COPULA-BASED DROUGHT ANALYSIS AND DROUGHT PREDICTIONS

Drought is a complex natural phenomenon that cannot be reduced to mere rainfall deficiency; it is a challenging event to manage due to its hydrological, agricultural, and socio-economic implications. The accurate analysis of these events, which are becoming increasingly frequent and severe due to climate change, is critical for the sustainable management of water resources. However, traditional single-variable methods (such as SPI) often fail to capture the complex dependency structures between climate parameters such as precipitation, temperature, and evaporation. Based on this requirement, this study focuses on “copula functions,” which stand out for their ability to model nonlinear relationships and tail dependencies between variables. This section provides a detailed, step-by-step analysis covering topics such as the mathematical foundations of copula theory (Sklar Theorem), marginal distribution selection, parameter estimation, and model fit testing. To validate the theoretical framework, a case study based on the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) is also presented. In conclusion, it is emphasized that copula-based multivariate models provide more reliable results than classical methods when determining common drought probabilities and recurrence intervals. These models serve as an effective decision support tool in risk management.