  As drought is among the natural hazards which affects people and economies
worldwide and often results in huge monetary losses sophisticated methods for
drought monitoring and decision making are needed. Several different approaches
to quantify drought have been developed during past decades. However, most of
these drought indices suffer from different shortcomings and do not account for
the multiple driving factors which promote drought conditions and their
inter-dependencies. We provide a novel methodology for the calculation of
(multivariate) drought indices, which combines the advantages of existing
approaches and omits their disadvantages. Moreover, our approach benefits from
the flexibility of vine copulas in modeling multivariate non-Gaussian
inter-variable dependence structures. A three-variate data example is used in
order to investigate drought conditions in Europe and to illustrate and reason
the different modeling steps. The data analysis shows the appropriateness of
the described methodology. Comparison to well-established drought indices shows
the benefits of our multivariate approach. The validity of the new methodology
is verified by comparing the spatial extent of historic drought events based on
different drought indices. Further, we show that the assumption of non-Gaussian
dependence structures is well-grounded in this real-world application.
