Abstract
The initial step in calculating the Standardized Precipitation Index (SPI) is to determine a probability density function (pdf) that describes the precipitation series under analysis. Once this pdf is determined, the cumulative probability of an observed precipitation amount is computed. The inverse normal function is then applied to the cumulative probability. The result is the SPI. This article assessed the changes in SPI final values, when computed based on Gamma 2-parameters (Gam) and Pearson Type III (PE3) distributions (SPIGam and SPIPE3, respectively). Monthly rainfall series, available from five weather stations of the State of São Paulo, were chosen for this study. Considering quantitative and qualitative assessments of goodness-of-fit (evaluated at 1-, 3-, and 6-months precipitation totals), the PE3 distribution seems to be a better choice than the Gam distribution, in describing the long-term rainfall series of the State of São Paulo. In addition, it was observed that the number of SPI time series that could be seen as normally distributed was higher when this drought index was computed from the PE3 distribution. Thus, the use of the Pearson type III distribution within the calculation algorithm of the SPI is recommended in the State of São Paulo.
Highlights
The Standardized Precipitation Index (SPI) was developed by McKee et al (1993, 1995) as a drought indicator which standards the rainfall deficits/excess on temporal and regional basis
As pointed out by Guttman (1999), standardization of this drought index calculation algorithm is necessary in order to provide for all users, a common basis for both spatial and temporal comparison of the SPI values
According to Equations 1 and/or 4, while the Gamma 2-parameters (Gam) distribution has a fixed (x = 0) bound, the PE3 distribution bound depends on the values of its own parameters (x
Summary
The Standardized Precipitation Index (SPI) was developed by McKee et al (1993, 1995) as a drought indicator which standards the rainfall deficits/excess on temporal and regional basis. As described by Keyantash and Dracup (2002) the SPI represents observed rainfall as a standardized departure with respect to a rainfall probability distribution function. Since McKee et al (1993), the SPI model has been used by several authors on climate variability evaluations. Hayes et al (1999) applied the SPI algorithm in describing drought conditions in the State of Texas, USA.
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