On the use of Standardized Precipitation Index (SPI) for drought intensity assessment

Abstract

Monthly rainfall data from June to October for 39 years were used to compute Standardized Precipitation Index (SPI) values based on two parameter gamma distribution for a low rainfall and a high rainfall districts of Andhra Pradesh state, India. Comparison of SPI with actual rainfall and rainfall deviation from the mean indicated that SPI values under-estimate the intensity of dryness/wetness when the rainfall is very low/very high, respectively. As a result, the SPI in the worst drought years of 2002 and 2006 in the low rainfall district indicated only moderate dryness instead of extreme dryness. SPI values of the high rainfall district showed slightly better stretching in both positive and negative directions, compared to that of the low rainfall district. Further, the SPI values of longer time scales (2, 3 and 4 months) showed an extended range compared to that of 1 month, but the sensitivity in drought years has not improved significantly.Normality tests were conducted based on Shapiro-Wilk statistic, p-values and absolute value of the median to ascertain whether non-normality of SPI is a possible reason. Although the results confirmed normal distribution, the scatter plot indicated deviation of the cumulative probability distribution of SPI from normal probability in the lower and upper ranges.Therefore, it is suggested that SPI as a stand alone indicator needs to be interpreted with caution to assess the intensity of drought. Further investigations should include sensitivity of SPI to the estimated shape and scale at lower and upper bounds of the gamma distribution and use of other distributions, such as Pearson III, to standardize the computational procedures, before using SPI as a substitute to the rainfall deviations from normal, for drought intensity assessment.

The Temporal-Spatial Characteristics of Drought in the Loess Plateau Using the Remote-Sensed TRMM Precipitation Data from 1998 to 2014

“…The SPI (Standardized Precipitation Index), which is based on precipitation only, was proposed by McKee et al [7]. The SPI is based on the conversion of precipitation data to probabilities using long-term monthly precipitation records computed at different time scales.…”

Spatial characteristics of precipitation shortfalls in the Greater Alpine Region—a data-based analysis from observations

“…Quantiles introduce a lower boundary (zero), which makes a severity assessment, as described below, much more straightforward. As highlighted by Naresh Kumar et al (2009), the SPI underestimated the severity of dry and wet extremes due to distribution fitting issues which underpins the advantage of using quantiles.…”

An Optimized System for the Classification of Meteorological Drought Intensity with Applications in Drought Frequency Analysis

“…These results are in agreement with the findings of earlier studies in different geographic locations. For example, Kumar et al (2009) used monthly precipitation data to evaluate the response of the drought intensity categories proposed by FIG. 6.…”

“…Thus, if one is interested in determining the cause of drought events, attention should be focused on meteorological (precipitation) drought (Dracup et al 1980). Indeed, while the occurrence and progression of drought conditions also depend on other variables such as temperature, wind speed, evapotranspiration, and soil moisture, the precipitation deficit is the main driver of any drought event (Heim 2002;Smakhtin and Schipper 2008;Kumar et al 2009;Vicente-Serrano et al 2010;Mishra and Singh 2011). The longer and the more spatially extensive the lack of precipitation, the more likely different types of droughts will occur.…”

“…The threshold levels of drought intensity proposed by McKee et al (1993) have been used worldwide in numerous applications at different time scales of precipitation accumulation, such as to monitor drought in the United States (Hayes et al 1999;Guttman 1999;Wu et al 2005) and Europe (Sepulcre-Canto et al 2012), to calculate a drought climatology for Europe (Lloyd-Hughes and Saunders 2002), for detecting droughts in East Africa (Ntale and Gan 2003), to monitor drought conditions and their uncertainty in Africa using data from the Tropical Rainfall Measuring Mission the transition probabilities of drought events in the Kansabati River basin in India (Mishra et al 2009) and in the Alentejo region in Portugal (Paulo et al 2005), for improving the fire danger forecast in the Iberian Peninsula (Hofer et al 2012), and to assess drought intensity in low-and high-precipitation districts of Andhra Pradesh in India (Kumar et al 2009), to cite but a few. Although the SPI classification system proposed by McKee et al (1993) provides a consistent and replicable standard for drought intensity classification using threshold levels easily understood by nonexperts, its values have been questioned, for example, by Agnew (2000), Paulo et al (2005), Kumar et al (2009), andQuiring (2009). Indeed, Agnew (2000) proposed alternative SPI threshold levels based on fixed precipitation percentiles used by hydrologists in low flow analysis and showed that the effects of changing the threshold values is observed at the demarcation between the ''mild'' and ''moderate'' drought categories defined by McKee et al (1993) (Fig.…”