This index is also as simple as SPI and calculated by subtracting the long term mean from an individual rainfall value and then dividing the difference by the standard deviation. The Z-Score does not require adjusting the data by fitting the data to the
Gamma or Pearson Type III distributions. Because of this, it is speculated that Z-Score might not represent the shorter time scales (Edwards and Mckee, 1997). Because of its simple calculation and effectiveness, Z-Score have been used in many drought studies (Akhtari et al., 2009; Komuscu, 1999; Morid et al., 2006; Tsakiris and Vangelis, 2004; Dogan et al., 2012). Various researchers also acclaimed that it is as
good as SPI and can be calculated on multiple time steps. It can also accommodate missing values in the data series like CZI.

The ZSI is occasionally confused with SPI. However, it is more analogous to CZI, but without the requirement for fitting precipitation data to either gamma distribution or Pearson type III distribution. ZSI can be calculated by the following equation: AgMerra Drought paper.

The Z-index is a measure of the monthly moisture anomaly and it reflects the departure of moisture conditions in a particular month from normal (or climatically appropriate) moisture conditions (Heim, 2002). The first step in calculating the monthly moisture status (Z-index) is to determine the expected evapotranspiration, runoff, soil moisture loss and recharge rates based on at least a 30-year time series. A water balance equation is subsequently applied to derive the expected, or normal precipitation.

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