How can I prepare my data for SPI calculation and how starting and ending years can be active?

If you want use MDM you can use Monthly Data For MDM or Daily Data For MDM excel file for monthly sample. However, you should create an excel file with 2 columns. One of them is Date and another is Rainfall (Precipitation). In the Date file you should write year for each row. If your data are in monthly you should create 12 rows for each year and in the Date column, please write year (YYYY) and in Rainfall column, please write rainfall data. For example if your period is 1980-2010, you should write in the Date column: 12 rows "1980" and 12 rows "1981" and etc.

Using the SPI as the indicator, a functional and quantitative definition of drought can be established for each time scale. A drought event for time scale i is defined here as a period in which the SPI is continuously negative and the SPI reaches a value of -1.0 or less. The drought begins when the SPI first falls below zero and ends with the positive value of SPI following a value of -1.0 or less. Guttman (1999) went into the specifics about different probability distributions applied to the long-term data sets and examined the impact of six distributions on the SPI. The recommendation from Guttman (1999) is that the Pearson Type III distribution is "best" suited to normalize the long-term data sets when calculating the SPI. Edwards and McKee (1997) used the two-parameter gamma distribution to calculate the SPI. Guttman (1999) also recommended that the procedure be uniform for everyone so that applications of the SPI would be consistent. Different software versions to determine the SPI are now available from Colorado State University and the National Climatic Data Center.

The SPI has the following favourable characteristics (Tsakiris et al. 2007): a) It is uniquely related to probability, b) only precipitation is needed to calculate the precipitation deficit for the current period and for the desirable time scale, and c) the SPI is normally distributed so it can be used to monitor wet as well as dry periods. Guttman (1999) argued that, if different probability distributions are used to describe an observed series of precipitation, then different SPI values may be obtained. Easy computation of SPI by a user still remains a primary goal, and a secondary goal of the standardization is to select one probability density function for all sites and time scales.