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• 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]

Summary: This paper combines hydrological observations and modelling results of a semi arid catchment in Brazil that could lead to a better understanding of the hydrology of similar catchments in semi-arid regions. The Tapacurá catchment (area 470.5km2) in the Northeast of Brazil was selected for this study. The Distributed Catchment Scale Model, DiCaSM, was calibrated and validated for the stream flows of the Tapacurá catchment. The model performance was further tested by comparing simulated and observed scaled soil moisture. The results showed the ability of the model to simulate the stream flow and the scaled soil moisture. The simulated impacts of climate change of low emission (B1) scenarios, on the worst perspective, indicated the possibility of reduction in surface water availability by −13.90%, −22.63% and −32.91% in groundwater recharge and by −4.98%, −14.28% and −20.58% in surface flows for the time spans 2010–2039, 2040–2069, 2070–2099, respectively. This would cause severe impacts on water supply in the region. Changing the land use, for example by reforestation of part of the catchment area which is currently arable land, would lead to a decrease in both groundwater recharge by −4.2% and stream flow by −2.7%. Changing land use from vegetables to sugar cane would result in decreasing groundwater recharge by almost −11%, and increasing stream flow by almost 5%. The combination of possible impacts of climate change and land use requires a proper plan for water resources management and mitigation strategies. [Copyright &y& Elsevier]

Summary: Efficient management of water and energy is an important goal of sustainable development for any nation. Streamflow forecasts, have been used in complex optimization models to maximize water use efficiency and electrical energy production. In this paper we develop a statistical model for the long term forecasts of hydroenergy inflow into the Brazilian hydropower system, which consists of more than 70 hydropower reservoirs. At present, the planning of reservoir operation and energy production in Brazil is made with no reliable long term (one season or longer lead times) streamflow forecasts. Here we use the NINO3 index and the main modes of the tropical Pacific thermocline structure as climate predictors in order to achieve skillfull forecasts at long leads. Cross-validated results show that about 50% of the total hydroenergy inflow can be predicted with moderate accuracy up to 20 month lead time. [Copyright &y& Elsevier]

• Evaporation trends of tropical reservoirs in Brazil were investigated. • Both positive and negative trends in evaporation were observed. • In the industrial region, the negative trend was potentially caused by the impact of pollutant released to the atmosphere. • In the coastal region, the positive trend was related to increasing air temperature. This paper aims to analyze the trends (Mann-Kendall test) of evaporation and climatological variables, including air temperature, relative humidity and wind speed in four reservoirs located in the Brazilian Northeast. The evaporation was estimated through remote sensing using the Surface Energy Balance System for Water (AquaSEBS) model from 1985 to 2018. In addition, it was verified which meteorological parameter has the highest correlation (R2) with the evaporation in order to assess why this process is changing throughout the years in each reservoir. The model performed well, with root mean square error (RMSE) ≤ 1.25 mm/day, percent bias (PBIAS) ≤ 13.70% and coefficient of determination (R2) ≥ 0.51. One reservoir located closer to a coastal zone presented a positive trend (+0.24 mm/34 years), corroborating the measurements of a reference class A pan, and Penman-Monteith equation, while the others, located closer to industrial areas, displayed negative trends (−0.26 to −0.080 mm/34 years). This reduced evaporation was attributed to the impact of industrial pollutant release to the atmosphere, while the increasing effect in the other reservoir was due to its proximity to the coast, which implies an air renewal as the sea breeze contributes for an improvement in the air quality. The meteorological data also presented positive and negative correlations: Temperature exhibited a positive trend (+0.57 °C/34 years), whereas humidity (−4.83%) and wind speed (−0.29 m/s), a negative trend. Evaporation displayed moderate coefficient of determination (R2 > 0.31) with wind speed and humidity. Yet, the reduction of evaporation may be associated with a possible counterbalancing effect of wind speed over humidity. Overall, the results contribute to a better understanding of evaporation trends in a water-scarce region and can be used in a water resources management context as the increasing/reduction of evaporation might significantly impact water availability in this area. [ABSTRACT FROM AUTHOR]