2 (72 mm) in spatial average each year, with the largest differences in the early years of the twentieth century. The average spatial Ruxolitinib time series of the CRU TS 3.2 underestimates the mean precipitation values over the entire period, while GPCC v6 data fit best the extreme fluctuations. Comparisons of time series of gridded data (CRU TS 3.2 and GPCC v6) with observed data in grid points near the precipitation weather stations were also performed (not shown). These comparisons indicated that

the GPCC v6 data were better correlated with observations and presented smaller mean errors in different sectors of the study area. In addition the GPCC v6 dataset satisfy the reliability criteria of climate data to investigate dry/wet periods: (i) ease to access, (ii) uniform coverage of the area of interest, (iii) temporal duration long enough to be statistically trustworthy, and (iv) it has the ability to capture dry and wet events (Bordi et al., 2006). Based on these considerations and the results of validations we present only the results obtained with the GPCC v6 database. The SPI is constructed with the precipitation field and its computation for any location is based on the long-term precipitation record accumulated 17-AAG supplier over the selected time scale. The long-term record is fitted to a probability

distribution (usually a Gamma distribution), which is then transformed through an equal-probability transformation into a normal distribution (Raziei et al., 2010). A particular precipitation total

for a specified time period is then identified with a specific SPI value consistent with its probability. Positive SPI values indicate greater than median precipitation, while negative values indicate Cobimetinib ic50 less than median precipitation. The magnitude of departure from zero indicates the probability of occurrence and therefore, plans and decisions can be made based on this SPI value (Hayes et al., 1999). A detailed description of SPI calculation can be found in Edwards and McKee (1997), Lloyd-Huges and Saunders (2002) or Bordi and Sutera (2012), among others. The intensity of wet and dry EPE can be defined according to the classification system proposed by Agnew (2000) (Table 1), using probabilities of occurrence to define classes. Thus, at a given location, a very wet (dry) month will have a probability of occurrence of 10% and an extremely wet (dry) month 5%. Hence very wet (dry) conditions are only expected 1 year in 10 and extremely wet (dry) conditions in 1 year out of 20. Monthly precipitation series from GPCC v6 were transformed for each grid point into SPIn (t) series for n = 6, 12, and 18 months. In this paper, meteorological dry/wet condition have been assessed through SPI6 (t) as an indicator of short-term EPE for agricultural application, while SPI12 (t) and SPI18 (t) series, are used to investigate hydrological conditions.