## 5 Results and Discussion
### Satellite-Based Versus In-Situ Data  
The Spearman correlation coefficient and the respective \(p\)-value were also calculated in an attempt to establish the relationship between the in-situ rain gauge records, on the one hand, and the 0.25\({}^{\circ}\) resolution TMPA data, on the other hand. In this respect, it was found that \(R_{s}\) = 0.328 and \(p_{s}\) = 0.157 (see Figure 4). A similar approach was followed in establishing the relationship between the in-situ rain gauge records and the 0.1\({}^{\circ}\) resolution TMPA, where \(R_{s}\) = 0.546 and \(p_{s}\) = 0.012. For the relationship between the in-situ rain gauge records and the 0.1\({}^{\circ}\) resolution IMERG, \(R_{s}\) = 0.745 and \(p_{s}\) = 0.00016. Bearing in mind these results, it can be inferred that IMERG exhibited the strongest evidence for correlation with the rain gauges, whereas the 0.25\({}^{\circ}\) resolution TRMM data showed evidence for correlation with the rain gauges was very weak. Moreover, the 0.25\({}^{\circ}\) and 0.1\({}^{\circ}\) spatial resolution TMPA records revealed an underestimation of precipitation during the moderate and heavy-intensity events, while the light event records were highly coherent with the rain gauge records. IMERG displayed this same coherence with the light events, but both underestimated and overestimated values were recorded during the heavy-intensity events. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias (BIAS) metrics were calculated for each event and are summarized in Table 3. The IMERG dataset displayed the lowest RMSE values for the 2015, 2016, and 2018 precipitation events (10.677, 10.562, and 1.883 mm, respectively). In addition, IMERG exhibited the lowest MAE values for the 2015, 2016, and 2018 events (6.726, 8.076, and 1.367 mm, respectively). The values from the TMPA 0.1\({}^{\circ}\) dataset were close to those of the TMPA 0.25\({}^{\circ}\) dataset, but with better performance. As expected, the lowest bias is related to the coarsest resolution data set, namely IMERG. Furthermore, in the BIAS test for the 2015 and 2016 events, IMERG exhibited values closest to 0. Figure 3: Boxplots illustrating the differences between the TMPA and the IMERG at 0.1\({}^{\circ}\) spatial resolution, for successive time thresholds (0 h, 3 h, 6 h, 9 h, 12 h, and 24 h) and for each event: (**a**) Lowland 2015, (**b**) Highland 2015, (**c**) Lowland 2016, (**d**) Highland 2016, (**e**) Lowland 2017, (**f**) Highland 2017, (**g**) Lowland 2018, and (**h**) Highland 2018. The third group of categorical statistics was applied to the three different precipitation thresholds: 0.1 mm, 1 mm, and 10 mm. The results illustrated the high capability of the TMPA and IMERG analyses in detecting light-intensity events, as the 0.1-mm threshold performed best with both types of remote sensing data, calculating a 1 in the Probability of Detection (POD) and Critical Success Index (CSI) tests (Figure 5a,d), and 0.4 and 0.2 in the False Alarm Test (FAR) test. The second threshold also results in a 1 in the POD test for both data sets, but the CSI calculates at 0.8 and 1, and the FAR test results in 0.4 and 0.5 (Figure 5b,e). The last threshold, 10 mm, produces the worst results. TMPA amounts to a 0 on all the previously mentioned tests. IMERG records a 1, 1, and 0.3 for the POD, FAR, and CSI, respectively (Figure 5c,f). In general, the IMERG data shows better results than that of the TMPA. Both datasets featured higher certainty for light-intensity events.