This study was conducted in Bina river (an important tributary of the Betwa River) basin, in Bina province, Madhya Pradesh, India (Figure 1). Bina River originates from Begumganj block of Raisen district and enters Sagar district at Rahatgarh block and traverses through Khurai and Bina tehsil before the confluence with river Betwa near Basoda town in Vidisha district [13]. Bina basin falls between 23˚3'N to 24˚3'N latitudes and 78˚1'E to 78˚6'E longitudes having a total geographical area of 2822 sq∙km.

Several hydrological deterministic models have been developed to simulate the rainfall-runoff process for river watersheds, however, most have complicated structures and need various observed data. Streamflow simulation using the SWAT model was performed in the same river basin and the abovementioned

drawbacks were experienced. The MIKE11-NAM rainfall-runoff model has been broadly employed in many Asian countries not only for the reason that it is fewer data requirements but also because it is decent performance and straightforward structures [14]. However, this hydrological model needs extensive time and effort to calibrate the model parameters through a manual calibration method. A lumped conceptual model of the MIKE 11-NAM Model treats each sub-catchment area as a single homogenous unit [7].

The model structure is shown in Figure 2. It is an imitation of the land phase of the hydrological cycle. The various components of the rainfall-runoff process represent the average values for the entire sub-catchment by continuously accounting for water contents in 4 different but mutually interrelated forms of storage, namely: snow, overland flow, interflow, and baseflow, as shown in Figure 2, is also based on the linear reservoir.

The basic input requirements for the MIKE11-NAM model consist of model parameters, initial conditions, meteorological and hydrological data.

The NAM model simulates the rainfall-runoff process in a lumped fashion so provision was given in combined meteorological data from four gage stations of Bina basin specifically Begumganj, Gairatganj, Gyaraspur and Kurwai into a single time series of weighted averages, in such a manner which represent the entire basin. Bina basin Thiessen polygon map is shown in Figure 3. The areal precipitation was computed by multiplying the precipitation at each station and their calculated percentage weight. The percentage weights were used for the mean areal rainfall input, in the MIKE11-NAM model. The percentage weights input according to their contribution rank into the MIKE11-NAM model are shown in Table 1.

The first step in MIKE 11-NAM modeling is to examine the relationship between precipitation and streamflow, which is crucial for accurate modeling of streamflow. A commonly adopted method is to fit a linear regression line between Runoff and Precipitation and to accept the result if the correlation coefficient is nearer to 1. In this study, the correlation was tested by computing the coefficient of determination (R²) between precipitation and discharge data and it was found as 0.87 as shown in Figure 4. And this value indicated that the precipitation data has a strong positive correlation with discharge data, which was reliable to be used for rainfall-runoff modeling.

Estimation of the runoff coefficient before the calibration process was another important step for gaining a hint about the Overland flow coefficient (CQOF) in the calibration of the model. Runoff coefficient is a part of the precipitation, which is converted into a runoff, was also calculated by dividing annual precipitation by observed annual runoff and shown in Table 2. The values of the runoff coefficient varied from 0.48 to 0.69. The average annual potential evapotranspiration (ET_O) was estimated at 931.366 mm.

The parameters of MIKE11-NAM could not be found direct from measurable quantities of basin characteristics and therefore, model calibration is necessary. Calibration is a process of modifying model parameters to reduce the error between the simulated and observed streamflow. In the MIKE 11-NAM model, a manual way of model calibration is practiced.

In manual calibration, a trial-and-error parameter adjustment is performed, based on a visual judgment by comparing the measured and the predicted discharge. Auto-calibration, the default model parameters were kept the same and the model was run in auto-calibration mode [20]. After several manual calibrations have been made the calibration was done again with very small changes. To check the quality of the results, the calibration was checked for the coefficient of determination (R²), water balance error (%WBL) values and graphically analyzed for the degree of agreement between simulated and observed runoff.

Model validation means evaluating the capability of the calibrated model. According to Refsgaard and Knudsenn [21], a model is said to be validated if its accuracy and predictive capacity in the verification period have been proven to lie within acceptable limits. The verification is implemented by using the new set of observed data and the parameters that have been calibrated in the previous step.

The performance of the model can be examined based on the coefficient of determination (R²), the use of the coefficient of determination is to test the goodness of fit of the model and to assess how well a model explains and predicts future outcomes. Efficiency Index (EI) was another hydrological model assessor as described by Nash and Sutcliffe [22] which had been widely used to detect the model error for the long-term simulation. The EI was developed to evaluate the percentage of accuracy of the simulated values with respect to their observed values. EI values equal to 1 signifies the accurate performance of the model.

EI, R², and %WBL have defined as:

In these equations, and are i^th observed and simulated values, respectively, is mean value for observed and n is the number of samples.

In MIKE 11-NAM model, the most sensitive model parameters are identified by changing the value of one of the model parameters and keeping the value of the remaining parameters constant [23]. The model parameters were picked one after another and their values obtained during the calibration increased and decreased by 10% and 20% to both sides. The analysis results were plotted against the EI and R² of the model and the most sensitive model parameters were identified. The sensitivity of the parameters depends on how it affects the value of EI and R² of the model.

Once the model was set up with the input information, the calibration has been carried out from 1994 to 1996. These optimum values are considered as the representative coefficient to determine the runoff within the catchment area. Table 3 illustrates the final values of parameters that have been adjusted in the calibration process. Figure 5 shows the comparison of simulated and observed discharge data during the past three year’s runoff (1994 to 1996) in rate (m³/s). The comparison shows a good match of simulated data with the observed data and a good overall agreement of the shape of the hydrograph with respect to timing, rate, and volume. Similar results were reported by several authors i.e. Hafezparast et al. [24] simulated Sarsoo river basin streamflow using the MIKE 11-NAM model. They found that a significantly a good agreement between the

observed and simulated flow values. A study by Amir et al. [20] at Fitzroy basin, Australia using the MIKE 11-NAM model found that there is a good hydrographs agreement between observed and simulated discharge which shows the ability of the model to simulate the streamflow the basin. Odiyo et al. [25] simulated Latonyanda River Quaternary catchment (LRQ) streamflow using MIKE 11 NAM model and, found that the observed and the simulated streamflow for LRQ catchment correlated well except for under-prediction of peak events and a few low flows.

The simulated values of different components of the hydrological cycle, such as Potential evapotranspiration, Actual evapotranspiration, Groundwater recharge, Runoff, Overland flow, Interflow, and Baseflow simulated during model calibration are shown in Table 4 in the form of water balance (mm). The coefficient of determination (R²) and water balance error (%WBL) for the model calibration was obtained 0.87% and −8.63% respectively as presented in Figure 5, which indicated that the model calibrated efficiently and could simulate the runoff of the catchment. The comparison of observed and simulated mean monthly discharge is shown in Figure 6. It was observed that good agreement between the averages simulated and observed basin discharge.

The 1997 and 1998 time series had been used for checking the validity of the model. During validation, the set of model parameters acquired during the

Q-Obs = Observed Discharge, Q-sim = Simulated Discharge, %diff = Difference between Observed and Simulated Discharge PotEvap = Potential Evapotranspiration, ActEvap = Actual Evapotranspiration, OF = Overland Flow, IF = Inter Flow and BF = Base Flow.

calibration was served to simulate the runoff. Figure 7 and Table 5 represents the results of model validation. The coefficient of determination (R²) and water balance error (%WBL) for the model validation was observed 0.68% and −6.72% respectively as shown in Figure 7, which indicated the capability of the model to estimate runoff for periods outside that used to calibrate the model. A study conducted by Tiwari et al. [26] on the Shipra river basin using the MIKE 11-NAM model found that the model is suitable for simulating the streamflow of the basin for the extended time period.

The simulated values of different components of the hydrological cycle, such as Potential evapotranspiration, Actual evapotranspiration, Groundwater recharge, Runoff, Overland flow, Interflow, and Baseflow simulated during model validation are shown in Table 5 in the form of water balance (mm). The comparison of observed and simulated mean monthly runoff volume is shown in Figure 8, it was detected that good agreement between the averages simulated and observed basin runoff volume for validation period, this shows the model could calibrate runoff outside the calibration periods.

The reliability of the MIKE11-NAM was evaluated based on the coefficient of determination (R²). It is expressed as a value between zero and one and it is obtained 0.87. The reliability of the MIKE11-NAM was also evaluated based on the

Q-Obs = Observed Discharge, Q-sim = Simulated Discharge, %diff = Difference between Observed and Simulated Discharge PotEvap = Potential Evapotranspiration, ActEvap = Actual Evapotranspiration, OF = Overland Flow, IF = Inter Flow and BF = Base Flow.

efficiency index (EI) as described by Nash and Sutcliffe (1996). The EI was developed to evaluate the percentage of accuracy or goodness of the simulated values with respect to their observed values. The efficiency index (EI) equal to 1 indicates the best (perfect) performance of the model. The EI obtained during this study was 0.87. Both evaluation criteria have shown a good result, which means the model is good enough to simulate the runoff of the catchment. Refsgaard and Knudsen [21] showed that the hydrological model is considered as valid when R² > 0.80 and %WBL < 10%.

The sensitivity analysis of the MIKE11-NAM model was carried out by running the model by selecting model parameters one by one as a variable and keeping other parameters constant to identify the most sensitive model parameters. For each simulated runoff time series, EI and R² were calculated using Equations (3) and (4) respectively. The output results were analyzed by plotting EI and R² against the respective model parameters. The model parameters CQOF, CK1,2 and L_max were found as the most influencing and sensitive parameters as shown in Figure 9. Whereas, the remaining parameters were found non-sensitive. Therefore, the significant parameters are CQOF, CK1,2 and L_max because they result in the high differences in EI and R² magnitude. Our results were supported by various authors viz. Rainfall-Runoff modeling conducted by Yadav et al. [27] on the Sher river basin using MIKE-NAM 11 model reported that the model parameters CQOF, CK12 were the most sensitive ones. Shimelis [28] found that U_max, L_max, CQOF, and CK1,2 were the most sensitive MIKE11-NAM model parameters for predicting Upper Guder Catchment streamflow. In this study sensitivity analysis has played a significant role in the improvement in calibration on which parameters must be focused.