Hydrometeorological drought analysis through Two-variate Standardized Index for the Paraná River Basin, Brazil

Highlights • The study estimates the TSI as tailored climate information to characterize and monitor hydrometeorological droughts. • The TSI demonstrated the potential as operational index for capturing drought onset and duration in diverse climate regimes. • TSI's conservative nature makes it a promising tool for early warning in water resource management. • Multivariate analysis is crucial for accurate drought assessment. Abstract Brazil has faced severe droughts in recent decades, significantly affecting agriculture, water supply, and hydropower generation, among other sectors. The Paraná River Basin, a major hub for hydropower, has been particularly affected, posing challenges for water management. To address this, we conducted a study to provide tailored climate information for the Brazilian hydroelectric sector. Given the complexity of hydrometeorological processes, involving hydrological, meteorological and climatological processes and anthropogenic factors, a multivariate approach is essential for drought assessment. We assessed the effectiveness of a Two-variate Standardized Index (TSI) in characterizing hydrometeorological droughts on large-scales basins and its potential as an operational decision support index. The TSI was compared with the Standardized Precipitation Index and the Standardized Streamflow Index, and we examined the correlation between TSI and Terrestrial Water Storage from GRACE and reservoir volumes, at different time scales. We used the HAND model and water table depth to gain a comprehensive understanding of drought conditions. The results show that the TSI outperforms the univariate indices SPI and SSFI in characterizing drought events in terms of severity and duration. The correlation analysis showed that time scales of 12 and 24 months are the most suitable for hydrometeorological drought monitoring. Overall, the study highlights the potential of TSI as an operational index for monitoring drought conditions and can be applied in basins with different climate regimes.