Standardized drought indices: a novel univariate and multivariate approach

As drought is among the natural hazards which affect people and economies world wide and often results in huge monetary losses, sophisticated methods for drought monitoring and decision making are needed. Many approaches to quantify drought severity have been developed during recent decades. However, most of these drought indices suffer from different shortcomings, account only for one or two driving factors which promote drought conditions and neglect their interdependences. We provide novel methodology for the calculation of (multivariate) drought indices, which combines the advantages of existing approaches and omits their disadvantages. It can be used flexibly in different applications to model different types of drought on the basis of user‐selected, drought relevant variables. The methodology benefits from the flexibility of vine copulas in modelling multivariate non‐Gaussian intervariable dependence structures. Based on a three‐variate data set, an exemplary agrometeorological drought index is developed. The data analysis illustrates and reasons the methodology described. A validation of the exemplary multivariate agrometeorological drought index against observed soybean yield affirms the validity and abilities of the methodology. A comparison with established drought indices shows the benefits of our multivariate approach.