## 3 Methodology
### Discharge Simulations Using Multiple Precipitation Data Sets under Two Model Setup Scenarios  
Based on the findings of statistical evaluations of multiple SPPs against the ground precipitation observations, the comparatively better SPPs in near- and post-real-time versions were selected to drive the GXAJ model for 3-hourly streamflow simulations. The hydrological performance of these SPPs was evaluated according to two model setup scenarios.  
Scenario I: Discharge simulations with rain-gauge-calibrated parameters. The GXAJ model parameters were calibrated using the rain-gauge-based precipitation data. Then, the GXAJ model was replaced by different SPPs to execute discharge simulations using the rain-gauge-calibrated model parameters. This method was widely applied to hydrological simulations over gauged basins [19].  
Scenario II: Discharge simulations with SPP-specific parameters. The GXAJ model parameters were recalibrated using different SPPs, and the model with the SPP-specific parameters were used to perform streamflow simulations. This approach is an alternative for hydrological modeling in ungauged basins, wherein only SPPs are available [19].  
The calibration period in this study was from 1 April 2014 to 31 December 2015, and the validation period was from 1 January 2016 to 31 December 2016. The watershed was divided into five computational blocks (sub-basins) according to the drainage area controlled by the five hydrological stations. The GXAJ model parameters in each computational block were calibratedusing the shuffled complex evolution (SCE-UA) automatic optimization method [(69; 70)]. At all grid cells with each sub-basin, the parameter values are defined to be spatially uniform. Maximum NSE was used as the objective function for hydrological model optimization. Given that this objective function tends to provide high importance to high flows, the optimized hydrological model may not accurately simulate low-flow processes. [PERSON] et al. [(47)] suggested the use of the maximum sum of NSE and log-transformed NSE as the objective function and found that the optimized GXAJ model can comprehensively characterize high- and low-flow processes in the Chindwin River basin. However, this method tends to sacrifice the accuracy of high-flow simulations to some degree. The maximum of NSE was still used as the objective function, because the present study focused on flood simulations. Finally, the simulated 3-hourly discharge time series using different precipitation inputs under each simulation scenario were compared with the observations to assess the hydrologic utility of multiple SPPs in the study area.  
Although the SCE-UA algorithm has been proved to be effective, consistent, and efficient in retrieving the parameter values of a hydrologic model with a given objective function, it has a critical deficiency in population degeneration, which assumes that the global optimum is located in a linearly operated subspace of the parameter space [(71)]. As a result, this assumption is likely to cause the situation where SCE-UA misses the global optimum or misconverges. To locate a hydrological parameter set that best possibly approximates the global optimum, 20 independently initialized runs of the SCE-UA optimizations were executed. For each run, optimization was terminated when iteration reached 10,000 times or population converged within 0.1% of the parameter space. Finally, the parameter set in the optimization run with the highest objective function value was adopted for discharge simulation.