A multilinear discrete Nash-cascade model for stage-hydrograph routing in compound river channels

ABSTRACT

Stream gauge-based information is the foundation for many hydrological applications in a river basin including the aquatic-habitat conservation. A simple two-parameter model for routing streamflow depth (alternatively, stream–stage) hydrographs and estimating corresponding discharge hydrographs in river channels is proposed using the multilinear approach, based on Nash-type discrete-cascade model. The applicability of this model is investigated by extending its framework to the realm of compound cross-section trapezoidal channels for both in-bank and overbank flows by using 20 flood events of the Tiber River in the Umbria region of Central Italy, and subsequently comparing the simulated results with the corresponding simulations of the HEC-RAS (Hydrologic Engineering Center – River Analysis System) hydrodynamic model and observed flow depth hydrographs. The field application, comparative study, and uncertainty and sensitivity analysis of the results demonstrate that the proposed multilinear discrete Nash-cascade stage-hydrograph (MDNS) routing model has the potential for routing floods in real-world rivers and canal irrigation systems, especially in operational mode.     

Rainfall Algorithms Using Oceanic SatelliteObservations from MWHS-2

正1EarthEnvironmental Sciences Department, Vanderbilt University, Nashville, TN 37235, USA2Space ScienceEngineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA     

差分搜索的多线性高光谱图像解混研究

针对高光谱图像解混精度不高和全约束非线性解混耗时长的问题,该文提出了一种基于差分搜索的多线性高光谱图像解混算法。首先,引入多线性混合模型建立全约束解混目标函数,将多线性解混问题转化为最优化问题;再利用差分搜索算法的[0,1]搜索域与"和为1"边界控制机制满足丰度约束条件,从而简化全约束解混目标函数;最后,对简化后的目标函数进行迭代优化求解,进而实现多线性高光谱图像解混。实验结果表明:该算法在保证解混精度的同时减少了全约束非线性解混时间,能够取得较好的解混效果。