A new method for dividing flood period in the variable-parameter Muskingum models

The Muskingum routing model is favored by water engineers owing to its simplicity and accuracy. A large amount of research is done to improve the accuracy of the model. One way to do so is to consider variable hydrological parameters during the flood routing period. In this study, the random selection (RS) method was proposed to divide the flood period of the nonlinear Muskingum model into three sub-periods. The proposed method was based on RS of members in each sub-region. It was applied to rout three flood hydrographs, and the objective function was the sum of squared errors. Comparing the results from the three variable-parameter nonlinear Muskingum model with those from the variable-parameter nonlinear Muskingum models in previous studies, the proposed model optimized the objective function in these hydrographs up to 61%. The uncertainty analysis of Muskingum parameters for Wilson's hydrograph was performed by the fuzzy alpha cut method, and it was found that the uncertainty of the parameter x is greater than the uncertainty of the parameters k and m.

Developing a Novel Parameter-free Optimization Algorithm for Flood Routing

The Muskingum model is a popular hydrologic flood routing method; however, the accurate estimation of Muskingum model parameters is a critical task in the successful and precise implementation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation associated with various complex models including the nonlinear Muskingum model. Among EMOAs, teaching-learning-based optimization (TLBO) is a relatively new parameterless metaheuristic optimization algorithm, inspired by the relationship between teacher and students in a classroom to improve the overall knowledge of a topic in a class. This paper presents an application of TLBO to estimate Muskingum model parameters by minimizing the prediction error of outflow measurements. Several examples evaluate and confirm the successful performance of TLBO for the estimation of Muskingum-routing parameters precisely. The results show TLBO-Muskingum’s high accuracy for estimating accurately Muskingum’s parameters based on the Nash-Sutcliffe Efficiency (NSE) to evaluate the TLBO’s predictive skill with benchmark problems.

Simulation of Rainfall Runoff Process Using HEC-HMS Model for Upper Godavari Basin Maharashtra, India

The Hydrologic Engineering Centers Hydrologic Modeling System (HEC-HMS) is a popularly used watershed model to simulate rainfall- runoff process. Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. It allows to predict the hydrologic response to various watershed management practices and to have a better understanding of the impacts of these practices. It is evident from the extensive review of the literature that the studies on comparative assessment of watershed models for hydrologic simulations are very much limited in developing countries including India. In this study, modified SCS Curve Number method is applied to determine loss model as a major component in rainfall-runoff modeling. The study of HEC-HMS model is used to simulate rainfallrunoff process in Nashik region (Upper Godavari basin), Maharashtra. To compute runoff volume, peak runoff rate, and flow routing methods SCS curve number, SCS unit hydrograph, Exponential recession and Muskingum routing methods are chosen, respectively. The results of the present study indicate that HEC-HMS tool applied to watershed proved to be useful in achieving the various objectives. The study confirmed a significant increase in runoff as a result of urbanization. It is a powerful tool for flood forecasting Index

Variable Parameter Multilinear Muskingum Method: Case Study on the Danube River

The Muskingum method is based on a linear relationship between a channel’s storage and inflow and outflow discharges. The applicability of using travel-time discharge relationships to model the variability of the K parameter in a Muskingum routing model was tested. The new parameter estimation method is based on the relationships between the traveltime parameter (K) and the input discharge for the reach of the Danube River between Devín-Bratislava and Medveďov, which includes the Gabčíkovo hydropower scheme. The variable parametrisation method was compared with the classical approach. The parameter X was taken as the average of its values from a small set of flood waves, K was estimated as a function of the travel-time parameter and discharge, which was optimized for one flood wave. The results were validated using the Nash-Sutcliffe coefficient on 5 floods. The results obtained by these methods were satisfactory and, with their use, one could reduce the amount of data required for calibration in practical applications.

DEVELOPMENT OF RIVER FLOOD ROUTING MODEL USING NON-LINEAR MUSKINGUM EQUATION AND EXCEL TOOL 'GANetXL'

Flood routing is of utmost importance to water resources engineers and hydrologist. Muskingum model is one of the popular methods for river flood routing which often require a huge computational work. To solve the routing parameters, most of the established methods require knowledge about different computer programmes and sophisticated models. So, it is beneficial to have a tool which is comfortable to users having more knowledge about everyday decision making problems rather than the development of computational models as the programmes. The use of micro-soft excel and its relevant tool like solver by the practicing engineers for normal modeling tasks has become common over the last few decades. In excel environment, tools are based on graphical user interface which are very comfortable for the users for handling database, modeling, data analysis and programming. GANetXL is an add-in for Microsoft Excel, a leading commercial spreadsheet application for Windows and MAC operating systems. GANetXL is a program that uses a Genetic Algorithm to solve a wide range of single and multi-objective problems. In this study, non-linear Muskingum routing parameters are solved using GANetXL. Statistical Model performances are compared with the earlier results and found satisfactory.

DEVELOPMENT OF RIVER FLOOD ROUTING MODEL USING NON-LINEAR MUSKINGUM EQUATION AND EXCEL TOOL 'GANetXL'

Flood routing is of utmost importance to water resources engineers and hydrologist. Muskingum model is one of the popular methods for river flood routing which often require a huge computational work. To solve the routing parameters, most of the established methods require knowledge about different computer programmes and sophisticated models. So, it is beneficial to have a tool which is comfortable to users having more knowledge about everyday decision making problems rather than the development of computational models as the programmes. The use of micro-soft excel and its relevant tool like solver by the practicing engineers for normal modeling tasks has become common over the last few decades. In excel environment, tools are based on graphical user interface which are very comfortable for the users for handling database, modeling, data analysis and programming. GANetXL is an add-in for Microsoft Excel, a leading commercial spreadsheet application for Windows and MAC operating systems. GANetXL is a program that uses a Genetic Algorithm to solve a wide range of single and multi-objective problems. In this study, non-linear Muskingum routing parameters are solved using GANetXL. Statistical Model performances are compared with the earlier results and found satisfactory.

Parameter Estimation of Nonlinear Muskingum Models Based on OEPSO

In the past, various methods have been used to estimate the parameters in the nonlinear three-parameter Muskingum model to allow the model to more closely approximate a nonlinear relation compared to the original two-parameter Muskingum model. In this study, the particle swarm optimization algorithm based on the organizational evolutionary (OEPSO), which the evolutional operations are acted on organizations directly in the algorithm, and gained the global convergence ends through competition and cooperation, and overcome the shortcomings of the traditional PSO, is introduced. The OEPSO is proposed for the purpose of estimating the parameters of nonlinear Muskingum routing model. The performance of this approach is compared with other reported parameter estimation techniques. Results of the application of this approach to an example with high nonlinearity between storage and weighted-flow, show that the OEPSO approach is efficient in estimating parameters of the nonlinear routing models.