Uncertainty in drought monitoring by the Standardized Precipitation Index: the case study of the Abruzzo region (central Italy)

Uncertainty Analysis of Standardized Precipitation Index Due to the Effects of Probability Distributions and Parameter Errors

“…No clear differences are observed from Figure 2, and all the alternative distributions seem to give satisfactory fits to the series. For evaluating the performance of different probability distributions in more details, goodness-of-fit tests including the Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) tests are carried out (Svensson et al, 2017;Vergni et al, 2017). Also taking the cumulative precipitation data in January as an example, the calculated statistic values for both tests are given in Table 3.…”

“…Among the drought indices, standardized precipitation index (SPI) is widely used (Moreira, 2015;Zhang et al, 2017;Merabti et al, 2018;Oliveira-Júnior et al, 2018;Tirivarombo et al, 2018) because it can determine drought at different time scales and only requires precipitation data (Ma et al, 2013). However, some uncertainties exist in its calculation due to the probability distribution functions in fitting the precipitation data, parameter estimation methods and errors, time scales and data length, and so on (e.g., Wu et al, 2005;Stagge et al, 2015;Vergni et al, 2017;Beyaztas et al, 2018). McKee et al (1993), the proposers of SPI, suggested using gamma distribution to fit the cumulative precipitation in calculating this index, whereas many scholars such as like Cindrić et al (2012), Hong et al (2013), Gabriel and Monica (2015), Wu et al (2016), and Vergni et al (2017) indicated that the applicability of theoretical distributions in describing the cumulative precipitation was inconsistent across different regions.…”

“…However, some uncertainties exist in its calculation due to the probability distribution functions in fitting the precipitation data, parameter estimation methods and errors, time scales and data length, and so on (e.g., Wu et al, 2005;Stagge et al, 2015;Vergni et al, 2017;Beyaztas et al, 2018). McKee et al (1993), the proposers of SPI, suggested using gamma distribution to fit the cumulative precipitation in calculating this index, whereas many scholars such as like Cindrić et al (2012), Hong et al (2013), Gabriel and Monica (2015), Wu et al (2016), and Vergni et al (2017) indicated that the applicability of theoretical distributions in describing the cumulative precipitation was inconsistent across different regions. Specifically, Guttman (1999) verified that the Pearson type III distribution is a better universal model in America; Sienz et al (2012) concluded that the Weibull-type distributions give distinctly improved fits compared to gamma in Europe; Angelidis et al (2012) found that the log-normal distribution gives almost the same results as gamma in the calculation of SPI at 12-and 24month scales in Guadiana (Portugal); Gabriel and Monica (2015) demonstrated that the generalized normal distribution presents the best performance in fitting precipitation series in Brazil.…”

“…As to the input data, a large number of studies have concerned about the uncertainties associated with input data sources, data length, and sampling uncertainty in the calculation of drought index (e.g., Hao et al, 2014Hao et al, , 2016Liu et al, 2014;Hu et al, 2015;Katiraie-Boroujerdy et al, 2016;Vergni et al, 2017;Zambrano et al, 2017;Beyaztas et al, 2018;Carbone et al, 2018). Vu et al (2018) compared the performance of rain-gauge data and gridded precipitation data in the calculation of SPI over Vietnam.…”

“…Carbone et al (2018) demonstrated that the SPI estimates derived from 30-year record have considerably more uncertainty than those from the 60-year record. Liu et al (2014), Hu et al (2015), Vergni et al (2017), and Beyaztas et al (2018) quantified the sampling uncertainties and their effects on the estimation of drought index.…”