Muskingum Models’ Development and their Parameter Estimation: A State-of-the-art Review

Wang, Wenchuan; Tian, Weican; Xu, Dongmei; Chau, Kwok-wing; Ma, Qiang; Liu, Changjun

doi:10.1007/s11269-023-03493-1

Cited by 12 publications

(3 citation statements)

References 74 publications

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“…The Muskingum method is a channel confluence algorithm based on tank storage equation and water balance equation and widely used in basin routing because of its simplicity, high efficiency, and relatively low data requirement [32]. The segmental Muskingen method, Muskingum-Cunge method with the nonlinear calculus [33][34][35], and the meshing calculus, as well as the Muskingum-Cunge-Todini with variable parameter method [36,37] were proposed successively.…”

Section: Confluence Calculation Method-the Muskingum Methodsmentioning

confidence: 99%

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

Zhao,

Wang,

Zhang

et al. 2024

Sustainability

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With the advancement of society and the impact of various factors such as climate change, surface conditions, and human activities, there has been a significant increase in the frequency of extreme rainfall events, leading to substantial losses from flood disasters. The presence of numerous small and medium-sized water conservancy projects in the basin plays a crucial role in influencing runoff production and rainwater confluence. However, due to the lack of extensive historical hydrological data for simulation purposes, it is challenging to accurately predict floods in the basin. Therefore, there is a growing emphasis on flood simulation and forecasting that takes into account the influence of upstream water projects. Moushan Reservoir basin is located in a hilly area of an arid and semi-arid region in the north of China. Flooding has the characteristics of sudden strong, short confluence time, steep rise, and steep fall, especially floods caused by extreme weather events, which have a high frequency and a wide range of hazards, and has become one of the most threatening natural disasters to human life and property safety. There are many small and medium-sized reservoirs in this basin, which have a significant influence on the accuracy of flood prediction. Therefore, taking Moushan Reservoir as an example, this paper puts forward a flash flood simulation method for reservoirs in hilly areas, considering upstream reservoirs, which can better solve the problem of flood simulation accuracy. Using the virtual aggregation method, the 3 medium-sized reservoirs and 93 small upstream reservoirs are summarized into 7 aggregated reservoirs. Then, we construct the hydrological model combining two method sets with different runoff generation and confluence mechanisms. Finally, after model calibration and verification, the results of different methods are analyzed in terms of peak discharge error, runoff depth error, difference in peak time, and certainty coefficient. The results indicate that the flooding processes simulated by the proposed model are in line with the observed ones. The errors of flood peak and runoff depth are in the ranges of 2.3% to 15% and 0.1% to 19.6%, respectively, meeting the requirements of Class B accuracy of the “Water Forecast Code”. Method set 1 demonstrates a better simulation of floods with an average flood peak error of 5.63%. All these findings illustrate that the developed model, utilizing aggregate reservoirs and dynamic parameters to reflect regulation and storage functions, can effectively capture the impact of small water conservancy projects on confluence. This approach addresses challenges in simulating floods caused by small and medium-sized reservoirs, facilitating basin-wide flood prediction.

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Section: Confluence Calculation Method-the Muskingum Methodsmentioning

confidence: 99%

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

Zhao,

Wang,

Zhang

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…The Muskingum method stands out for its simplicity in principle, ease of calculation, and ability to yield calculation results that align well with actual requirements, rendering it one of the more frequently employed flood calculation methods today [37,38]. In flow routing, the downstream flow process is deduced from the upstream flow process, constituting the forward flow routing method.…”

Section: Reverse Flow Routing Modelmentioning

confidence: 99%

Simulation of the Entire Process of an Interbasin Water Transfer Project for Flow Routing

Ye,

Wang,

Xie

et al. 2024

Water

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The flow routing process plays a crucial role in underpinning the execution of real-time operations within interbasin water transfer projects (IWTPs). However, the water transfer process within the supplying area is significantly affected by the time lag of water flow over extended distances, which results in a misalignment with the water demand process in the receiving area. Hence, there is an imperative need to investigate the flow routing patterns in long-distance water transfer processes. While MIKE11(2014 version) software and the Muskingum method are proficient in simulating flow routing within a water transfer network, they fall short in addressing issues arising from mixed free-surface-pressure flows in water transfer pipelines. This study enhanced the capabilities of the MIKE11(2014 version) software and the Muskingum method by introducing the Preissmann virtual narrow gap method to tackle the challenge of simulating mixed free-surface-pressure flows, a task unattainable by the model independently. This approach provides a clear elucidation of hydraulic characteristics within the water transfer network, encompassing flow rates and routing times. Furthermore, this is integrated with the Muskingum inverse method to compute the actual water demand process within the supplying area. This methodology is implemented in the context of the Han River to Wei River Diversion Project (HTWDP). The research findings reveal that the routing time for the Qinling water conveyance tunnel, under maximum design flow rate conditions, is 12.78 h, while for the south and north main lines, it stands at 15.85 and 20.15 h, respectively. These results underscore the significance of the time lag effect in long-distance water conveyance. It is noteworthy that the average errors between simulated and calculated values for the south and north main lines in the flow routing process are 0.45 m³/s and 0.51 m³/s, respectively. Compared to not using the Preissmann virtual narrow gap method, these errors are reduced by 59.82% and 70.35%, indicating a significant decrease in the discrepancy between simulated and calculated values through the adoption of the Preissmann virtual narrow gap method. This substantially improves the model’s fitting accuracy. Furthermore, the KGE indices for the flow routing model are all above 0.5, and the overall trend of the reverse flow routing process closely aligns with the simulated process. The relative errors for most time periods are constrained within a 5% range, demonstrating the reasonability and precision of the model.

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“…The Wilson River (1974) data represent a smooth single-peak hydrograph with a pronounced non-linear relationship, making it widely used to validate the effectiveness of various parameter optimization estimation techniques in the Muskingum model [29,30].…”

Section: Case 1-wilson River (1974)mentioning

confidence: 99%

The Flood Simulation of the Modified Muskingum Model with a Variable Exponent Based on the Artificial Rabbit Optimization Algorithm

Li,

Cui,

Fan

2024

Water

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In order to further improve the accuracy of flood routing, this article uses the Variable Exponential Nonlinear Muskingum Model (VEP-NMM), combined with the Artificial Rabbit Optimization (ARO) algorithm for parameter calibration, to construct the ARO-VEP-NMM flood routing model. Taking Wilson’s (1974) flood as an example, the model calculation results were compared and analyzed with the Muskingum model constructed with seven optimization algorithms. At the same time, six measured floods in the Zishui Basin were selected for model applicability testing. The results show that the ARO algorithm exhibits stronger robustness and search ability compared with other optimization algorithms and can better solve the parameter optimization problem of the Muskingum model. The use of the ARO-VEP-NMM model for flood routing accurately reflects the movement patterns of floods. The Nash coefficient of the Wilson section reached 0.9983, and the average Nash coefficient during the flood validation period in the Zishui Basin was 0.9, further verifying the adaptability and feasibility of the ARO-VEP-NMM model in flood routing. The research results can provide certain references and a theoretical basis for improving the accuracy of flood forecasting.

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Muskingum Models’ Development and their Parameter Estimation: A State-of-the-art Review

Cited by 12 publications

References 74 publications

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

Simulation of the Entire Process of an Interbasin Water Transfer Project for Flow Routing

The Flood Simulation of the Modified Muskingum Model with a Variable Exponent Based on the Artificial Rabbit Optimization Algorithm

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