## 3 Materials and Methods
### Hydro-Meteorological Assessment  
The reliability assessment of the IMERG products is divided into two main parts: (1) meteorological assessment--comparison with precipitation gauges; and (2) hydrological assessment--validation of the SWAT model simulations with observed streamflow. The assessment was conducted in monthly and daily time-scales. The precipitation evaluation periods were focused for the entire period (12 March 2014 to 31 December 2016) and the 2014-2015 flood period (14 December 2014 to 3 January 2015). Four continuous statistical metrics including Correlation Coefficient (CC), Root Mean Square Error (RMSE), Relative Bias (RB), and Mean Absolute Difference (MAD) were used to evaluate the reliability of the three IMERG products. The main function of CC is to test the degree of linear correlation between the IMERG products and precipitation gauges. The CC value varies between \(-1\) (perfect negative correlation) and 1 (perfect positive correlation), while a value of 0 shows no correlation. The average absolute error magnitude of the IMERG products was tested using RMSE and MAD, where a smaller value indicated a better performance. RB measures the systematic bias of the IMERG products, with positive and negative values showing an overestimation and underestimation, respectively. Generally, the IMERG products can be regarded as a reliable source, if a CC value is greater than 0.7 and a RB value ranges from \(-10\%\) to \(10\%\)[31]. Five categorical statistical metrics including Probability of Detection (POD), False Alarm Rate (FAR), Critical Success Index (CSI), Equitable Threat Score (ETS), and Accuracy (ACC) were used to evaluate the precipitation detection ability of the IMERG products. A threshold of 1 mm/day was used to differentiate precipitation and non-precipitation days in KRB. POD measures the fraction of the SPPs correctly detected precipitation observed in the precipitation gauges. FAR indicates what the fraction of the SPPs detected precipitation did not observe in the precipitation gauges. ACC represents the fraction of the SPPs that correctly detected precipitation and non-precipitation days. CSI and ETS describe how well the precipitation days detected from SPPs correspond to the gauges, and the latter approach considers the hits due to chance. For the perfect score, POD = 1, CSI = 1, ETS = 1, ACC = 1, and FAR = 0. Detailed calculations of the continuous and categorical statistical metrics can be found in several SPPs validation studies [9; 32; 33]. Hydrological assessment of the IMERG products was conducted using the SWAT model. Initially, the baseline testing period of the SWAT model was set up from 2010 to 2016. The 2010 year was used as the initialization year, while the following three years (2011-2013) were considered as the calibration period. The Sequential Uncertainty Fitting algorithm (SUFI-2) in the SWAT Calibration and Uncertainty (SWAT-CUP) tool was used to calibrate the SWAT model. For an iteration, the SWAT model was calibrated with 500 different parameter combinations. At the same time, the global sensitivity analysis approach was integrated to identify the sensitivity of each parameter. The best parameter combination was obtained after running several iterations. Once the model calibration was completed, the three IMERG products were incorporated into the calibrated SWAT model to validate the daily streamflow for the entire period (12 March 2014 to 31 December 2016) and the 2014-2015 flood period (14 December 2014 to 3 January 2015). For the IMERG products, we only considered the pixels over precipitation gauges (Figure 1) to reduce the uncertainty in this assessment. We incorporated the IMERG products into the SWAT model by replacing the gauges' precipitation data with the IMERG precipitation data for the period of 12 March 2014 to 31 December 2016. To evaluate the reliability of SWAT simulations, the Nash-Sutcliffe Coefficient (NSE), Coefficient of Determination (R\({}^{2}\)), and RB were used. [PERSON] et al.