Composite Agrometeorological Drought Index Accounting for Seasonality and Autocorrelation

AbstractDrought indices are statistical tools used for monitoring the departure from normal conditions of water availability. Recently, the multivariate nature of droughts was addressed through composite indices capable of including different factors contributing to the occurrence of a drought. However, some issues (like the autocorrelation or the proper definition of the multivariate index) are still open and need to be addressed to make these indices applicable in current practice. Here, a composite agrometeorological drought index (AMDI-SA) has been introduced, accounting for meteorological and agricultural droughts, considering specifically seasonality and autocorrelation. The AMDI-SA combines, through the copula concept and the Kendall function, two drought indices [namely multivariate standardized precipitation index (MSPI) and the multivariate standardized soil moisture index (MSSI)] in a statistically consistent (normal distributed) drought indicator. Nonparametric distributions have been used for the variables of interest and the calculation of MSPI and MSSI, whereas parametric and nonparametric (empirical) copulas are used to build the AMDI-SA. A prewhitening procedure has been applied to the MSPI and MSSI to remove the autocorrelation. An application to the Urmia lake basin in Iran has been presented, drought indices compared, and their spatial variability investigated. Results showed that MSPI and MSSI are able to justify 72 and 89% of the variability throughout the year. The AMDI-SA reflects the combined effect of soil moisture and precipitation, and has a behavior in between whitened MSPI and MSSI. In addition, having no memory and being a composite index, the AMDI-SA is able to clearly detect the temporal variability of recorded droughts to a greater extent than the MSPI and MSSI.