The heavy sediment load of the Yellow River makes it difficult to simulate sediment concentration using classic numerical models. In this paper, on the basis of the classic one-dimensional numerical model of open channel flow, a variational-based data assimilation method is introduced to improve the simulation accuracy of sediment concentration and to estimate parameters in sediment carrying capacity. In this method, a cost function is introduced first to determine the difference between the sediment concentration distributions and available field observations. A one-dimensional suspended sediment transport equation, assumed as a constraint, is integrated into the cost function. An adjoint equation of the data assimilation system is used to solve the minimum problem of the cost function. Field data observed from the Yellow River in 2013 are used to test the proposed method. When running the numerical model with the data assimilation method, errors between the calculations and the observations are analyzed. Results show that (1) the data assimilation system can improve the prediction accuracy of suspended sediment concentration; (2) the variational inverse data assimilation is an effective way to estimate the model parameters, which are poorly known in previous research; and (3) although the available observations are limited to two cross sections located in the central portion of the study reach, the variational-based data assimilation system has a positive effect on the simulated results in the portion of the model domain in which no observations are available.
From 1960 to 1999, the river regime in the Lower Yellow River had been the wandering. After the operation of Xiaolangdi Reservoir, the river regime in the Lower Yellow River has experienced some changes because of the reduced discharge and sediment loads. According to the river regime maps, the wandering features of the Lower Yellow River are inhibited and the river channel is becoming stable. In addition, the fractal dimension method is introduced to discriminate the river regime. The river fractal features are able to reflect the features of river system and the fractal dimension is an important variable that can reflect the fractal features. Based on the basic principle of fractal theory, the fractal dimension method is applied to discriminate the river pattern evolution of the Lower Yellow River. The results show that all the fractal dimensions of the three studied reaches decrease from 1-1.35 before 2004 to less 1.05 after 2004, indicating that the river regimes are becoming more regular. It means that the discrimination results fit well with the facts and the fractal dimension method is suitable for the river pattern discrimination.
We have known that many of the main features of river can be captured in a relatively simple cellular computer model. Here we examine some of the detailed characteristics of this model. We show a new tangential angle and special treatment method in curvature channel in a simple cellular model. The water distribute will be determined by the tangential angle which represent the flow direction. And the same tangential angle cellular in curvature reach will be transport in one group in filially. The results show it can make the flow routing conformed to the real river, and solve the question of curve bending coefficient is too large. The results of the routing scheme are compared with field measurements of cross-section, with the predictions of a more close to the real. It’s indicate that the curvature flow routing scheme outlined here is able to overcome some of the limitations of previous simple cellular automata models and may be suitable for use in curvature reach of river modeling water and sediment transport and channel change in complex fluvial environments. As such this research represents a small and ongoing contribution to the field of numerical simulation of curvature channel processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.