Spatio-temporal analysis of remotely sensed rainfall datasets retrieved for the transboundary basin of the Madeira River in Amazonia

Rainfall is recognized as the most important driving force of the hydrologic cycle. To accurately represent the spatio-temporal rainfall variability continues to be an enormous hydrological task when using commonly sparse, if available, rain gauges networks. Therefore, the present study devoted a special effort to analyze the robustness of some satellite rainfall products, notably the datasets hereafter named as (i) CHIRP (Climate Hazards Group InfraRed Precipitation), (ii) CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), (iii) 3B42, and (iv) 3B42RT of the Tropical Rainfall Measuring Mission (TRMM), to adequately represent the pluviometric regime in the Madeira river basin. To assess the accuracy of acquired remotely sensed rainfall products, comparisons to observational available rain gauges usually taken as ground-truth in the literature, despite their well-known limitations, were performed. Wavelet analysis was also used to validate the performance of the referred satellite products by means of extracting the corresponding cycles, frequencies, and tendencies along the available time series across the studied basin. The results showed that the data sources CHIRPS and CHIRP better represent the pluviometric phenomenon by means of their monthly accumulated rainfall in the Madeira river basin when compared to the 3B42 and 3B42RT products taking into account rain gauges as baseline information. The CHIRPS product performed the best among the selected rainfall estimators for the Madeira river basin. Further analysis brought up also another very interesting result related to non-rainfall periods, which is usually not reported. However, such evaluation is quite important in hydrology when examining run sequences of droughts and consequent effects in the water balance at the watershed scale. Highly accurate estimates in the sense of identifying non-rainfall periods by remotely sensed information was achieved, which represents an additional and valuable asset of satellite rainfall products. It is worthwhile to say that this perspective deserves to receive much more attention in the literature in order to deeply discuss the water-energy-food nexus.