Abstract
Extreme weather events have frequently caused serious damage to the quality of life of the population and the economy of the Brazilian semiarid region, where droughts are the main natural disaster. Therefore, a robust and skilled data network is needed for precise monitoring of droughts, and satellite precipitation products have stood out as useful alternative methods for providing estimated precipitation data. Thus, the objective of this study was to evaluate the efficiency of satellite-estimated long-term precipitation for monitoring meteorological drought in a semiarid region. This study was carried out in the Piranhas River basin, northeastern Brazil, using the following data (1994–2017): (a) observations at 38 rain gauges, (b) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network – Climate Data Record (PERSIANN-CDR), and (c) Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. To assess the short-, medium- and long-term meteorological droughts, standardized precipitation indices (SPI-6, SPI-12 and SPI-24) were used in semiannual, annual and biannual time scale analyses. The results showed that the CHIRPS and PERSIANN-CDR data presented acceptable performance in the identification of meteorological droughts in the study area. The results also showed that the time scales of the SPI-6, SPI-12 and SPI-24 datasets were adequate for identifying the main drought events that have affected the Piranhas River basin in recent years. The PERSIANN-CDR data performed better than the CHIRPS data, although the two datasets described the occurrence of droughts in the basin well. In summary, the study showed that CHIRPS and PERSIANN-CDR are valuable complements to rain gauge-measured rainfall data and that these datasets could be additional sources for hydrometeorological applications in the Piranhas River basin.
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