## Materials and Methods
### Employed Datasets  
Table 1 reports the rain and streamflow measurement stations leveraged in the present research. Such stations are under the control of ANA, and their location is noted in Figure 2. The employed historical data series collected from the reported stations can be accessed on the online platform \"Hidroweb\", which is provided by the Brazilian National Water Resources Information System (Sistema Nacional de Informacoes sobre Recursos Hidricos--SNIRH, [[https://www.snirh.gov.br/hidroweb/serieshistoricas](https://www.snirh.gov.br/hidroweb/serieshistoricas)]([https://www.snirh.gov.br/hidroweb/serieshistoricas](https://www.snirh.gov.br/hidroweb/serieshistoricas)) accessed on 1 September 2024). The selected AutoML models were trained and tested relying on daily surface rainfall and streamflow data within the time-span from January 1985 to November 2023. Any time interval lacking collected data was omitted from the study. The streamflow data \(\overline{Q}_{d}\) collected on the day \(d\) from the streamflow gauge station noted with code 017210000 in Table 1, are shown in Figure 3. The latter streamflow time-series was composed of 14,212 samples. The well-known Thiessen polygon method [54] was employed to compute the average daily rainfall within the basin:  
\[\overline{P}_{d}=\frac{\sum_{i=1}^{n}P_{i}A_{i}}{A_{t}}, \tag{1}\]  
Figure 2: Characterization map of the upper Teles Pires River basin reporting a digital elevation model. The reported altitudes range from a minimum altitude of 272 m to a maximum of 895 m and are color-coded according to the reported legend on the left side. The entire drainage network, fluviometric, pluviometric, and meteorological stations are reported on the map. Figure adapted from [PERSON] et al. [30] under the terms of the Creative Commons Attribution License—CC BY 4.0 ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) accessed on 1 September 2024). in which \(A_{i}\) represents the area of influence (in squared kilometers) for the \(i\)-th rain gauge station, which recorded rainfall of \(P_{i}\) (in millimeters) on the day \(d\), the number \(n\), set to 4, represents the number of the employed stations, and \(A_{t}\) denotes the total area (in squared kilometers) of the considered basin. The rainfall data collected from the four rain gauge stations, reported in Table 1, and the computed average daily rainfall \(\overline{P}_{d}\) in the basin were shown in Figure 4. The four time-series collected from the four rain gauge stations on which the average daily rainfall \(\overline{P}_{d}\) was computed were composed of 14,212 samples. Alongside surface rainfall data, the present study also relied on rainfall data derived from remote sensing. In particular, the latter data included precipitation estimates from remotely sensed products using ANNs, i.e., PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now products (refer to Table 2) [41, 42]. The latter products were provided by the Center for Hydrometeorology and Remote Sensing (CHRS), located at the University of California (UC), Irvine, CA, USA. The average daily precipitation for the latter products was computed using a standard arithmetic approach: for each day, all the available precipitation measurements were averaged to produce a single daily average precipitation value. The daily average precipitation on the day \(d\), namely \(\overline{P}_{d}\), was calculated for each remote-sensing product using the following formula:  
\[\overline{P}_{d}=\frac{1}{n_{d}}\sum_{i=1}^{n_{d}}P_{i} \tag{2}\]