3.2.2. Extreme Precipitation Events

The spatial patterns of extreme precipitation events identified by the four SBP datasets are presented in Figure 7.

**Figure 7.** Spatial distribution of the daily precipitation 95th percentile from 2014 to 2015 in the four SBP datasets. Figure (**a**,**c**,**e**,**g**) shows the accurately detected extreme precipitation events in (%), (**b**,**d**,**f**,**h**) bias in extreme precipitation events for each station. The black and blue lines denotes the national boundary of the country and 3000 masl elevation contour, respectively.

Extreme precipitation events are defined as those exceeding the 95th percentile (high-intensity extreme) values in gauge-observed datasets from 2015 to 2016 for each station. Extreme events were calculated only for those stations with daily observation data available for more than 90% of the year. Figure 7 shows the extreme events detected by SBP datasets on the same day as those in the gaugeobserved data sets (temporal accuracy) and the mean bias in the total number of extreme events at each station across the country, respectively. GSMaP-Gauge has been moderately improved in contrast with GSMaP-MVK, especially in central Nepal, where more precipitation was observed than in other areas (Figure 7e,g). The spatial distribution of the extreme events suggests that all four SBP datasets have low accuracy and mostly underestimate the frequency of extreme events over the study area (Figure 7b,d,f,h).

Figure 8 shows the performances of the four SBP datasets in detecting extreme events within the three elevation intervals. The statistics indicate that all four SBP datasets underestimated the frequency of extreme events in regions below 2500 m, while the IMERG-C and GSMaP-Gauge showed many more fake extreme events than the satellite-only products and thus overestimated the frequency in regions above 2500 m. As shown in Figure 8a, the higher number of extreme events were observed in regions below 2500 m (low and mid-elevation) than that for high elevation regions, and most DHM gauge stations (96.5% of total) are also located in these regions. Therefore, the GPCC analysis dataset interpolated from data of 125 DHM gauge stations and the NOAA/CPC dataset interpolated from data of 54 DHM gauge stations, which were used to calibrate the IMERG and GSMaP products respectively, may present fake high occurrence of extreme events in regions above 2500 m. That is why the calibrated SBP products overestimated the frequency of extreme events in regions above 2500 m. According to Figure 8, IMERG-UC performed much better in presenting the extreme event occurrence than other products especially in regions below 1500 m, suggesting that sometimes the calibration may skew some important signals contained in the satellite-only product. In general, all four SBP products had low accuracy (Figure 8b) and underestimated the frequency of extreme events (Figure 8a) across the country.

**Figure 8.** Bar charts showing extreme events (mean number of days) and RMSE (mean number of days) in the gauge observed data and four SBP datasets from 2015 to 2016 for (**a**) low-elevation (below 1500 m), (**b**) mid-elevation (between 1500 and 2500 m), (**c**) high-elevation (above 2500 m), and (**d**) the whole region.
