Muskingum Routing Research Articles

Parallel river channel routing computation based on a straightforward domain decomposition of river networks

River routing is an important component of hydrological models, and is a time-consuming task since it is usually based on finer-scale river channel networks and runs with shorter time steps. In order to utilize the parallel computing capabilities of today’s computers, it is necessary to decompose river networks into multiple domains and allocate them to multiple parallel processor cores to process. In this study, a straight-forward domain decomposition method is proposed for an efficient Muskingum routing algorithm on an arbitrary river network of tree-like hierarchical structure: evenly split the upstream-to-downstream sorted river segments into multiple blocks (domains). Within each domain, the cross-domain segments (CDSs), which are defined as those segments receiving inflows from other domains, need to be identified. For such domain decomposition, three parallelization schemes (the standard scheme, the full-parallelization scheme and the simplified scheme) of the Muskingum river routing are proposed. The total number of CDSs increases with the increasing number of domains. For the standard scheme, when too many CDSs are produced, the computation time that saved by the parallelization in its first step should be canceled out by the serial processing in its second step. The sorting order of river segments also affects execution speed and output errors: a depth-first traverse generates much fewer CDSs than a width-first traverse. The number of CDSs were analyzed on decomposing the networks of the Yellow River and the Mississippi, and the optimal number of domains is close to 16 (Yellow River, 22′750 river segments) and 32(Mississippi, 64′579 river segments), respectively. The computing time over different numbers of domains was also tested, based on the parallel execution by both multicore CPU and GPU. The errors produced by the full-parallelization scheme and the simplied scheme are relatively small, especially for the output at the mainstem segments. The parallel river routing can correctly produce the result as expected, and the application to actual river flow modeling produces reasonable outputs.

Flood modelling due to climate change impact in the Logawa watershed

Rainfall change is one of the results of global climate change. It is required to analyse the impact of rainfall changes on flood risks. The aims of this research are (i) to model the watershed using the HECHMS hydrological model, (ii) to apply the scenario-neutral approach, (iii) to perform the frequency analysis using simulated discharges, and (iv) to determine the response and sensitivity of the watershed due to rainfall changes. This research uses a quantitative method using secondary data. The data required in this research are the watershed's geospatial and biogeophysical data. The simulation methods used in this model are the user Hyetograph Model, SCS Curve Number, Clark’s Unit Hydrograph, Recession Baseflow, and Muskingum Routing. The results of this research are (i) the hydrological model of the watershed with a percent bias score of 19.18% and an NSE score of 0.218; (ii) 130 total scenarios as the result of the RStudio using the scenario-neutral approach; (iii) the discharge result from frequency analysis with return periods of 1.1, 2, 5, 10, 20, 30, 40, 50, 100, and 200 years; and (iv) the watershed’s sensitivity with the average highest maximum discharge from November to April and the average lowest maximum discharge from May to October.

A Coupled Dynamic System Inversion Model for Higher Accuracy in Flood Forecasting

AbstractFlood routing is a critical component of real‐time flood forecasting and plays a pivotal role in reducing economic and life losses due to flooding. However, flood routing always possesses errors because it is affected by various sources of uncertainty, which are transmitted and accumulated in the routing process from upstream to downstream, particularly for long or complex river systems. Thus, a real‐time error correction is rapidly becoming a key measure for reducing errors and improving flood forecasting accuracy. In this study, a dynamic system inversion (DSI) model was developed as a new correction method to update forecasting errors for long river systems. The new method utilizes all observed, forecasted, and corrected information at upstream stations and couples the matrix‐based Muskingum routing method with an error‐inversed updating technique to form the DSI equation. Multisource spatiotemporal errors of the river system are simultaneously combined into the DSI equation, and the discharges at the downstream station are reforecasted, thereby providing error correction in flood routing. Based on a case study on the Le'an River (China), the DSI model performed better than the traditional autoregressive (AR) model based on five metrics for both small and large flood events. Moreover, with an increase in lead time, the metrics of AR deteriorated rapidly, whereas that of DSI deteriorated slightly. In conclusion, the proposed DSI model is a sophisticated and robust method for flood routing and provides a new alternative for error correction in flood routing simulations of complex river systems.

Multivariate Dam-Site Flood Frequency Analysis of the Three Gorges Reservoir Considering Future Reservoir Regulation and Precipitation

Under a changing environment, the current hydrological design values derived from historical flood data for the Three Gorges Reservoir (TGR) might be no longer applicable due to the newly-built reservoirs upstream from the TGR and the changes in climatic conditions. In this study, we perform a multivariate dam-site flood frequency analysis for the TGR considering future reservoir regulation and summer precipitation. The Xinanjiang model and Muskingum routing method are used to reconstruct the dam-site flood variables during the operation period of the TGR. Then the distributions of the dam-site flood peak and flood volumes with durations of 3, 7, 15, and 30 days are built by Pearson type III (PIII) distribution with time-varying parameters, which are expressed as functions of both reservoir index and summer precipitation anomaly (SPA). The multivariate joint distribution of the dam-site flood variables is constructed by a 5-D C-vine copula. Finally, by using the criteria of annual average reliability (AAR) associated with the exceedance probabilities of OR, AND and Kendall, we derive the multivariate dam-site design floods for the TGR from the predicted flood distributions during the future operation period of the reservoir. The results indicate that the mean values of all flood variables are positively linked to SPA and negatively linked to RI. In the future, the flood mean values are predicted to present a dramatic decrease due to the regulation of the reservoirs upstream from the TGR. As the result, the design dam-site floods in the future will be smaller than those derived from historical flood distributions. This finding indicates that the TGR would have smaller flood risk in the future.

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

Abstract 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 framework for flood routing

The Muskingum model is a popular hydrologic flood routing technique; however, the accurate estimation of model parameters challenges the effective, precise, and rapid-response operation of flood routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation task associated with a wide range of complex models including the nonlinear Muskingum model. However, more proficient frameworks requiring less computational effort are substantially advantageous. Among the EMOAs teaching–learning-based optimization (TLBO) is a relatively new, parameter-free, and efficient metaheuristic optimization algorithm, inspired by the teacher-student interactions in a classroom to upgrade the overall knowledge of a topic through a teaching–learning procedure. The novelty of this study originates from (1) coupling TLBO and the nonlinear Muskingum routing model to estimate the Muskingum parameters by outflow predictability enhancement, and (2) evaluating a parameter-free algorithm’s functionality and accuracy involving complex Muskingum model’s parameter determination. TLBO, unlike previous EMOAs linked to the Muskingum model, is free of algorithmic parameters which makes it ideal for prediction without optimizing EMOAs parameters. The hypothesis herein entertained is that TLBO is effective in estimating the nonlinear Muskingum parameters efficiently and accurately. This hypothesis is evaluated with two popular benchmark examples, the Wilson and Wye River case studies. The results show the excellent performance of the “TLBO-Muskingum” for estimating accurately the Muskingum parameters based on the Nash–Sutcliffe Efficiency (NSE) to evaluate the TLBO’s predictive skill using benchmark problems. The NSE index is calculated 0.99 and 0.94 for the Wilson and Wye River benchmarks, respectively.

Analisis Metode Routing terhadap Hidrograf Banjir Sungai Way Sekampung di Way Kunyir Menggunakan HEC-HMS

Flood events often occur in Indonesia so that quite a lot of people researched about the movement of floods and flood monitoring in the Watershed. The location of the study was carried out by the Way Sekampung river in Way Kunyir located in the Pringsewu district, Lampung province and is located downstream of the Batutegi dam. Flood tracing is intended to analyze the chances of a flood in the form of a flood hydrograph with or without using the Routing method and find out the results of some Routing methods in the watershed. The Routing method used in this study is Lag, Lag and K and Muskingum. The HEC-HMS modeling results state that peak discharge data when without Routing is greater than entering the Routing parameter. This is because entering the Routing parameter in modeling is very influential at peak times so that it affects the peak discharge at the watershed outlet. As a result of Muskingum Routing, the peak discharge is lower than before entering the flow routing, this is due to the occurrence of reservoirs along the river so that the peak discharge becomes lower than without Routing. The results of Lag Routing and Lag and K Routing are peak discharge decreased compared to before entering the Routing parameter. what should have happened to the peak discharge with Routing and without Routing remains the same, however, only peak discharge tranlations occur and there is travel time. This happens because there is no debit data at the station being reviewed so it uses rain data instead of HEC-HMS modeling.

Informing hydrological models of poorly gauged river catchments – A parameter regionalization and calibration approach

Development of robust hydrologic-hydrodynamic simulation models is challenging, especially in regions, where in-situ observation data are scarce. Parameter calibration is often a necessary and data-demanding step. Moreover, good calibration performance does not guarantee predictive skill in neighboring geographic regions or new time periods and scenarios. A robust calibration strategy is necessary and must apply an informed parameter regionalization approach, which can transfer parameter values from gauged to ungauged subcatchments, whilst accounting for data availability and quality. In this study, a calibration strategy combining a parameter transfer framework based on catchment similarities with a holistic calibration against hydrological signatures is presented to obtain reliable simulations at all relevant locations within a catchment. The approach is demonstrated for three African river catchments: the Tana, the Upper Niger and the Semliki catchments.A rainfall-runoff model based on Budyko’s concept of limits with up to 11 calibration parameters and a daily simulation time step is used, coupled with a Muskingum routing scheme. Each river catchment is subdivided into so-called hydrological response units (HRU) based on climatic and physiographic characteristics. Multi-mission satellite remote sensing observations are used for the HRU classification: terrain slope derived from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) and the publicly available European Space Agency (ESA) Climate Change Initiative (CCI) land cover map. Subcatchments within each HRU share the same parameters. The number of units is constrained by the number of in-situ stations available. The calibration strategy is a catchment-scale, multi-objective approach.The method improved the overall model performance compared to a simpler, nearest-neighbor regionalization method. In particular, spatial patterns of the hydrological response are more reasonable and performance is improved at validation stations, highlighting the importance of appropriate parameter regionalization within nested catchments. With this method, a wider range of currently available remote sensing data (elevation, precipitation, land cover, etc.) can be exploited in hydrological model development and calibration.

Analisis Metode Routing terhadap Hidrograf Banjir Sungai Way Sekampung di Way Kunyir Menggunakan HEC-HMS

Flood events often occur in Indonesia so that quite a lot of people researched about the movement of floods and flood monitoring in the Watershed. The location of the study was carried out by the Way Sekampung river in Way Kunyir located in the Pringsewu district, Lampung province and is located downstream of the Batutegi dam. Flood tracing is intended to analyze the chances of a flood in the form of a flood hydrograph with or without using the Routing method and find out the results of some Routing methods in the watershed. The Routing method used in this study is Lag, Lag and K and Muskingum. The HEC-HMS modeling results state that peak discharge data when without Routing is greater than entering the Routing parameter. This is because entering the Routing parameter in modeling is very influential at peak times so that it affects the peak discharge at the watershed outlet. As a result of Muskingum Routing, the peak discharge is lower than before entering the flow routing, this is due to the occurrence of reservoirs along the river so that the peak discharge becomes lower than without Routing. The results of Lag Routing and Lag and K Routing are peak discharge decreased compared to before entering the Routing parameter. what should have happened to the peak discharge with Routing and without Routing remains the same, however, only peak discharge tranlations occur and there is travel time. This happens because there is no debit data at the station being reviewed so it uses rain data instead of HEC-HMS modeling.

Assessing the effects of the Three Gorges Dam and upstream inflow change on the downstream flow regime during different operation periods of the dam

AbstractAs the largest hydroelectric dam in the world, the Three Gorges Dam (TGD) has raised wide concerns over the environmental and ecological impacts since its dramatic effect on the downstream flow regime of the Yangtze River. Since 2003, the TGD has progressed from the initial operation period to the full operation period, with different effects on the downstream flow regime over each period. Although the upstream inflow change (USIC) of the TGD is a possibly additional factor affecting the downstream flow regime, this has drawn little attention. This study aims to quantify the individual contributions of the TGD and the USIC to the changes of the downstream flow regime over different operation periods of the dam. Using the Muskingum routing model and the Xin'anjiang rainfall–run‐off model, we reconstruct the discharge unregulated by the TGD for the post‐TGD period from 2003 to 2015. On this basis, the effects of the TGD and the USIC on the downstream flow regime are quantitatively assessed. Benchmarked against the flow regime during the pre‐TGD period from 1955 to 2002, it is found that the TGD and the USIC play considerable and comparable roles in affecting the downstream flow regime during the whole post‐TGD period from 2003 to 2015. Furthermore, the TGD appears to have a limited effect on the downstream flow regime during the initial operation period from 2003 to 2008 relative to the USIC. In contrast, during the full operation period from 2009 to 2015, the TGD plays a dominant role in changing the downstream flow regime, although the effect of the USIC cannot be neglected. The findings of this study are helpful to understand the exact impacts of the TGD on the downstream flow regime, thereby facilitating the development of a rational strategy for operating the dam.

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

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

MUSKINGUM AND LAG METHOD RIVER ROUTING PARAMETERS FOR KLANG RIVER AT KUALA LUMPUR CITY CENTRE DERIVED USING RECORDED HYDROGRAPHS

In this Study, the Muskingum and Lag river routing models were used to estimate the routing coefficients of Klang river using flood records of gauging stations at Leboh Pasar and Sulaiman Bridge, for a river reach of 900m between the two gauging stations. Recorded inflow hydrographs at Leboh Pasar and outflow hydrographs at Suilaman Bridge were used to calibrate and validate the Muskingum and Lag routing models. Best fit routing parameters for the models were derived using the optimization module of HEC-HMS, and the average parameters obtained were used to validate the routing models using different sets of flood events. The outflow hydrographs at Sulaiman Bridge derived from routing models were used to compare with the observed hydrographs by plotting the hydrographs for visual inspections. The ability to produce the observed flow at the Sulaiman Bridge by the routing models were also assessed statistically by calculating the goodness of fit indices from the routed and observed hydrographs. Graphical inspections of routed and observed hydrographs show that the Muskingum and lag models give congruent results in estimating the outflow at Sulaiman Bridge. The results obtained are also in good agreement with the observed flows. However, the goodness of fit indices show that the Muskingum model overall gives a better fit than the Lag.

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

Abstract 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.

On the validity range and conservation properties of diffusion analogy and variable parameter Muskingum

A wide range of real-world flood routing problems can be approached using the Parabolic Approximation, widely recognized as a convenient simplification of the Saint Venant equations. From the Parabolic Approximation, head and discharge-based linear and non-linear advection-diffusion models were also derived by merging mass and momentum conservation equations into a single second-order equation to be used for one-dimensional flood routing. Both head and discharge-based advection-diffusion models were originally derived in their linear forms and extensively studied in the literature because they admit analytical solutions. The discharge-based non-linear models have been used as the basis for the derivation of the variable-parameter Muskingum routing method by setting the truncation error at par with the diffusion parameter. Whereas the linear models are mass-conservative, but inadequately reproduce routing in a variety of cases, the non-linear ones have been recently declared to be non-conservative. In the literature it has also been stated that the variable-parameter Muskingum fails to approximate the discharge-based non-linear discharge based advection-diffusion model on mild slopes. This paper aims at providing a systematic overview of all these models, showing that whereas the non-linear head-based advection-diffusion model, an original version of which is derived here, is in fact conservative, also the discharge-based model, which in principle is not, remains de facto conservative if integrated over a space-time grid with sufficiently small spatial cells. To complete the overview, we also demonstrate that in real-world applications the variable parameter Muskingum gives satisfactory results on mild slopes because lowland rivers in nature are essentially prone to slowly rising hydrographs. The results of all models are intercompared by routing different flood waves through prismatic reference channels with slope 10-4 and smaller.

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

Abstract. Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall–runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the Shuttle Radar Topography Mission digital elevation model (SRTM DEM) and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE (Famine Early Warning System rainfall estimate) and the Tropical Rainfall Measuring Mission (TRMM) 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from (1) historical in situ discharge observations from the period 1953–1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE (Gravity Recovery and Climate Experiment) total water storage change (TWSC). Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR (synthetic aperture radar) imagery water mask and used the observations for validation of the model. The use of new satellite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and the river stage, with an RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM-forced model and 0.87 m for the FEWS-RFE-forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modeling with multi-mission remote sensing from 10 different satellite missions, we obtain new information on an otherwise unstudied basin. The proposed model is the best current baseline characterization of hydrological conditions in the Ogooué in light of the available observations.

Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods

ABSTRACTThis paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models.

An Investigation of Errors in Distributed Models' Stream Discharge Prediction Due to Channel Routing

AbstractOver the summer of 2015, the National Water Center hosted the National Flood Interoperability Experiment (NFIE) Summer Institute. The NFIE organizers introduced a national‐scale distributed hydrologic modeling framework that can provide flow estimates at around 2.67 million reaches within the continental United States. The framework generates discharges by coupling a given Land Surface Model (LSM) with the Routing Application for Parallel Computation of Discharge (RAPID). These discharges are then accumulated through the National Hydrography Dataset Plus stream network. The framework can utilize a variety of LSMs to provide the runoff maps to the routing component. The results obtained from this framework suggested that there still exists room for further enhancements to its performance, especially in the area of peak timing and magnitude. The goal of our study was to investigate a single source of the errors in the framework's discharge estimates, which is the routing component. The authors substitute RAPID which is based on the simplified linear Muskingum routing method by the nonlinear routing component the Iowa Flood Center have incorporated in their full hydrologic Hillslope‐Link Model. Our results show improvement in model performance across scales due to incorporating new routing methodology.

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.