for flood management in urbanization plain. The dynamic inversion of hydrodynamic parameters is one of the difficulties in real-time flood simulation and forecasting. Based on prior knowledge of rive roughness, this paper explored an efficient dynamic inversion technique for estimating river roughness parameters. Firstly, based on the continuity and slowness of spatial variation of river roughness, a prior knowledge that used smoothness matrix representing spatial variation of roughness was proposed. Then, taking the prior knowledge as a soft constraint condition, and combining with the extended Kalman filter algorithm for real-time correction of nonlinear dynamic system, a robust dynamic inversion model of river network roughness parameters in urbanization plain was developed. Through an example analysis, the influences of weight of roughness smoothness item, initial roughness estimation and station number on dynamic inversion results were systematically examined. The results show that: 1) the stability and spatial inversion characteristics of river roughness parameters can be effectively controlled by adjusting weight of roughness smoothness item; 2) the inversed roughness values of channels near stations tend to be true, and the ones of channels far from stations tend to be smooth in spatial distribution; 3) the inversed roughness values of channels near stations are less affected by the initial roughness estimation, while the ones of channels far from stations are more affected by the initial roughness estimation; 4)the more accurate the initial roughness estimation and the more the number of stations, the smaller the systematic error of inversed roughness, the smaller the amplitude of fluctuation adjustment at the beginning of dynamic inversion, and the better the inversion effect. The proposed model can be effectively applied to the dynamic inversion of river roughness parameters in urbanization plain.
With the continuous development of urbanization plain of China, the real-time flood simulation and forecasting has become the main research content and important technical support for flood management in urbanization plain. The dynamic inversion of hydrodynamic parameters is one of the difficulties in real-time flood simulation and forecasting. Based on prior knowledge of rive roughness, this paper explored an efficient dynamic inversion technique for estimating river roughness parameters. Firstly, based on the continuity and slowness of spatial variation of river roughness, a prior knowledge that used smoothness matrix representing spatial variation of roughness was proposed. Then, taking the prior knowledge as a soft constraint condition, and combining with the extended Kalman filter algorithm for real-time correction of nonlinear dynamic system, a robust dynamic inversion model of river network roughness parameters in urbanization plain was developed. Through an example analysis, the influences of weight of roughness smoothness item, initial roughness estimation and station number on dynamic inversion results were systematically examined. The results show that: 1) the stability and spatial inversion characteristics of river roughness parameters can be effectively controlled by adjusting weight of roughness smoothness item; 2) the inversed roughness values of channels near stations tend to be true, and the ones of channels far from stations tend to be smooth in spatial distribution; 3) the inversed roughness values of channels near stations are less affected by the initial roughness estimation, while the ones of channels far from stations are more affected by the initial roughness estimation; 4)the more accurate the initial roughness estimation and the more the number of stations, the smaller the systematic error of inversed roughness, the smaller the amplitude of fluctuation adjustment at the beginning of dynamic inversion, and the better the inversion effect. The proposed model can be effectively applied to the dynamic inversion of river roughness parameters in urbanization plain.
引文
[1]Chen Yifan,Cheng Weiping,Jiang Jianqun.A robust river roughness calibration model[J].Journal of Zhejiang University(Engineering Science),2013,47(8):1361-1365.[陈一帆,程伟平,蒋建群.一种稳健的河流糙率反演方法[J].浙江大学学报(工学版),2013,47(8):1361-1365.]
[2]Shen Wuwei,Chen Yifan,Shen Zhendong.Inverse problems of river roughness:Research review and prospect[J].Zhejiang Hydrotechnics,2017,212(4):1-3.[沈五伟,陈一帆,申振东.河道糙率反问题研究回顾与展望[J].浙江水利科技,2017,212(4):1-3.]
[3]Becker L,Yeh W W G.Identification of multiple reach channel parameters[J].Water Resources Research,1973,9(2):326-335.
[4]Wasantha Lal A M.Calibration of riverbed roughness[J].Journal of Hydraulic Engineering,1995,121(9):664-671.
[5]Atanov G A,Evseeva E G,Meselhe E A.Estimation of roughness profile in trapezoidal open channel[J].Journal of Hydraulic Engineering,ASCE,1999,125(3):309-312.
[6]Khatibi R H,Williams J J,Wormleaton P R.Identification problem of open-channel friction parameters[J].Journal of Hydraulic Engineering,1997,123(12):1078-1088.
[7]Ramesh R,Datta B,Bhallamudi S M,et al.Optimal estimation of roughness in open-channel flows[J].Journal of Hydraulic Engineering,2000,126(4):299-303.
[8]Dong Wenjun,Jiang Hengyu,Yu Wenhuan.Parameter identification of manning roughness in one-dimensional flow equation[J].Journal of Tianjin University(Science and Technology),2001,34(2):201-204.[董文军,姜亨余,喻文唤.一维水流方程中曼宁糙率的参数辨识[J].天津大学学报(自然科学与工程技术版),2001,34(2):201-204.]
[9]Cheng Weiping,Liu Guohua.Inverse analysis of channel friction based on generalized inverse theory[J].Journal of Zhejiang University(Engineering Science),2005,39(10):1063-1068.[程伟平,刘国华.基于广义逆理论的河网糙率反演研究[J].浙江大学学报(工学版),2005,39(10):1063-1068.]
[10]Ding Y,Wang S S.Identification of Manning’s roughness coefficients in channel network using adjoint analysis[J].International Journal of Computational Fluid Dynamics,2005,19(1):3-13.
[11]Bilgil A,Altun H.Investigation of flow resistance in smooth open channels using artificial neural networks[J].Flow Measurement and Instrumentation,2008,19(6):404-408.
[12]Li Li,Wang Jiahu,Wang Jianqun,et al.Application of adaptive random search algorithm in roughness inversion of mathematical model for river networks[J].Advances in Science and Technology of Water Resources,2011,31(5):64-67.[李丽,王加虎,王建群,等.自适应随机搜索算法在河网数学模型糙率反演中的应用[J].水利水电科技进展,2011,31(5):64-67.]
[13]Lai R,Fand H,He G,et al.Dual state-parameter optimal estimation of one-dimensional open channel model using ensemble Kalman filter[J].Journal of Hydrodynamics,2013,25(4):564-571.
[14]Butler T,Graham L,Estep D,et al.Definition and solution of a stochastic inverse problem for the Manning’s n parameter field in hydrodynamic model[J].Advances in Water Resources,2015,78:60-79.
[15]Chen Yifan,Cheng Haiyang,Wan Xiaoli,et al.Research on data-assimilation combined with roughness correction for dynamic river system[J].Advances in Water Resources,2015,26(5):731-738.[陈一帆,程海洋,万晓丽,等.结合糙率校正的河网水情数据同化[J].水科学进展,2015,26(5):731-738.]
[16]Jia Benyou,Wu Shiqiang,Fan Ziwu,et al.Application of particle swarm optimization in parameter calibration of channel hydrodynamic model[J].South-to-North Water Transfers and Water Science&Technology,2018,16(3):143-148.[贾本有,吴时强,范子武,等.粒子群算法在河道水动力模型参数校正中的应用[J].南水北调与水利科技,2018,16(3):143-148.]
[17]Zhan Jiemin,Lu Manying,Li Yuxiang,et al.An efficient,accurate and applicable numerical model for unsteady flows in river networks[J].Journal of Hydrodynamics,2006,21(6):686-692.[詹杰民,吕满英,李毓湘,等.一种高效实用的河网水动力数学模型研究[J].水动力学研究与进展,2006,21(6):686-692.]
[18]Islam A,Raghuwanshi N S,Singh R,et al.Comparison of gradually varied flow computation algorithms for openchannel network[J].Journal of Irrigation and Drainage Engineering,2005,131(5):457-465.
[2]Shen Wuwei,Chen Yifan,Shen Zhendong.Inverse problems of river roughness:Research review and prospect[J].Zhejiang Hydrotechnics,2017,212(4):1-3.[沈五伟,陈一帆,申振东.河道糙率反问题研究回顾与展望[J].浙江水利科技,2017,212(4):1-3.]
[3]Becker L,Yeh W W G.Identification of multiple reach channel parameters[J].Water Resources Research,1973,9(2):326-335.
[4]Wasantha Lal A M.Calibration of riverbed roughness[J].Journal of Hydraulic Engineering,1995,121(9):664-671.
[5]Atanov G A,Evseeva E G,Meselhe E A.Estimation of roughness profile in trapezoidal open channel[J].Journal of Hydraulic Engineering,ASCE,1999,125(3):309-312.
[6]Khatibi R H,Williams J J,Wormleaton P R.Identification problem of open-channel friction parameters[J].Journal of Hydraulic Engineering,1997,123(12):1078-1088.
[7]Ramesh R,Datta B,Bhallamudi S M,et al.Optimal estimation of roughness in open-channel flows[J].Journal of Hydraulic Engineering,2000,126(4):299-303.
[8]Dong Wenjun,Jiang Hengyu,Yu Wenhuan.Parameter identification of manning roughness in one-dimensional flow equation[J].Journal of Tianjin University(Science and Technology),2001,34(2):201-204.[董文军,姜亨余,喻文唤.一维水流方程中曼宁糙率的参数辨识[J].天津大学学报(自然科学与工程技术版),2001,34(2):201-204.]
[9]Cheng Weiping,Liu Guohua.Inverse analysis of channel friction based on generalized inverse theory[J].Journal of Zhejiang University(Engineering Science),2005,39(10):1063-1068.[程伟平,刘国华.基于广义逆理论的河网糙率反演研究[J].浙江大学学报(工学版),2005,39(10):1063-1068.]
[10]Ding Y,Wang S S.Identification of Manning’s roughness coefficients in channel network using adjoint analysis[J].International Journal of Computational Fluid Dynamics,2005,19(1):3-13.
[11]Bilgil A,Altun H.Investigation of flow resistance in smooth open channels using artificial neural networks[J].Flow Measurement and Instrumentation,2008,19(6):404-408.
[12]Li Li,Wang Jiahu,Wang Jianqun,et al.Application of adaptive random search algorithm in roughness inversion of mathematical model for river networks[J].Advances in Science and Technology of Water Resources,2011,31(5):64-67.[李丽,王加虎,王建群,等.自适应随机搜索算法在河网数学模型糙率反演中的应用[J].水利水电科技进展,2011,31(5):64-67.]
[13]Lai R,Fand H,He G,et al.Dual state-parameter optimal estimation of one-dimensional open channel model using ensemble Kalman filter[J].Journal of Hydrodynamics,2013,25(4):564-571.
[14]Butler T,Graham L,Estep D,et al.Definition and solution of a stochastic inverse problem for the Manning’s n parameter field in hydrodynamic model[J].Advances in Water Resources,2015,78:60-79.
[15]Chen Yifan,Cheng Haiyang,Wan Xiaoli,et al.Research on data-assimilation combined with roughness correction for dynamic river system[J].Advances in Water Resources,2015,26(5):731-738.[陈一帆,程海洋,万晓丽,等.结合糙率校正的河网水情数据同化[J].水科学进展,2015,26(5):731-738.]
[16]Jia Benyou,Wu Shiqiang,Fan Ziwu,et al.Application of particle swarm optimization in parameter calibration of channel hydrodynamic model[J].South-to-North Water Transfers and Water Science&Technology,2018,16(3):143-148.[贾本有,吴时强,范子武,等.粒子群算法在河道水动力模型参数校正中的应用[J].南水北调与水利科技,2018,16(3):143-148.]
[17]Zhan Jiemin,Lu Manying,Li Yuxiang,et al.An efficient,accurate and applicable numerical model for unsteady flows in river networks[J].Journal of Hydrodynamics,2006,21(6):686-692.[詹杰民,吕满英,李毓湘,等.一种高效实用的河网水动力数学模型研究[J].水动力学研究与进展,2006,21(6):686-692.]
[18]Islam A,Raghuwanshi N S,Singh R,et al.Comparison of gradually varied flow computation algorithms for openchannel network[J].Journal of Irrigation and Drainage Engineering,2005,131(5):457-465.
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