3.1.2. Seasonal Pattern of Precipitation in Nepal

The annual cycles of precipitation in the gauge observed data, and all four SBP estimates in the western, central, and eastern regions of Nepal from 2015 to 2016 (only a two-year was chosen due to incomplete annual SBP datasets in 2014) are shown in Figure 4. High precipitation occurs from June–September in all three regions (Figure 4).

**Figure 4.** Monthly variation in precipitation (mm/day) over the (**a**) western (80–82ºE), (**b**) central (83–85ºE), and (**c**) eastern (86–88ºE) regions, derived from the gauge observed data and four SBP products averaged over 2015 to 2016.

The mean precipitation in the winter (December to February) was heavier over the western (0.74 mm/day) region than in the central (0.54 mm/day), and eastern regions (0.28 mm/day). In contrast, western Nepal was drier (4.90 mm/day) than the central (7.02 mm/day) and eastern regions (6.85 mm/day) during the other seasons (March to November) due to the influence of

summer monsoon. The precipitation in winter is primarily influenced by the westerlies system and is more pronounced in the western part of the country, while, moisture transfer from Bay of Bengal (monsoon) produces the widespread precipitation during the monsoon season (JJAS) over the country. All four SBP datasets show higher precipitation during the summer monsoon and lower precipitation in winter, with the maximum in July except for GSMaP-MVK in the eastern region (Figure 4c). The satellite-only datasets overestimated the precipitation during winter and pre-monsoon season; however, after the gauge calibration, the positive bias was reduced and is more consistent with observed datasets. Figure 4 indicates that among all four SBP datasets, the gauge calibrated datasets (i.e., IMERG-C and GSMaP-Gauge) represent well the seasonal precipitation variation across all three regions of Nepal, although they all yield underestimations. However, all four SBP datasets well captured the seasonal precipitation dynamics across the country.

For a detailed analysis, the statistical metrics of the four SBP datasets from 2015 to 2016 were calculated against the station observations (Table 3). In the western region, IMERG-UC and GSMaP-MVK showed smaller MBs than their gauge-calibrated datasets, i.e., IMERG-C and GSMaP-Gauge, respectively. Nevertheless, both gauge-calibrated datasets showed better overall performance as indicated by lower RRMSE and higher R-value (Table 3). For the central region, IMERG-UC showed the smaller MBs of −0.93 mm/day than that of −1.48 mm/day in IMERG-C; however, both have proximal RRMSE. Meanwhile, among all SBP, GSMaP-Gauge outperformed GSMaP-MVK and both IMERG datasets as indicated by the lowest MBs and RRMSE (Table 3). Both gauge-calibrated datasets showed very similar MBs with their corresponding satellite-only in the eastern region, although gauge calibrated IMERG-C performed more consistently, with a smaller RRMSE of 0.18, followed by GSMaP-Gauge. In the whole country, among all products, IMERG-UC showed the smallest MB of −0.47 mm/day and IMERG-C showed the lowest RRMSE of 0.28. It is worth noting that the positive bias in IMERG-UC and GSMaP-MVK between January and June later reduces the negative bias during July to October and shows smaller MBs among the datasets (Figures 4a–c and A1). The seasonal performances of all four SBP datasets were calculated to check the consistency in different seasons and presented in Table A1. The seasonal performance also showed that gauge calibrated datasets well represent the seasonal dynamics than satellite-only as indicated by lower MBs, RRMSE and higher R-value in Table A1. In general, all four datasets generally exhibited high correlations (R > 0.80), which indicate that the seasonal precipitation dynamics can be captured across the country by all four datasets.


**Table 3.** Statistical metrics in the western, central, and eastern regions, as well as in the whole study region, derived from the regional monthly mean precipitation (mm/day) from 2015 to 2016. Bold font indicates the best performance for a given metric.

### 3.1.3. Elevation Dependency

The knowledge of the elevation gradient of precipitation is vital for many hydro-meteorological applications. As known that a larger portion of the precipitation occurs during the summer monsoon season, thus the elevation dependency was investigated based on monthly data from the summer monsoon season. The mean precipitation data from observed and four SBP were averaged over summer monsoon at different elevation ranges in every 500 m from 60 m to below 3000 m during the study period (Figure 5). The number of stations above 3000 m is very limited; thus, the elevation dependency was only calculated below 3000 m. The gauge observations reveal an evident elevation dependency of precipitation, as shown in Figure 5 for the monsoon period. Gauge observations show that precipitation gradually increases with increasing elevation up to 2000 m, and then decreases rapidly (black line, Figure 5a). The highest precipitation (approximately 13 mm/day) occurs in the range 1500–2000 m during the summer monsoon. These patterns are similar to the results revealed by the previous study conducted using gauge observations [72]. IMERG-C moderately captured this evident elevation-dependent pattern, with the highest precipitation (approximately 10 mm/day) in the elevation range of 1500–2000 m. In contrast, other three SBP products failed to capture this pattern; IMERG-UC and GSMaP-MVK showed the highest precipitation at the lowest elevation (below 500 m), and GSMaP-Gauge shows the highest precipitation in the elevation range 500–1000 m. In the higher elevation areas (above 3000 m) with limited stations (14), GSMaP-Gauge and both IMERG datasets overestimated the observed precipitation (not shown in Figure 5). This could be associated with the complex terrain and orographic effect [73,74]. It is worth to note that, orographic rain corrected GSMaP-Gauge showed the variation in precipitation amount for different elevation intervals (i.e., precipitation increase and decrease pattern) [75]. Additionally, we calculated the elevation dependency of SBP datasets by averaging the precipitation across all grid boxes within different elevation ranges (Figure 5b). The numbers of stations and grid boxes in different elevation ranges are listed in Table 4. Grid-based elevation dependency of SBP showed a similar pattern to that of the point-pixel results, but with slightly different precipitation amounts. Overall, both gauge satellite-only IMERG-UC and GSMaP-MVK significantly underestimated the monsoonal precipitation amount. However, after gauge correction, the precipitation estimates of gauge calibrated datasets were more consistent with the gauge observation than the satellite-only datasets. Therefore, the procedure of calibrating SBP products with rain gauge data is the reason for their increased accuracy.

Table 5 gives the statistical metrics of errors for the four SBP datasets across three different geographic regions, based on summer monsoon mean values at each station. In lowland areas (below 1500 m) the error metrics indicate that IMERG-UC showed smallest MBs of −0.85 mm/day; indicating the estimated precipitation amount was more consistent with the observed datasets; meanwhile, GSMaP-Gauge showed better overall performance than other three datasets with lower RRMSE (0.45) and a higher R-value (0.56). In the highest precipitating mid-elevation areas (between 1500 and 2500 m), both gauges calibrated datasets showed a more consistent performance to observed datasets than satellite-only datasets. IMERG-C showed the best performance to estimate the precipitation amount with lowest MBs of −3.07 mm/day, while GSMaP-Gauge presented the evident lowest RRMSE and higher R-value, indicating better performance in reproducing the spatial distribution of gauge observed precipitation among all. However, all four datasets underestimated the monsoon precipitation amount below 2500 m.

**Figure 5.** Mean precipitation averaged over the summer monsoon season (mm/day) for the study period, (**a**) in gauge observed and four SBP datasets at station locations, (**b**) in IMERG and GSMaP product at each grid box averaged over different elevation ranges, respectively.


**Table 4.** Numbers of stations and grid boxes in different elevation ranges.

**Table 5.** Statistical metrics in the station mean precipitation at different elevation intervals (below 1500 m, between 1500 and 2500 m and above 2500 m), as well as in the whole study region, derived from the four SBP datasets and compared to the gauge observations during the summer monsoon for the study period. Bold font indicates the dataset with the best performance for a given metric.


In high-elevation regions (above 2500 m), characterized by complex topography with low precipitation, IMERG-C showed the best performance with smaller errors (MB and RRMSE) and higher R than other datasets, demonstrating that the calibration based on GPCC data significantly improved the IMERG product. Meanwhile, the GSMaP-Gauge product overestimated summer monsoon precipitation and showed very poor correlation with the gauge observation. As mentioned in Section 2.2.1, the GPCC data merged observations from 125 gauges in Nepal, while NOAA/CPC data only merged observations from 54 gauges. Therefore, GPCC data may integrate more precipitation information, especially in high elevation regions where gauge observations are very scarce than NOAA/CPC data. This might be the reason for the improved performance of IMERG-C than GSMaP-Gauge in high-elevation regions.

Overall, the gauge-calibrated products performed better than the satellite-only products on a monthly scale. IMERG-C yielded smaller MBs than GSMaP-Gauge, while GSMaP-Gauge showed the smaller RRMSE and higher R-value; indicating IMERG-C was more consistent to estimate the precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presented more reasonable spatial distribution than IMERG-C.
