Abstract
The Standard Precipitation Index (SPI) is a widely used statistical technique for the characterization of droughts. It is based on a probabilistic standardization procedure, which converts a Gamma-type probability distribution function (PDF) into a normal (Gaussian) standard series with zero mean and unit standard deviation. Drought classification based on SPI indicates dry and wet spell characteristics, provided that the hydro-meteorological records abide by normal (Gaussian) PDF only, otherwise the results will be biased. Therefore, in this paper, the actual precipitation index (API) method is presented, which provides drought classification and information regardless of the underlying PDFs. The main purpose of this paper is to explain the main differences between SPI and API and to prove that the use of API is the more reliable solution for classification of droughts into five categories described as “Normal dry”, “Slightly dry”, “Medium dry”, “Very dry” and “Extremely dry”. The application of the methodology is presented for two sets of precipitation data; one with exponential PDF monthly precipitation records from Istanbul City, Turkey and one for New Jersey, USA with almost normal (Gaussian) PDF based on annual precipitation records. The comparisons indicate that API is applicable regardless of the underlying PDF of the hydro-meteorology data. It produces real drought classification from the original data without recourse to standard normal PDF conversion.
Highlights
Drought phenomena are forms of natural hazards that start to manifest when meteorological droughts set in, leading to hydrological and agricultural droughts
The major driving force causing prolonged drought periods is a reduction in the amount of precipitation, which, in recent times, appears to be due to the impacts of global warming and climate change, following increases in greenhouse gas (GHG) emissions (IPCC 2007, 2014)
The Standard Precipitation Index (SPI) transforms the original precipitation time series records into a standardized normal (Gaussian) probability distribution function (PDF) and provides classifications, whereas the actual precipitation index (API) works on actual data without any transformation of the underlying PDF and gives classifications on real data values
Summary
Drought phenomena are forms of natural hazards that start to manifest when meteorological droughts set in, leading to hydrological and agricultural droughts. In the SPI series, it is not possible to identify quantitative actual drought features at different levels, such as dry period duration, magnitude or intensity, because the features of the SPI data are not similar to those of the original data series For such quantification to work, it is preferable to base the dry and wet spell classifications on the statistical standardization procedure. The API probabilities are entered into this Gamma CDF to calculate the corresponding original data probability boundaries, B, for dry and wet spell classification as, records are considered using records from New Jersey, USA, covering more than 100 years of statewide annual precipitation amounts from 1895 to 2003.
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