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Muskingum-Cunge Method with Variable Parameters
Journal of the Hydraulics Division, 1978The Muskingum-Cunge method in which the parameters K and X are allowed to vary in time and space is investigated. A three-point approach and an iterative four-point approach to the calculation of the variable parameters are shown to be sufficiently accurate in the simulation of flood flows.
Victor Miguel Ponce, Vujica Yevjevich
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Optimised Muskingum-Cunge solution method for solute transport with equilibrium Freundlich reactions
Journal of Contaminant Hydrology, 1995Because of its simplicity and efficiency, the Muskingum- Cunge (M-C) method is a widely used modelling tool applied to flood routing problems. However, in the M-C method, there is an infinite number of combinations of spatial and temporal step sizes for which the solution is feasible and not all of these lead to accurate results.
D.A. Barry, K. Bajracharya
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PARAMETER DETERMINATION FOR THE MUSKINGUM‐CUNGE FLOOD ROUTING METHOD1
JAWRA Journal of the American Water Resources Association, 1994ABSTRACT: The proportionality coefficient, K, and the weighing parameter, X, required for the Muskingum‐Cunge Flood Routing Method are dependent on the hydraulic characteristics of the channel and the dynamic characteristic of the flood wave. This work focuses on the determination of the Muskingum‐Cunge Flood Routing Method parameters for streams ...
Rolando Bravo +2 more
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Application of the geological streamflow and Muskingum Cunge models in the Yala River Basin, Kenya
Journal of Agriculture, Science and Technology, 2015The nature of surface runoff and its effects in the watershed can be represented by the application of hydrologic and hydraulic models. In this study, the Geological Streamflow Model (GeoSFM) and the Muskingum Cunge (M-C) model were used to model the hydrologic processes of the Yala river network.
Kiluva, VM +3 more
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2023
Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models — particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables.
Tadd Bindas +7 more
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Recently, rainfall-runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid physics-NN models — particularly, a genre called differentiable modeling that intermingles NNs with physics to learn relationships between variables.
Tadd Bindas +7 more
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Hydrological Sciences Journal, 1999
Abstract For the sustainable management of water quantity and quality, a hydrological model that can simulate the hydro-environmental dynamics of river basins at arbitrary locations is valuable. There are several streamflow simulation models suitable for such a purpose. Yet the lack of data commonly poses a serious problem for their application.
KUNIYOSHI TAKEUCHI +2 more
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Abstract For the sustainable management of water quantity and quality, a hydrological model that can simulate the hydro-environmental dynamics of river basins at arbitrary locations is valuable. There are several streamflow simulation models suitable for such a purpose. Yet the lack of data commonly poses a serious problem for their application.
KUNIYOSHI TAKEUCHI +2 more
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Discussion of “Muskingum-Cunge Method with Variable Parameters”
Journal of the Hydraulics Division, 1979Roland K. Price, Judith E. Slade
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World Environmental and Water Resources Congress 2012, 2012
Muthiah Perumal, Bhabagrahi Sahoo
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Muthiah Perumal, Bhabagrahi Sahoo
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Recent advancements in flow routing models have enabled learning from big data using differentiable modeling techniques. However, their application remains constrained to smaller basins due to limitations in computational memory and hydrofabric scaling.
Tadd Bindas +4 more
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Tadd Bindas +4 more
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Closure to “Muskingum-Cunge Method with Variable Parameters”
Journal of the Hydraulics Division, 1980Victor Miguel Ponce, Vujica Yevjevich
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