Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran.
Department of Geography, University of California, Santa Barbara, CA, USA.
Environ Monit Assess. 2018 Apr 24;190(5):306. doi: 10.1007/s10661-018-6686-3.
Reverse stream flood routing determines the upstream hydrograph in a stream reach given the downstream hydrograph. The Muskingum model of flood routing involves parameters that govern the routed hydrograph. These parameters are herein estimated using simulation methods coupled with optimization tools to achieve optimized parameters. Different simulation methods are shown to perform unequally in the estimation of nonlinear Muskingum parameters. This paper presents two simulation methods for nonlinear Muskingum reverse flood routing: (1) Euler equations and (2) Runge-Kutta 4th order equations. Moreover, the generalized reduced gradient (GRG) is used as the optimization tool that minimized the sum of the squared deviations (SSQ) between observed and routed inflows in a benchmark flood routing problem. Results show the Runge-Kutta 4th order equations yield better routed hydrographs with smaller SSQ than obtained in previous research and with the first simulation method (Euler equations).
反向水流路径确定给定下游水流图的河流河段中的上游水流图。洪水路由的马斯京根模型涉及控制路由水流图的参数。使用模拟方法和优化工具来估算这些参数，以实现优化的参数。不同的模拟方法在估计非线性马斯京根参数方面表现出不平等。本文提出了两种用于非线性马斯京根反向洪水路由的模拟方法：（1）欧拉方程和（2）龙格-库塔 4 阶方程。此外，广义简约梯度（GRG）用作优化工具，使观测到的和路由的流入之间的平方偏差（SSQ）之和最小化。结果表明，与之前的研究和第一种模拟方法（欧拉方程）相比，龙格-库塔 4 阶方程产生的路由水流图具有更小的 SSQ。
