Over the course of centuries, river systems have been heavily trained for the purpose of safe discharge of water, sediment and ice, and improves navigation. Traditionally, dikes are used to be reinforced and heightene...Over the course of centuries, river systems have been heavily trained for the purpose of safe discharge of water, sediment and ice, and improves navigation. Traditionally, dikes are used to be reinforced and heightened to protect countries from ever higher flood levels. Other types of solutions than technical engineering solutions, such as measures to increase the flood conveyance capacity（e.g., lowering of groynes and floodplains, setting back dikes） become more popular. These solutions may however increase the river bed dynamics and thus impact negatively navigation, maintenance dredging and flood safety. A variety of numerical models are available to predict the impact of river restoration works on river processes. Often little attention is paid to the assessment of uncertainties. In this paper, we show how we can make uncertainty explicit using a stochastic approach. This approach helps identifying uncertainty sources and assessing their contribution to the overall uncertainty in river processes. The approach gives engineers a better understanding of system behaviour and enables them to intervene with the river system, so as to avoid undesired situations. We illustrate the merits of this stochastic approach for optimising lowland river restoration works in the Rhine in the Netherlands.展开更多
基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全...基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全球浅水模式对接,检验了HBFNEn KF同化方法的有效性。同时,对比了集合均方根滤波(En SRF)、HNEn KF(Hybrid Nudging En KF)、HBFNEn KF三种方法在有误差模式中的同化效果。试验结果表明:HBFNEn KF同化方法保留了HNEn KF方法的同化连续性,解决了En KF同化不连续不平滑的问题,同时还有着更快的收敛速度;当采用单变量分析试验时,HBFNEn KF方法的优势最为明显,表明HBFNEn KF能够较好地保持不同模式变量间的平衡。此外,增量场尺度分析结果表明:相比En SRF,HBFNEn KF在大尺度范围有更好的同化效果,同时能够避免在中小尺度范围内出现大量的虚假增量。展开更多
基金The work presented herein was mainly carried out in the framework of the project ’Stochastic modelling of low-land river morphologyfunded under number DCB 5302’ by the Netherlands Foundation for Technical Sciences (STW)+2 种基金the Dutch Ministry of Infrastructure and the Environment for the permission to use the Rhine model and the historical discharge recordsMr. H. Havinga of the Ministry of Infrastructure and the EnvironmentDr. A. Paarlberg of HKV Consultants for their valuable inputs into this project
文摘Over the course of centuries, river systems have been heavily trained for the purpose of safe discharge of water, sediment and ice, and improves navigation. Traditionally, dikes are used to be reinforced and heightened to protect countries from ever higher flood levels. Other types of solutions than technical engineering solutions, such as measures to increase the flood conveyance capacity（e.g., lowering of groynes and floodplains, setting back dikes） become more popular. These solutions may however increase the river bed dynamics and thus impact negatively navigation, maintenance dredging and flood safety. A variety of numerical models are available to predict the impact of river restoration works on river processes. Often little attention is paid to the assessment of uncertainties. In this paper, we show how we can make uncertainty explicit using a stochastic approach. This approach helps identifying uncertainty sources and assessing their contribution to the overall uncertainty in river processes. The approach gives engineers a better understanding of system behaviour and enables them to intervene with the river system, so as to avoid undesired situations. We illustrate the merits of this stochastic approach for optimising lowland river restoration works in the Rhine in the Netherlands.
文摘基于前后张驰逼近(Back and Forth Nudging,简称BFN)和集合卡尔曼滤波(En KF)方法,构建了一种新的同化方法 HBFNEn KF(Hybrid Back and Forth Nudging En KF)混合同化方法,并将此同化系统分别与通道浅水模式(shallow water model)和全球浅水模式对接,检验了HBFNEn KF同化方法的有效性。同时,对比了集合均方根滤波(En SRF)、HNEn KF(Hybrid Nudging En KF)、HBFNEn KF三种方法在有误差模式中的同化效果。试验结果表明:HBFNEn KF同化方法保留了HNEn KF方法的同化连续性,解决了En KF同化不连续不平滑的问题,同时还有着更快的收敛速度;当采用单变量分析试验时,HBFNEn KF方法的优势最为明显,表明HBFNEn KF能够较好地保持不同模式变量间的平衡。此外,增量场尺度分析结果表明:相比En SRF,HBFNEn KF在大尺度范围有更好的同化效果,同时能够避免在中小尺度范围内出现大量的虚假增量。
