## 4 Discussion
### Spatial Variability  
In contrast, the spatial differences between precipitation estimates by remote sensing and reanalysis, and the measurements at the weather stations can be partly due to differences in the spatial scales represented by these techniques. A rain gauge consists of a point measurement, while the gridded products (i.e., satellites and land surface models or reanalysis) obtain an average value over the analyzed pixel, although there are different adjustments depending on the local characteristics of each region [33; 34]. As a result, satellites and reanalysis products have the ability to estimate precipitation events in areas that a rain gauge was unable to record [30]. It should be noted that remote sensing products can mistakenly estimate precipitation events due to the thickness and temperature of clouds [34].  
The GLDAS product uses data from several satellites and rain gauges in its model. The satellite information is based on infrared and microwave sensors, including the TRMM satellite, which justified its relatively improved results [18]. On the other hand, there are some uncertainties that can occur in the data used in developing the GLDAS product, due to the small number of rain gauges and the quality of their measurements in the region [29].  
The GPCP tended to overestimate precipitation because the region is strongly influenced by mesoscale convective systems and cold fronts, which provide strong convection and cloud formation (Cirrus), which are more complex to assimilate [35]. As with the GLDAS product, GPCP uncertainties may increase due to the fact that this product uses and combines indirect estimates of precipitation from satellite sensors (visible, infrared, and microwave) as well as rain gauges to calibrate its input data [36].  
The MERRA product had noticeable limitations as it underestimated precipitation in all locations, and it percutaneously estimated precipitation events that were not measured at the weather stations [37]. In addition, this reanalysis product has a low spatial resolution--a characteristic that makes it difficult to estimate and detect the occurrence of precipitation events in Mato Grosso where precipitation events have high spatiotemporal variability and are mainly influenced by convective events. Convective precipitation events are more complex to be assimilated by reanalysis models [32; 38; 39; 40].  
Moreover, MERRA uses average surface precipitation data (rain gauges) to establish its initial conditions from the National Oceanic and Atmospheric Administration (NOAA) Unified Gauge-Based Analysis of Global Daily Precipitation. Therefore, the estimated precipitation in the center of South America has low accuracy due to the low density of pluviometric stations--indicating an issue of discontinuity in precipitation measurements [20]. There was not enough information available about how many precipitation stations within Mato Grosso were directly used in developing MERRA, if any. The study by [20] indicated that MERRA estimates showed increased error in the Amazon due to sharp decline in the number of precipitation stations used in MERRA from 350 between 1980-2004 to only 65 since 2004. Precipitation estimates by MERRA are closely dependent on the GEOS-5 Catchment land surface model, which can also be highly sensitive to changes in other hydrological variables. In addition, the precipitation measurements used as input in the GEOS-5 model can generate uncertainty in the estimates because the model and the calibration period of the instruments are heterogeneous [40]. A study carried out in the central region of South America, evaluated the MERRA estimates based on an earlier version of this product and found an opposite relationship with the surface precipitation data. The study justified this error due to the low density of installed rain gauges in this region [41].