## 3 Materials  
### TRMM Multi-Satellite Precipitation Analysis (TMPA)  
Precipitation-based remotely sensed data can provide a broad solution to the extensive problems that arise due to the low number and sparse distribution of rain gauges in certain areas. Moreover, it provides both a high spatial (4 to 25 km) and temporal resolution (every 30 min to 6 h). Furthermore, it offers annual, seasonal, and daily coverage at local and regional scales [53]. The Tropical Rainfall Measuring Mission (TRMM) provided the first widely used remote sensing data for estimating rainfall in tropical and subtropical areas [17; 54]. TRMM was a joint space mission between the US National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) [55; 56]. The TRMM carried onboard five instruments: a Precipitation Radar (PR, operating at 13.8 GHz), a TRMM Microwave Imager (TMI, a nine-channel passive microwave radiometer), a Visible Infrared Scanner (VIRS, a five-channel visible/infrared radiometer), a Clouds & Earths Radiant Energy System (CERES), and a Lightning Imaging Sensor (LSI). It operated at one transmitting/receiving frequency and one polarization, providing information about rain type, strength, and distribution [55]. The TRMM Microwave Imager (TMI) provided quantitative information about rainfall, water vapor, cloud water content, and sea surface temperature (SST) [55]. The PR complemented the results of the TMI and passive microwave sensors to provide measurements of radiance through precipitating clouds along the sensor view path. Radiance frequency reflects the properties of clouds and precipitation particles [57]. The active microwave sensors provided information about cloud height by measuring a  
Figure 1: Satellite map showing the study area. El-Qaa Plain is contained within the black outline with its five ground-based stations identified (source: Google Earth, 2017). backscatter delay [57]. The Visible and Infrared Scanner (VIRS) provided indirect measurements of rainfall intensity, distribution, and type [55; 56]. The VIRS provided less reliable data on its own [57]. However, it provided more frequent data when compared to the infrequent data captured by the TMI and PR. The LIS was a lightning sensor, which played an important role in connecting lightning occurrence to precipitation events, while the CERES allowed for the determination of the total radiant energy balance. Analyzed together with the latent heating derived from precipitation, it was then possible to construct a significantly improved picture of our atmospheric energy system [57]. For each of the rainfall events studied here, the temporal resolution is eight TMPA scenes in a day (i.e., one per 3 h) retrieved from the official NASA webpage (mirador.gsfc.nasa.gov (accessed on 4 February 2021)) in the netcdf format. The ArcGIS 10.5 software was subsequently used to process these data. Processing was performed in four steps, complementing the first stage of the statistical metrics. The data were first opened as a raster layer and clipped to match the study area. The data pixel size was subsequently resampled, adopting the nearest-neighbor interpolation, in order to match the IMERG data spatial resolution. Finally, the value of each pixel was calculated and recorded, including the starting point of the events (0 h), after three hours (3 h), six hours (6 h), nine hours (9 h), 12 hours (12 h), and one day (24 h). This time series was sufficient to cover all rainfall events, as the precipitation ceases after 12 hours. Next, the satellite-based rainfall data was divided into lowland and highland groups, according to the respective pixel's elevation. Data recorded at points with elevations ranging from 0 to 300 m belong to the lowland group, while those from 300 to 2000 m belong to the highland group. The values of pixels coinciding with rain gauges were collected at both 0.25\({}^{\circ}\) and 0.1\({}^{\circ}\) resolutions on a daily basis.