【摘 要】
：
The Yellow River Estuary (YRE) alternatively experienced channel aggradation and degradation during the period 1990-2016.To study the variation in flood discharge capacity during the process of river bed evolution,bankfull characteristic parameters were i
【机 构】
：
State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 43
论文部分内容阅读
The Yellow River Estuary (YRE) alternatively experienced channel aggradation and degradation during the period 1990-2016.To study the variation in flood discharge capacity during the process of river bed evolution,bankfull characteristic parameters were inves-tigated on the basis of measured hydrological data and surveyed cross-sectional profiles,which was crucial for comprehending the processes and the key factors to cause these rapid changes.A reach-averaged method was presented in this study in order to calculate the character-istic bankfull parameters in the YRE,and this method integrated the geometric mean using the logarithmic transformation with a weighted mean based on the distance between the two successive sections.The reach-averaged bankfull parameters in the tail reach of the Yellow River Estuary (the Lijin-Xihekou reach) during the period 1990-2016 were then calculated.Calculated results indicated that the adoption of a concept of reach-averaged bankfull discharge was much more representative than the cross-sectional bankfull discharge,and the results also indicated that bankfull discharges decreased during the process of channel aggradation,and increased during the process of channel degradation.Finally,an empirical formula and a delayed response function were established between the reach-averaged bankfull discharge and the previous 4-year average fluvial erosion intensity during flood seasons,and both of them were adopted to reproduce the reach-averaged bankfull discharges,and calculated results showed high correlations (R2 ＞ 0.8) of these two methods.
其他文献
Why does the 1909 typhoon,Lekima,become so destructive after making landfall in China?Using a newly developed mathematical apparatus,the multiscale window transform (MWT),and the MWT-based localized mutliscale energetics analysis and theory of canonical t
采用十六烷基三甲基溴化铵(CTAB)、十二胺、硅酸乙酯与膨润土反应,制备具有较好吸附性能的多孔黏土异质结构材料(porous clay heterostructures,PCHs),并使用PCHs对挥发性有机化合物甲苯进行动态吸附,以探究甲苯气体初始质量浓度、总流速对于PCHs吸附量的影响.试验结果表明:PCHs相较膨润土,前者比表面积有极大增加,总孔容和表面孔道分布增加,最可几孔径减小,且对甲苯气体吸附容量明显提升;当甲苯气体初始质量浓度越高、总流速越大时,PCHs对甲苯的饱和吸附量越大.
To investigate the paleoenvironmental controls on organic matter accumulation of Upper Paleozoic shales in the eastern Ordos Basin,China,26 shale samples were collected from two wells drilled into the Shanxi and Taiyuan Formations.The total organic carbon
利用Lyapunov泛函法讨论高度非线性比例型混杂随机微分方程在时滞反馈控制下稳定所需满足的条件.当系数满足局部Lipschitz条件和Khasminskii型条件时,通过设计满足一定条件的时滞反馈控制器构造出适当的时滞量,验证受控后系统存在唯一解且稳定.通过一个算例验证了结论的有效性.
Understanding the coupling relationship between ecosystem services and poverty livelihoods is a prerequisite for poverty eradication and ecological protec-tion in China.In this paper,we intended to quantify the coupling relationship between ecosystem serv
Estimating horizontal winds in and around typhoons is important for improved monitoring and prediction of typhoons and mitigating their damages.Here,we present a new algorithm for estimating typhoon winds using multiple satellite observations and its appl
Human activity transforms a land surface into a complex surface where artificial and natural landforms coexist and continuous and emergent landforms merge.In this background,the problems of conventional digital elevation models (DEMs),such as morphologica
为降低控制成本,同时考虑到现实中的反馈控制信号通常带有延迟,研究了带有马尔可夫切换的非线性连续时间变时滞系统的镇定问题,设计了一种状态和模态离散观测并带有状态延迟的反馈控制器.通过构造有效的Lyapunov泛函,给出了系统在均方意义下H∞稳定、渐近稳定和指数稳定的充分条件,得到含延迟观测时间间隔的上界.最后给出一个数值算例说明所得结论的有效性.
The risk of wind waves in a bay is often overlooked,owing to the belief that peninsulas and islands will inhibit high waves.However,during the passage of a tropical cyclone,a semi-enclosed bay is exposed to two-directional waves:one generated inside the b
Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles (AVs).However,it is challenging to adopt mul