# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 4 Results and Discussion
### Evaluation of Precipitation Products' Hydrological Performance using SWAT Model
#### 4.2.1 Evaluation of Model Performance  
The 1998 data were used as a \"warm-up\" period to initialize the model, and the data from the rain gauge observations and the three GPPs (CPC, TRMM 3B42, and CMORPH) from 1999 to 2006 were used for model calibration. With GSMaP, the precipitation from 2001 to 2006 was used for model calibration, with the 2000 dataset used for the warm-up period, due to a lack of obtained data. This calibration procedure was performed at each individual discharge gauge and for each individual GPP. The best model parameter values obtained for each precipitation product for the calibration period are presented in Figure 6 and Table 5.  
The P-factor and R-factor results for the calibration range from 0.68-0.98 and 0.67-1.55, respectively, which reveals that all of the simulations to capture streamflow discharge acquire reasonable uncertainties [45]. The simulated streamflow reproduced by the different GPPs at each individual station is satisfactory, with NSE > 0.67 and R\({}^{2}\) > 0.77 (Table 5). The models using the GSMaP and CPC products as the precipitation data attain excellent performance for the upstream part of the MRB during the calibration period, with NSE and R\({}^{2}\) at Chiange Saen and Luang Prabang being in the range of 0.82 and 0.86, respectively (Figure 6),while the performances with the observation data for the calibration periods are 0.7 for NSE and 0.81 for R\({}^{2}\). These results indicate that the spatial coverage of the rainfall stations in the upstream part of the MRB does not feature a sufficient density to reflect the rainfall characteristics in this region. Calibrating the SWAT model with individual gridded precipitation further improves streamflow simulations \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **Statistics Index** & **Gauge** & **GSMaP** & **TRMM 3B42** & **CMORPH** & **CPC** \\ \hline \multirow{3}{*}{Chiange Saen} & NSE & 0.7 & 0.83 & 0.69 & 0.74 & 0.82 \\  & PBIAS & 6.14 & \(-\)7.16 & \(-\)2.01 & \(-\)12.81 & \(-\)3.61 \\  & R\({}^{2}\) & 0.81 & 0.86 & 0.79 & 0.77 & 0.85 \\ \hline \multirow{3}{*}{Luang Prabang} & NSE & 0.67 & 0.84 & 0.87 & 0.84 & 0.88 \\  & PBIAS & \(-\)26.78 & \(-\)6.25 & \(-\)3.29 & \(-\)4.43 & \(-\)7.78 \\  & R\({}^{2}\) & 0.88 & 0.94 & 0.95 & 0.94 & 0.98 \\ \hline \multirow{3}{*}{Nong Khai} & NSE & 0.93 & 0.84 & 0.93 & 0.93 & 0.93 \\  & PBIAS & 3.65 & \(-\)5.93 & \(-\)2.45 & \(-\)4.62 & \(-\)8.76 \\  & R\({}^{2}\) & 0.97 & 0.94 & 0.97 & 0.95 & 0.96 \\ \hline \multirow{3}{*}{Mukhdan} & NSE & 0.94 & 0.78 & 0.86 & 0.83 & 0.78 \\  & PBIAS & \(-\)7.54 & \(-\)26.64 & \(-\)5.04 & \(-\)17.73 & \(-\)24.54 \\  & R\({}^{2}\) & 0.98 & 0.93 & 0.96 & 0.95 & 0.94 \\ \hline \multirow{3}{*}{Pakse} & NSE & 0.98 & 0.91 & 0.91 & 0.93 & 0.94 \\  & PBIAS & \(-\)3.87 & \(-\)11.21 & \(-\)3.58 & \(-\)4.95 & \(-\)4.86 \\  & R\({}^{2}\) & 0.98 & 0.97 & 0.97 & 0.98 & 0.98 \\ \hline \multirow{3}{*}{Stung Treng} & NSE & 0.93 & 0.86 & 0.91 & 0.89 & 0.88 \\  & PBIAS & \(-\)8.23 & \(-\)21.51 & \(-\)5.62 & \(-\)8.47 & \(-\)10.44 \\ \cline{1-1}  & R\({}^{2}\) & 0.98 & 0.97 & 0.99 & 0.97 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics index for the models of the Mekong River Basin using SWAT at six different stations. Figure 6: Nash–Sutcliffe efficiency coefficient (NSE) and PBIAS of values of the hydrological stations. compared to the model calibrated with gauges for the sparse-rain gauge region in the upstream part of the MRB. Although individual precipitation may possess large biases, calibration may mitigate the errors of such biases. Over the northern section of the river basin with sparse gauges, the use of GPPs is more reasonable than that of gauge-based precipitation products.