# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 4 Results and Discussion
### Statistical Evaluation of GPPs
#### 4.1.3 Daily Comparison  
As shown in Table 4, in terms of the daily average precipitation, TRMM 3B42 has the smallest average errors (0.08 mm/day overestimation), while GSMaP has the largest (3.52 mm/day by GSMaP compared with 4.61 mm/day by observation). Similarly, TRMM 3B42 overestimates precipitation overall, with a PBIAS value of 4%, showing the best performance, while CMORPH, CPC, and GSMaP underestimate precipitation, with a PBIAS value of -16%, -15%, and -12%, respectively. With RMSD and MAE, the TRMM product performs worst, with large overestimations for RMSD of 12.19 mm/day and MAE of 5.24 mm/day; the other products--CMORPH, CPC, and GSMaP--performed slightly better, with both RMSD and MAE being 11.75-4.87, 11.44-4.83, and 10.73-4.56, respectively. Overall, higher accuracy can be seen more at the monthly scale than at the daily scale, a possible reason for which being that the errors at the daily scale are canceled out due to aggregation. The SE value for TRMM rainfall is 0.18 mm, which is closest to the ground observation, while those for GSMaP, CPC, and CMORPH rainfall, are 0.13, 0.14, 0.15, respectively. Figure 4 plots the correlation coefficients between the GPPs and the rain observations, considering the effects of latitude. It can be seen that, in the upstream part of the basin (higher than 20\({}^{\circ}\) N latitude), the CC of GSMaP is highest compared to the other GPPs, and the CPC product has better correlation than CMORPH and TRMM. In the low- and mid-latitude regions (lower than 20\({}^{\circ}\) N latitude), the CC of GSMaP shows strong variation, reaching 0.9 at several stations--and even lower than 0.1 at some stations. A narrower variation in the range of [0.2,0.6] is presented in the CC of TRMM 3B42 and CMORPH. At the basin scale, the CCs of all of the selected GPPs are around 0.4; the GSMaP has the best overall performance with a CC of 0.44, while the CC of TRMM, CMORPH, and CPC is 0.42, 0.40, and 0.40, respectively. This result indicates that \"calibration with rain gauge data\" has the potential to improve the daily precipitation distribution for hydrometeorological applications, and the performance of GPPs, especially GSMaP, is strongly related to latitude. The POD and CSI indicators were employed to analyze the ability to detect precipitation of all of the selected GPPs, and 0.1 mm/day was defined as the threshold of rain/no-rain detection [39]. Generally, the POD of all of the GPPs shows a good value, larger than 0.81 (Table 4); the POD of GSMaP and CPC is observed to be larger than 0.91, with an insignificant FAR of 0.44 for GSMaP and 0.41 for CPC, which indicates that the GSMaP and CPC data generally have more rain days compared to the observations. The CSI indicators of TRMM and CMORPH (0.5) are higher than those of GSMaP and CPC (0.46-0.48), which indicates that TRMM and CMORPH better detect precipitation than GSMaP and CPC. Figure 5 shows the frequency of rainfall events (bar chart) and their relative contributions to the total accumulative rainfall from the 1998-2006 period (line chart) by different rainfall classification of the four GPPs and rain gauge observations at the daily scale. Overall, the GPPs underestimate rainfall frequency, as well as the contributions of no rain (rain \(\leq\) 0.1 mm) and moderate/heavy rain events (rain \(>\) 20 mm) compared to the observations. Conversely, with the little and light rain intensities (0.1 \(<\) rain \(\leq\) 20 mm), both the occurrence frequencies and their contributions of the gridded data are overestimated in comparison to the rain gauges. The contribution of light rain shows the highest contribution in the GPPs, while the highest contribution of observation is from the moderate (20,50) mm rainfall class. GSMaP shows the largest discrepancy with the observations, both in frequency and contribution, in all rain intensity classes. The frequency is 27% higher than that of the observations in the little rain class (0.1,1) mm--31.3% for GSMaP and 4.3% for the observations.