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• Recovery of hydrological droughts can be 4 times longer than that of meteorological ones. • Recovery time is longest and more severe in dry regions and regions with high water use. • A multi-indicator approach is key to the proper characterization of drought development and propagation. Meteorological droughts propagate through the hydrological cycle causing hydrological droughts and societal impacts. However, the effects of climate and basin characteristics on hydrologic drought propagation vary regionally and remain largely unclear. In this paper, we characterize meteorological and hydrological droughts in 457 basins in Brazil. Using the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Index (SSI), we investigate hydrologic drought propagation based on differences in drought onset, the center of mass, and time to peak. Additionally, we estimate the recovery time of meteorological and hydrologic drought events. The results indicate that hydrological droughts are usually more long-lasting, severe, and with a slower recovery time compared to meteorological droughts. While the most severe meteorological droughts are observed in humid regions (e.g., Amazon and Southern Brazil), the most severe and long-lasting hydrological droughts are found in the driest region (i.e., Northeast Brazil) or mostly impacted by human activities (i.e., Southeast Brazil). Hydrological droughts in dry regions can take four times longer to recover than meteorological ones. For most regions, the propagation time was slightly different considering the different approaches. Our results highlight the importance of a multi-indicator approach to fully characterize the mechanisms controlling the development and propagation of droughts through the water cycle. [ABSTRACT FROM AUTHOR]

• Out-of-sample prediction of extreme precipitation using nonstationary models. • Significant trends in data of extreme precipitation are insufficient to warrant the selection of a nonstationary model. • Large prediction uncertainty reduces the support for nonstationary models of extreme precipitation. • (Non)stationary model selection should explicitly account for model complexity. Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes. [ABSTRACT FROM AUTHOR]