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.     «

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 nov...     »