## Materials and Methods
### Employed Datasets
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Name** & **Code** & **Station Type** & **Latitude (*)** & **Longitude (*)** \\ \hline Teles Pires & 017210000 & F & \(-12.67^{\circ}\) & \(-55.79^{\circ}\) \\ Passagem da BR-309 & 01453000 & P & \(-14.61^{\circ}\) & \(-53.99^{\circ}\) \\ Paranattinga & 01454000 & P & \(-14.41^{\circ}\) & \(-54.04^{\circ}\) \\ Porto Rconcador & 01355001 & P & \(-13.55^{\circ}\) & \(-55.33^{\circ}\) \\ Teles Pires & 01255001 & P & \(-12.67^{\circ}\) & \(-55.79^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The table reports both the rain and streamflow gauge stations employed in the present research. The “Code” column refers to the station identifiers used in the Brazilian national hydrometeorological network. Within the column “Station Type”, the letter “F” denotes a streamflow (Fluviometric) gauge station, while the letter “P” denotes a rain (Fluviometric) gauge station. The “Latitude” and “Longitude” columns report the coordinates for each of the considered stations. Figure 3: The Figure reports the average streamflow \(\overline{Q}_{d}\) (in m\({}^{3}\) s\({}^{-1}\)) recorded at the Teles Pires Fluviometric Station identified with code 017210000 (Latitude: \(-12.67^{\circ}\) and Longitude: \(-55.79^{\circ}\)) in Table 1 over the period from January 1985 to November 2023. The data shows significant seasonal fluctuations over months. Indeed, as reported in Section 3.1, peak rainfall usually occurs from October to April (rainy season), resulting in higher streamflow during such months, while the lowest precipitation period is from May to September (dry season), thus leading to lower induced streamflow (refer to Section 1 to deepen the relationship between rainfall and river flows). where \(n_{d}\) represents the number of precipitation measurements recorded in the day \(d\), and \(P_{i}\) denotes the individual precipitation measurement. The computed average daily rainfall \(\overline{P}_{d}\) derived from remote-sensing products was shown in Figure 5. The original datasets containing raw estimates are available on [[https://chrsdata.eng.uci.edu](https://chrsdata.eng.uci.edu)]([https://chrsdata.eng.uci.edu](https://chrsdata.eng.uci.edu)) accessed on 1 September 2024. Figure 4: The top four subplots in the Figure report the rainfall data collected from the four rain gauge stations listed in Table 1. Each subplot shows the total rainfall over time for the respective station on the \(y\)-axis. The final subplot displays the computed average daily rainfall, \(\overline{P}_{d}\), in red, calculated using the Thiessen polygon method. The data spans from January 1985 to November 2023, with the \(x\)-axis representing the measurement years for all the subplots. Additional details from Table 1, such as station names, types, and geographical coordinates, were also reported. Figure 5: The four subplots in the Figure report the average rainfall data \(\overline{P}_{d}\) computed from the four remote-sensing products, listed in Table 2. Each subplot shows the average rainfall over time for the respective remote-sensing product on the \(y\)-axis. The time-spans for the computed averages are reported in the title of each subplot, with the \(x\)-axis representing the measurement years. As a final remark, in the present study, outlier detection was not performed, as the raw data were already validated by the dataset providers, i.e., SNIRH [19, 20] and CHRS [41, 42], to ensure the accuracy and reliability of the provided datasets.