How Can I Calculate EDI(Effective Drought Index)?

Byun and Wilhite (1999) developed the EDI. It is the only index that was specifically designed to calculate daily drought severity. The EDI is a good index for operational monitoring of both meteorological and agricultural drought situations because calculations are updated daily.For detailed explanations, please refer to Byun and Wilhite (1999). With a single input required for calculations, it is possible to calculate EDI for any location where precipitation is recorded. EDI is standardized so that outputs from all climate regimes can be compared. It is effective in identifying the beginning, ending and duration of drought events. One of the obvious weakness of EDI is: With precipitation alone accounted for, the impact of temperature on drought situations is not directly integrated. Using daily data may make it difficult to use EDI in an operational situation, as daily updates to input data may not be possible.

Byun and Wilhite (1999) used a new concept of effective precipitation (EP). They defined effective precipitation as a function of current month's rainfall and weighted rainfall over a defined preceding period computed using a time dependent reduction function. They computed EDI as a function of the amount of precipitation required to return to normal (PRN). Where, PRN is calculated from monthly effective precipitation and its deviation from the mean for each month.

The "drought range" of the EDI indicates extreme drought at EDI less than -2, severe drought at EDI between -2 and -1.5, and moderate drought at EDI between -1.5 and -1.0. Near normal conditions are indicated by EDI between -1 and 1.0. You can calculate by Rain besed Drought Indices Tool easily.

Pandey et al. (2008) used SPATSIM for a drought study of Orissa, India and found that EDI performed better than other DIs. Dogan et al. (2012) compared six meteorological drought indices and indicated that each drought index identified the drought characteristics differently. They observed the variation in severity values and duration of a drought event computed using different indices. Further, they concluded that EDI performed better for monthly rainfall changes in semi-arid Kenya closed basin, and Turkey. Also, Dogan et al. (2012) suggested that application of multiple time steps has significant role in assessment of regional drought characteristics. If one is interested in short term rainfall anomalies near real time drought monitoring, the shorter time step (i.e., 1-month, 3-month) should be used. Salehnia et al. (2017) showed that EDI has performed different manner in showing driest period, see the paper for further details.

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