Hydrological Forecast Using Drought Indices in the South of Brazil

The study of the climate change impacts is very important for many economic sectors, being a relevant and recurrent theme in several conferences held all over the world. These changes affect both governments and population, as well as livestock, agriculture, fluvial navigation, and water resources in general. For a better comprehension and planning of building’s maintenance, hydrological forecasting, hazard prediction and hydropower generation, rainfalls and droughts can provide useful information in terms of some effects related to the climate change impacts, in rural areas or in urban centers. In terms of hydropower generation, Brazil is a potentially affected country due to its rainfall dependency on this type of generation. According to the Brazilian National Agency of Electrical Energy (ANEEL, in Portuguese), 64% of the Brazilian energy is generated by hydropower plants. In order to identify and analyze the climate change impacts in the rainfall series, we apply drought indices, once they provide useful information such as magnitude, intensity and severity. According to the literature review, both Reconnaissance Drought Index (RDI) and Standardized Precipitation Evapotranspiration Index (SPEI) are useful tools to identify these characteristics and the most suitable to perform climate change impacts all over the world. We apply both methods to analyze historical rainfall series and perform studies in predicted future scenarios. In addition, we apply simulations of the Regional Climate Model (RCM) ETA, which generates scenarios in order to perform a hydrological forecast for the future (until 2099). We apply the ETA simulations in 35 hydro-meteorological stations located across the Parana state, South of Brazil. We fit two parameter Log-Normal (LN2) and Gamma statistical distributions clustered in periods of 3, 6, 9, 12, 18, 24 and 48 months. This work aims at presenting the best fitting between LN2 and Gamma distributions, as well as possible trends and shifts along the future series and the best period of analysis associated to future forecast of the rainfall/droughts patterns alterations due to climate change impacts. Results present a good fit of both statistical distributions, as well as the significance of a trend in terms of drought severity for the study of climate change impacts.